U.S. patent application number 16/554561 was filed with the patent office on 2019-12-19 for self-aware system for adaptive navigation.
This patent application is currently assigned to Mobileye Vision Technologies Ltd.. The applicant listed for this patent is MOBILEYE VISION TECHNOLOGIES LTD.. Invention is credited to Daniel BRAUNSTEIN, Yoram GDALYAHU, Aran REISMAN, Amnon Shashua, Ofer SPRINGER.
Application Number | 20190384296 16/554561 |
Document ID | / |
Family ID | 55485323 |
Filed Date | 2019-12-19 |
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United States Patent
Application |
20190384296 |
Kind Code |
A1 |
Shashua; Amnon ; et
al. |
December 19, 2019 |
SELF-AWARE SYSTEM FOR ADAPTIVE NAVIGATION
Abstract
Systems and methods are disclosed for providing maps to an
autonomous vehicle. Methods include maintaining a road model that
includes trajectories associated with a road segment, the
trajectories used to assist the autonomous vehicle to navigate on a
target trajectory consistent with the road model; determining,
based on analysis of image data, an existence of a non-transient
condition that is inconsistent with the road model, the image data
from a camera integrated with the autonomous vehicle, wherein the
autonomous vehicle is configured to deviate from the target
trajectory based on the existence of the non-transient condition;
and storing information about the non-transient condition for
updating the road model.
Inventors: |
Shashua; Amnon; (Jerusalem,
IL) ; GDALYAHU; Yoram; (Jerusalem, IL) ;
SPRINGER; Ofer; (Jerusalem, IL) ; REISMAN; Aran;
(Givatayim, IL) ; BRAUNSTEIN; Daniel; (Jerusalem,
IL) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
MOBILEYE VISION TECHNOLOGIES LTD. |
Jerusalem |
|
IL |
|
|
Assignee: |
Mobileye Vision Technologies
Ltd.
|
Family ID: |
55485323 |
Appl. No.: |
16/554561 |
Filed: |
August 28, 2019 |
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Application
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Patent Number |
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15673323 |
Aug 9, 2017 |
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16554561 |
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PCT/US2016/017411 |
Feb 10, 2016 |
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15673323 |
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62114091 |
Feb 10, 2015 |
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62164055 |
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62271103 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06F 16/2379 20190101;
G08G 1/167 20130101; G05D 1/0088 20130101; G05D 1/0278 20130101;
G06K 9/00818 20130101; B60W 2710/20 20130101; G06K 9/00825
20130101; G08G 1/096725 20130101; G01C 21/3602 20130101; G01C 21/36
20130101; G05D 1/0246 20130101; H04L 67/12 20130101; G08G 1/096805
20130101; G06K 9/00798 20130101; G06T 7/00 20130101; G06T
2207/30256 20130101; B60W 2710/18 20130101; G05D 1/0221 20130101;
G05D 1/0251 20130101; G01C 21/3623 20130101; G01S 19/10 20130101;
B60W 30/14 20130101; B60W 30/18 20130101; G01C 21/34 20130101; G08G
1/0112 20130101; G05D 1/0287 20130101; B60W 2420/42 20130101; G01C
21/165 20130101; G05D 2201/0213 20130101; G06T 2207/30261 20130101;
G01C 21/14 20130101; B60W 2555/60 20200201; B60W 2720/10 20130101;
G01C 21/3644 20130101; G06T 2207/20081 20130101; G05D 1/0253
20130101; G01C 21/32 20130101; G01C 21/3407 20130101; G06K 9/00791
20130101; B62D 15/025 20130101; G05D 1/0219 20130101; G08G 1/09623
20130101; G05D 1/0212 20130101; G06F 16/29 20190101; G01C 21/3476
20130101; G06K 9/3258 20130101; G01C 21/3691 20130101 |
International
Class: |
G05D 1/00 20060101
G05D001/00; G06K 9/00 20060101 G06K009/00; G05D 1/02 20060101
G05D001/02; G01C 21/36 20060101 G01C021/36; G01C 21/34 20060101
G01C021/34; G08G 1/0968 20060101 G08G001/0968; B60W 30/14 20060101
B60W030/14; G08G 1/01 20060101 G08G001/01; G01C 21/16 20060101
G01C021/16; G08G 1/16 20060101 G08G001/16; G08G 1/0962 20060101
G08G001/0962; B62D 15/02 20060101 B62D015/02; G08G 1/0967 20060101
G08G001/0967; G01C 21/14 20060101 G01C021/14; B60W 30/18 20060101
B60W030/18; G06K 9/32 20060101 G06K009/32; G01C 21/32 20060101
G01C021/32; G06F 16/23 20060101 G06F016/23; G06F 16/29 20060101
G06F016/29 |
Claims
1-28. (canceled)
29. A navigation system for providing maps to an autonomous
vehicle, the navigation system comprising: at least one processor;
and a memory device including instructions, which when executed by
the processor, cause the processor to perform functions comprising:
maintain a road model that includes trajectories associated with a
road segment, the trajectories used to assist the autonomous
vehicle to navigate on a target trajectory consistent with the road
model; determine, based on analysis of image data, an existence of
a non-transient condition that is inconsistent with the road model,
the image data from a camera integrated with the autonomous
vehicle, wherein the autonomous vehicle is configured to deviate
from the target trajectory based on the existence of the
non-transient condition; and store information about the
non-transient condition for updating the road model.
30. The navigation system of claim 29, wherein the non-transient
condition includes an area of road construction.
31. The navigation system of claim 29, wherein the instructions
cause the processor to perform the functions comprising determining
whether to update the road model to produce an updated model.
32. The navigation system of claim 31, wherein the instructions
cause the processor to perform the functions comprising
distributing the road model to a plurality of vehicles.
33. The navigation system of claim 29, wherein the instructions
cause the processor to distribute and updated road model to a
plurality of vehicles for use in autonomous operation.
34. The navigation system of claim 29, wherein the instructions
cause the processor to identify a landmark to implement a neural
network to identify the landmark.
35. The navigation system of claim 29, wherein the instructions
cause the processor to identify a category and type of traffic
sign.
36. A method for providing maps to an autonomous vehicle, the
method comprising: maintaining a road model that includes
trajectories associated with a road segment, the trajectories used
to assist the autonomous vehicle to navigate on a target trajectory
consistent with the road model; determining, based on analysis of
image data, an existence of a non-transient condition that is
inconsistent with the road model, the image data from a camera
integrated with the autonomous vehicle, wherein the autonomous
vehicle is configured to deviate from the target trajectory based
on the existence of the non-transient condition; and storing
information about the non-transient condition for updating the road
model.
37. The method of claim 36, wherein the non-transient condition
includes an area of road construction.
38. The method of claim 36, further comprising determining whether
to update the road model to produce an updated model.
39. The method of claim 38, further comprising distributing the
road model to a plurality of vehicle.
40. The method of claim 36, wherein the instructions cause the
processor to distribute and updated road model to a plurality of
vehicles for use in autonomous operation.
41. The method of claim 36, wherein the instructions cause the
processor to identify a landmark to implement a neural network to
identify the landmark.
42. The method of claim 36, wherein the instructions cause the
processor to identify a category and type of traffic sign.
43. At least one non-transitory machine-readable medium including
data, which when used by a machine that is installable in a
vehicle, causes the machine to perform instructions that cause the
machine to perform operations comprising: maintaining a road model
that includes trajectories associated with a road segment, the
trajectories used to assist the autonomous vehicle to navigate on a
target trajectory consistent with the road model; determining,
based on analysis of image data, an existence of a non-transient
condition that is inconsistent with the road model, the image data
from a camera integrated with the autonomous vehicle, wherein the
autonomous vehicle is configured to deviate from the target
trajectory based on the existence of the non-transient condition;
and storing information about the non-transient condition for
updating the road model.
44. The at least one non-transitory machine-readable medium of
claim 43, wherein the non-transient condition includes an area of
road construction.
45. The at least one non-transitory machine-readable medium of
claim 43, wherein the operations perform functions comprising
determining whether to update the road model to produce an updated
model.
46. The at least one non-transitory machine-readable medium of
claim 45, wherein the operations perform functions comprising
distributing the road model to a plurality of vehicles.
47. The at least one non-transitory machine-readable medium of
claim 43, wherein the instructions cause the processor to
distribute and updated road model to a plurality of vehicles for
use in autonomous operation.
48. The at least one non-transitory machine-readable medium of
claim 43, wherein the instructions cause the processor to identify
a landmark to implement a neural network to identify the landmark.
Description
CROSS REFERENCES TO RELATED APPLICATIONS
[0001] This application claims the benefit of priority of U.S.
Provisional Patent Application No. 62/114,091, filed on Feb. 10,
2015; U.S. Provisional Patent Application No. 62/164,055, filed on
May 20, 2015; U.S. Provisional Patent Application No. 62/170,728,
filed on Jun. 4, 2015; U.S. Provisional Patent Application No.
62/181,784, filed on Jun. 19, 2015; U.S. Provisional Patent
Application No. 62/192,576, filed on Jul. 15, 2015; U.S.
Provisional Patent Application No. 62/215,764, filed on Sep. 9,
2015; U.S. Provisional Patent Application No. 62/219,733, filed on
Sep. 17, 2015; U.S. Provisional Patent Application No. 62/261,578,
filed on Dec. 1, 2015; U.S. Provisional Patent Application No.
62/261,598, filed on Dec. 1, 2015; U.S. Provisional Patent
Application No. 62/267,643, filed on Dec. 15, 2015; U.S.
Provisional Patent Application No. 62/269,818, filed on Dec. 18,
2015; U.S. Provisional Patent Application No. 62/270,408, filed on
Dec. 21, 2015; U.S. Provisional Patent Application No. 62/270,418,
filed on Dec. 21, 2015; U.S. Provisional Patent Application No.
62/270,431, filed on Dec. 21, 2015; U.S. Provisional Patent
Application No. 62/271,103, filed on Dec. 22, 2015; U.S.
Provisional Patent Application No. 62/274,883, filed on Jan. 5,
2016; U.S. Provisional Patent Application No. 62/274,968, filed on
Jan. 5, 2016; U.S. Provisional Patent Application No. 62/275,007,
filed on Jan. 5, 2016; U.S. Provisional Patent Application No.
62/275,046, filed on Jan. 5, 2016; and U.S. Provisional Patent
Application No. 62/277,068, filed on Jan. 11, 2016. All of the
foregoing applications are incorporated herein by reference in
their entirety.
BACKGROUND
Technical Field
[0002] The present disclosure relates generally to autonomous
vehicle navigation and a sparse map for autonomous vehicle
navigation. Additionally, this disclosure relates to systems and
methods for constructing, using, and updating the sparse map for
autonomous vehicle navigation.
Background Information
[0003] As technology continues to advance, the goal of a fully
autonomous vehicle that is capable of navigating on roadways is on
the horizon. Autonomous vehicles may need to take into account a
variety of factors and make appropriate decisions based on those
factors to safely and accurately reach an intended destination. For
example, an autonomous vehicle may need to process and interpret
visual information (e.g., information captured from a camera) and
may also use information obtained from other sources (e.g., from a
GPS device, a speed sensor, an accelerometer, a suspension sensor,
etc.). At the same time, in order to navigate to a destination, an
autonomous vehicle may also need to identify its location within a
particular roadway (e.g., a specific lane within a multi-lane
road), navigate alongside other vehicles, avoid obstacles and
pedestrians, observe traffic signals and signs, and travel from on
road to another road at appropriate intersections or interchanges.
Harnessing and interpreting vast volumes of information collected
by an autonomous vehicle as it travels to its destination poses a
multitude of design challenges. The sheer quantity of data (e.g.,
captured image data, map data, GPS data, sensor data, etc.) that an
autonomous vehicle may need to analyze, access, and/or store poses
challenges that can in fact limit or even adversely affect
autonomous navigation. Furthermore, if an autonomous vehicle relies
on traditional mapping technology to navigate, the sheer volume of
data needed to store and update the map poses daunting
challenges.
SUMMARY
[0004] Embodiments consistent with the present disclosure provide
systems and methods for autonomous vehicle navigation. The
disclosed embodiments may use cameras to provide autonomous vehicle
navigation features. For example, consistent with the disclosed
embodiments, the disclosed systems may include one, two, or more
cameras that monitor the environment of a vehicle. The disclosed
systems may provide a navigational response based on, for example,
an analysis of images captured by one or more of the cameras. The
navigational response may also take into account other data
including, for example, global positioning system (GPS) data,
sensor data (e.g., from an accelerometer, a speed sensor, a
suspension sensor, etc.), and/or other map data.
[0005] In some embodiments, the disclosed systems and methods may
use a sparse map for autonomous vehicle navigation. For example,
the sparse map may provide sufficient information for navigation
without requiring excessive data storage.
[0006] In other embodiments, the disclosed systems and methods may
construct a road model for autonomous vehicle navigation. For
example, the disclosed systems and methods may use crowd sourced
data for autonomous vehicle navigation including recommended
trajectories. As other examples, the disclosed systems and methods
may identify landmarks in an environment of a vehicle and refine
landmark positions.
[0007] In yet other embodiments, the disclosed systems and methods
may use a sparse road model for autonomous vehicle navigation. For
example, the disclosed systems and methods may provide navigation
based on recognized landmarks, align a vehicle's tail for
navigation, allow a vehicle to navigate road junctions, allow a
vehicle to navigate using local overlapping maps, allow a vehicle
to navigate using a sparse map, navigate based on an expected
landmark location, autonomously navigate a road based on road
signatures, provide forward navigation based on a rearward facing
camera, navigate based on a free space determination, navigate in
snow, provide autonomous vehicle speed calibration, determine lane
assignment based on a recognized landmark location, and use super
landmarks as navigation aids.
[0008] In still yet other embodiments, the disclosed systems and
methods may provide adaptive autonomous navigation. For example,
disclosed systems and methods may provide adaptive navigation based
on user intervention, provide self-aware adaptive navigation,
provide an adaptive road model manager, and manage a road model
based on selective feedback.
[0009] In some embodiments, a non-transitory computer-readable
medium may include a sparse map for autonomous vehicle navigation
along a road segment. The sparse map may include a polynomial
representation of a target trajectory for the autonomous vehicle
along the road segment; and a plurality of predetermined landmarks
associated with the road segment, wherein the plurality of
predetermined landmarks may be spaced apart by at least 50 meters,
and wherein the sparse map may have a data density of no more than
1 megabyte per kilometer.
[0010] In some embodiments of the non-transitory computer-readable
medium, the polynomial representation may be a three-dimensional
polynomial representation. The polynomial representation of the
target trajectory may be determined based on two or more
reconstructed trajectories of prior traversals of vehicles along
the road segment. The plurality of predetermined landmarks may
include a traffic sign represented in the sparse map by no more
than 50 bytes of data. The plurality of predetermined landmarks may
include a directional sign represented in the sparse map by no more
than 50 bytes of data. The plurality of predetermined landmarks may
include a general purpose sign represented in the sparse map by no
more than 100 bytes of data. The plurality of predetermined
landmarks may include a generally rectangular object represented in
the sparse map by no more than 100 bytes of data. The
representation of the generally rectangular object in the sparse
map may include a condensed image signature associated with the
generally rectangular object. The plurality of predetermined
landmarks may be represented in the sparse map by parameters
including landmark size, distance to previous landmark, landmark
type, and landmark position. The plurality of predetermined
landmarks included in the sparse map may be spaced apart by at
least 2 kilometers. The plurality of predetermined landmarks
included in the sparse map may be spaced apart by at least 1
kilometer. The plurality of predetermined landmarks included in the
sparse map may be spaced apart by at least 100 meters. The sparse
map may have a data density of no more than 100 kilobytes per
kilometer. The sparse map may have a data density of no more than
10 kilobytes per kilometer. The plurality of predetermined
landmarks may appear in the sparse map at a rate that is above a
rate sufficient to maintain a longitudinal position determination
accuracy within 1 meter.
[0011] In some embodiments, an autonomous vehicle may include a
body; and a non-transitory computer-readable medium that may
include a sparse map for autonomous vehicle navigation along a road
segment. The sparse map may include a polynomial representation of
a target trajectory for the autonomous vehicle along the road
segment; and a plurality of predetermined landmarks associated with
the road segment, wherein the plurality of predetermined landmarks
are spaced apart by at least 50 meters, and wherein the sparse map
has a data density of no more than 1 megabyte per kilometer. The
autonomous vehicle may include a processor configured to execute
data included in the sparse map for providing autonomous vehicle
navigation along the road segment.
[0012] In some embodiments of the autonomous vehicle, the
polynomial representation may be a three-dimensional polynomial
representation. The polynomial representation of the target
trajectory may be determined based on two or more reconstructed
trajectories of prior traversals of vehicles along the road
segment.
[0013] In some embodiments, an autonomous vehicle may include a
body; and a processor configured to receive data included in a
sparse map and execute the data for autonomous vehicle navigation
along a road segment. The sparse map may include a polynomial
representation of a target trajectory for the autonomous vehicle
along the road segment; and a plurality of predetermined landmarks
associated with the road segment, wherein the plurality of
predetermined landmarks are spaced apart by at least 50 meters, and
wherein the sparse map has a data density of no more than 1
megabyte per kilometer.
[0014] In some embodiments, a method of processing vehicle
navigation information for use in autonomous vehicle navigation may
include receiving, by a server, navigation information from a
plurality of vehicles. The navigation information from the
plurality of vehicles may be associated with a common road segment.
The method may include storing, by the server, the navigation
information associated with the common road segment. The method may
include generating, by the server, at least a portion of an
autonomous vehicle road navigation model for the common road
segment based on the navigation information from the plurality of
vehicles; and distributing, by the server, the autonomous vehicle
road navigation model to one or more autonomous vehicles for use in
autonomously navigating the one or more autonomous vehicles along
the common road segment.
[0015] In some embodiments of the method, the navigation
information may include a trajectory from each of the plurality of
vehicles as each vehicle travels over the common road segment. The
trajectory may be determined based on sensed motion of a camera,
including three-dimensional translation and three-dimensional
rotational motions. The navigation information may include a lane
assignment. Generating at least a portion of the autonomous vehicle
road navigation model may include clustering vehicle trajectories
along the common road segment and determining a target trajectory
along the common road segment based on the clustered vehicle
trajectories. The autonomous vehicle road navigation model may
include a three-dimensional spline corresponding to the target
trajectory along the common road segment. The target trajectory may
be associated with a single lane of the common road segment. The
autonomous vehicle road navigation model may include a plurality of
target trajectories, each associated with a separate lane of the
common road segment. Determining the target trajectory along the
common road segment based on the clustered vehicle trajectories may
include finding a mean or average trajectory based on the clustered
vehicle trajectories. The target trajectory may be represented by a
three-dimensional spline. The spline may be defined by less than 10
kilobytes per kilometer. The autonomous vehicle road navigation
model may include identification of at least one landmark,
including a position of the at least one landmark. The position of
the at least one landmark may be determined based on position
measurements performed using sensor systems associated with the
plurality of vehicles. The position measurements may be averaged to
obtain the position of the at least one landmark. The at least one
landmark may include at least one of a traffic sign, an arrow
marking, a lane marking, a dashed lane marking, a traffic light, a
stop line, a directional sign, a landmark beacon, or a
lamppost.
[0016] In some embodiments, a navigation system for a vehicle may
include at least one processor programmed to receive from a camera,
at least one environmental image associated with the vehicle;
analyze the at least one environmental image to determine
navigation information related to the vehicle; transmit the
navigation information from the vehicle to a server. The at least
one processor may be programmed to receive, from the server, an
autonomous vehicle road navigation model. The autonomous vehicle
road navigation model may include at least one update based on the
transmitted navigation information. The at least one processor may
be programmed to cause at least one navigational maneuver by the
vehicle based on the autonomous vehicle road navigation model.
[0017] In some embodiments of the navigation system, the navigation
information may include a trajectory from each of the plurality of
vehicles as each vehicle travels over the common road segment.
[0018] In some embodiments, a server for processing vehicle
navigation information for use in autonomous vehicle navigation may
include a communication unit configured to communicate with a
plurality of vehicles; and at least one processor programmed to
receive, via the communication unit, the navigation information
from the vehicles. The at least one processor may be programmed to
generate at least a portion of an autonomous vehicle road
navigation model based on the navigation information; and transmit
at least the portion of the autonomous vehicle road navigation
model to at least one of the vehicles to cause a navigational
maneuver by the at least one of the vehicles based on the portion
of the autonomous vehicle road navigation model.
[0019] In some embodiments of the server, the navigation
information may include a trajectory from each of the plurality of
vehicles as each vehicle travels over the common road segment. The
portion of autonomous vehicle road navigation model may include an
update to the autonomous vehicle road navigation model.
[0020] In some embodiments, a navigation system for a vehicle may
include at least one processor programmed to receive, from one or
more sensors, outputs indicative of a motion of the vehicle;
determine an actual trajectory of the vehicle based on the outputs
from the one or more sensors; receive, from a camera, at least one
environmental image associated with the vehicle; analyze the at
least one environmental image to determine information associated
with at least one navigational constraint; determine a target
trajectory, including the actual trajectory of the vehicle and one
or more modifications to the actual trajectory based on the
determined information associated with the at least one
navigational constraint; and transmit the target trajectory from
the vehicle to a server.
[0021] In some embodiments of the system, the one or more sensors
may include a speed sensor. The one or more sensors may include an
accelerometer. The one or more sensors may include the camera. The
at least one navigational constraint may include at least one of a
barrier, an object, a lane marking, a sign, or another vehicle. The
camera may be included in the vehicle.
[0022] In some embodiments, a method of uploading a target
trajectory to a server may include receiving, from one or more
sensors, outputs indicative of a motion of a vehicle; determining
an actual trajectory of the vehicle based on the outputs from the
one or more sensors; receiving, from a camera, at least one
environmental image associated with the vehicle; analyzing the at
least one environmental image to determine information associated
with at least one navigational constraint; determining a target
trajectory, including the actual trajectory of the vehicle and one
or more modifications to the actual trajectory based on the
determined information associated with the at least one
navigational constraint; and transmitting the target trajectory
from the vehicle to a server.
[0023] In some embodiments of the method, the one or more sensors
may include a speed sensor. The one or more sensors may include an
accelerometer. The one or more sensors may include the camera. The
at least one navigational constraint may include at least one of a
barrier, an object, a lane marking, a sign, or another vehicle. The
camera may be included in the vehicle.
[0024] In some embodiments, a system for identifying a landmark for
use in autonomous vehicle navigation may include at least one
processor programmed to: receive at least one identifier associated
with the landmark; associate the landmark with a corresponding road
segment; update an autonomous vehicle road navigation model
relative to the corresponding road segment to include the at least
one identifier associated with the landmark; and distribute the
updated autonomous vehicle road navigation model to a plurality of
autonomous vehicles. The at least one identifier may be determined
based on acquisition, from a camera associated with a host vehicle,
of at least one image representative of an environment of the host
vehicle; analysis of the at least one image to identify the
landmark in the environment of the host vehicle; and analysis of
the at least one image to determine the at least one identifier
associated with the landmark.
[0025] In some embodiments of the system, the at least one
identifier may include a position of the landmark. The at least one
identifier may include a shape of the landmark. The at least one
identifier may include a size of the landmark. The at least one
identifier may include a distance of the landmark relative to
another landmark. The at least one identifier may be determined
based on the landmark being identified as one of a plurality of
landmark types. The landmark types may include a traffic sign. The
landmark types may include a post. The landmark types may include a
directional indicator. The landmark types may include a rectangular
sign. The at least one identifier further may include a condensed
signature representation. The condensed signature representation of
the landmark may be determined based on mapping an image of the
landmark to a sequence of numbers of a predetermined data size. The
condensed signature representation may indicate an appearance of
the landmark. The condensed signature representation may indicate
at least one of a color pattern of an image of the landmark or a
brightness pattern of the image. The landmark may include at least
one of a directional sign, a traffic sign, a lamppost, a road
marking, and a business sign.
[0026] In some embodiments, a method of identifying a landmark for
use in autonomous vehicle navigation may include receiving at least
one identifier associated with the landmark; associating the
landmark with a corresponding road segment; updating an autonomous
vehicle road navigation model relative to the corresponding road
segment to include the at least one identifier associated with the
landmark; and distributing the updated autonomous vehicle road
navigation model to a plurality of autonomous vehicles.
[0027] In some embodiments, the method may include determining the
at least one identifier. Determining the at least one identifier
may include acquiring, from a camera associated with a host
vehicle, at least one image representative of an environment of the
host vehicle; analyzing the at least one image to identify the
landmark in the environment of the host vehicle; and analyzing the
at least one image to determine the at least one identifier
associated with the landmark. The at least one identifier may
include a distance of the landmark relative to another landmark,
and wherein determining the at least one identifier includes
determining a distance of the landmark relative to another
landmark. The at least one identifier may further include a
condensed signature representation, and wherein determining the at
least one identifier includes determining the condensed signature
representation from the at least one image.
[0028] In some embodiments, a system for determining a location of
a landmark for use in navigation of an autonomous vehicle may
include at least one processor programmed to: receive a measured
position of the landmark; and determine a refined position of the
landmark based on the measured position of the landmark and at
least one previously acquired position for the landmark. The
measured position and the at least one previously acquired position
may be determined based on acquisition, from a camera associated
with a host vehicle, of at least one environmental image associated
with the host vehicle, analysis of the at least one environmental
image to identify the landmark in the environment of the host
vehicle, reception of global positioning system (GPS) data
representing a location of the host vehicle, analysis of the at
least one environmental image to determine a relative position of
the identified landmark with respect to the host vehicle, and
determination of a globally localized position of the landmark
based on at least the GPS data and the determined relative
position.
[0029] In some embodiments of the system, the landmark may include
at least one of a traffic sign, an arrow, a lane marking, a dashed
lane marking, a traffic light, a stop line, a directional sign, a
landmark beacon, or a lamppost. Analysis of the at least one image
to determine the relative position of the identified landmark with
respect to the vehicle may include calculating a distance based on
a scale associated with the at least one image. Analyzing the at
least one image to determine the relative position of the
identified landmark with respect to the vehicle may include
calculating a distance based on an optical flow associated with the
at least one image. The GPS data may be received from a GPS device
included in the host vehicle. The camera may be included in the
host vehicle. Determining the refined position of the landmark may
include averaging the measured position of the landmark with the at
least one previously acquired position.
[0030] In some embodiments, a method for determining a location of
a landmark for use in navigation of an autonomous vehicle may
include receiving a measured position of the landmark; and
determining a refined position of the landmark based on the
measured position of the landmark and at least one previously
acquired position for the landmark. The measured position and the
at least one previously acquired position may be determined based
on acquisition, from a camera associated with a host vehicle, of at
least one environmental image associated with the host vehicle,
analysis of the at least one environmental image to identify the
landmark in the environment of the host vehicle, reception of
global positioning system (GPS) data representing a location of the
host vehicle, analysis of the at least one environmental image to
determine a relative position of the identified landmark with
respect to the host vehicle, and determination of a globally
localized position of the landmark based on at least the GPS data
and the determined relative position.
[0031] In some embodiments of the method, the landmark may include
at least one of a traffic sign, an arrow, a lane marking, a dashed
lane marking, a traffic light, a stop line, a directional sign, a
landmark beacon, or a lamppost. Analysis of the at least one image
to determine the relative position of the identified landmark with
respect to the vehicle may include calculating a distance based on
a scale associated with the at least one image. Analysis of the at
least one image to determine the relative position of the
identified landmark with respect to the vehicle may include
calculating a distance based on an optical flow associated with the
at least one image. The GPS data may be received from a GPS device
included in the host vehicle. The camera may be included in the
host vehicle. Determining the refined position of the landmark may
include averaging the measured position of the landmark with the at
least one previously acquired position.
[0032] In some embodiments, an autonomous vehicle may include a
body and at least one processor programmed to receive a measured
position of the landmark; and determine a refined position of the
landmark based on the measured position of the landmark and at
least one previously acquired position for the landmark. The at
least one processor may be further programmed to determine the
measured position and the at least one previously acquired position
based on acquisition, from a camera associated with the vehicle, of
at least one environmental image associated with the vehicle,
analysis of the at least one environmental image to identify the
landmark in the environment of the vehicle, reception of global
positioning system (GPS) data representing a location of the
vehicle, analysis of the at least one environmental image to
determine a relative position of the identified landmark with
respect to the vehicle, and determination of a globally localized
position of the landmark based on at least the GPS data and the
determined relative position.
[0033] In some embodiments of the vehicle, the landmark may include
at least one of a traffic sign, an arrow, a lane marking, a dashed
lane marking, a traffic light, a stop line, a directional sign, a
landmark beacon, or a lamppost. Analysis of the at least one image
to determine the relative position of the identified landmark with
respect to the vehicle may include calculating a distance based on
a scale associated with the at least one image. Analyzing the at
least one image to determine the relative position of the
identified landmark with respect to the vehicle may include
calculating a distance based on an optical flow associated with the
at least one image. The GPS data may be received from a GPS device
included in the host vehicle. Determining the refined position of
the landmark may include averaging the measured position of the
landmark with the at least one previously acquired position
[0034] In some embodiments, a system for autonomously navigating a
vehicle along a road segment may include at least one processor
programmed to: receive from an image capture device at least one
image representative of an environment of the vehicle; analyze the
at least one image to identify at least one recognized landmark;
determine a current location of the vehicle relative to a
predetermined road model trajectory associated with the road
segment based, at least in part, on a predetermined location of the
recognized landmark; and determine an autonomous steering action
for the vehicle based on a direction of the predetermined road
model trajectory at the determined current location of the vehicle
relative to the predetermined road model trajectory.
[0035] In some embodiments of the system, the recognized landmark
may include at least one of a traffic sign, an arrow marking, a
lane marking, a dashed lane marking, a traffic light, a stop line,
a directional sign, a reflector, a landmark beacon, or a lamppost.
The recognized landmark may include a change in spacing of lines on
the road segment. The recognized landmark may include a sign for a
business. The predetermined road model trajectory may include a
three-dimensional polynomial representation of a target trajectory
along the road segment. Navigation between recognized landmarks may
include integration of vehicle velocity to determine a location of
the vehicle along the predetermined road model trajectory. The
processor may be further programmed to adjust a steering system of
the vehicle based on the autonomous steering action to navigate the
vehicle. The processor may be further programmed to: determine a
distance of the vehicle from the at least one recognized landmark;
and determine whether the vehicle is positioned on the
predetermined road model trajectory associated with the road
segment based on the distance. The processor may be further
programmed to adjust the steering system of the vehicle to move the
vehicle from a current position of the vehicle to a position on the
predetermined road model trajectory when the vehicle is not
positioned on the predetermined road model trajectory.
[0036] In some embodiments, a vehicle may include a body; at least
one image capture device configured to acquire at least one image
representative of an environment of the vehicle; and at least one
processor programmed to: receive from the at least one image
capture device the at least one image; analyze the at least one
image to identify at least one recognized landmark; determine a
current location of the vehicle relative to a predetermined road
model trajectory associated with the road segment based, at least
in part, on a predetermined location of the recognized landmark;
and determine an autonomous steering action for the vehicle based
on a direction of the predetermined road model trajectory at the
determined current location of the vehicle relative to the
predetermined road model trajectory.
[0037] In some embodiments of the vehicle, the recognized landmark
may include at least one of a traffic sign, an arrow marking, a
lane marking, a dashed lane marking, a traffic light, a stop line,
a directional sign, a reflector, a landmark beacon, a lamppost, a
change is spacing of lines on the road, or a sign for a business.
The predetermined road model trajectory may include a
three-dimensional polynomial representation of a target trajectory
along the road segment. Navigation between recognized landmarks may
include integration of vehicle velocity to determine a location of
the vehicle along the predetermined road model trajectory. The
processor may be further programmed to adjust the steering system
of the vehicle based on the autonomous steering action to navigate
the vehicle. The processor may be further programmed to: determine
a distance of the vehicle from the at least one recognized
landmark; and determine whether the vehicle is positioned on the
predetermined road model trajectory associated with the road
segment based on the distance. The processor may be further
programmed to adjust the steering system of the vehicle to move the
vehicle from a current position of the vehicle to a position on the
predetermined road model trajectory when the vehicle is not
positioned on the predetermined road model trajectory.
[0038] In some embodiments, a method of navigating a vehicle may
include receiving, from an image capture device associated with the
vehicle, at least one image representative of an environment of the
vehicle; analyzing, using a processor associated with the vehicle,
the at least one image to identify at least one recognized
landmark; determining a current position of the vehicle relative to
a predetermined road model trajectory associated with the road
segment based, at least in part, on a predetermined location of the
recognized landmark; determining an autonomous steering action for
the vehicle based on a direction of the predetermined road model
trajectory at the determined current location of the vehicle
relative to the predetermined road model trajectory; and adjusting
a steering system of the vehicle based on the autonomous steering
action to navigate the vehicle.
[0039] In some embodiments, the method may include determining a
location of the vehicle along the predetermined road model
trajectory by integrating the vehicle velocity. The method may
include determining, using the processor, a distance of the vehicle
from the at least one recognized landmark; and determining whether
the vehicle is positioned on the predetermined road model
trajectory associated with the road segment based on the distance.
The method may include determining a transformation required to
move the vehicle from a current position of the vehicle to a
position on the predetermined road model trajectory; and adjusting
the steering system of the vehicle based on the transformation.
[0040] In some embodiments, a system for autonomously navigating an
autonomous vehicle along a road segment may include at least one
processor programmed to receive from an image capture device, a
plurality of images representative of an environment of the
autonomous vehicle; determine a traveled trajectory of the
autonomous vehicle along the road segment based, at least in part,
on analysis of one or more of the plurality of images; determine a
current location of the autonomous vehicle along a predetermined
road model trajectory based on analysis of one or more of the
plurality of images; determine a heading direction for the
autonomous vehicle based on the determined traveled trajectory; and
determine a steering direction for the autonomous vehicle, relative
to the heading direction, by comparing the traveled trajectory to
the predetermined road model trajectory at the current location of
the autonomous vehicle.
[0041] In some embodiments of the system, the comparison between
the traveled trajectory and the predetermined road model trajectory
may include determination of a transformation that reduces an error
between the traveled trajectory and the predetermined road model
trajectory. The processor may be further programmed to adjust the
steering system of the autonomous vehicle based on the
transformation. The predetermined road model trajectory may include
a three-dimensional polynomial representation of a target
trajectory along the road segment. The predetermined road model
trajectory may be retrieved from a database stored in a memory
included in the autonomous vehicle. The predetermined road model
trajectory may be retrieved from a database accessible to the
autonomous vehicle over a wireless communications interface. The
image capture device may be included in the autonomous vehicle.
Determination of the steering direction may be further based on one
or more additional cues, including one or more of a left lane mark
polynomial model, a right lane mark polynomial model, holistic path
prediction, motion of a forward vehicle, determined free space
ahead of the autonomous vehicle, and virtual lanes or virtual lane
constraints determined based on positions of vehicles forward of
the autonomous vehicle. Determination of the steering direction may
be based on weights applied to the one or more additional cues.
[0042] In some embodiments, an autonomous vehicle may include a
body; at least one image capture device configured to acquire at
least one image representative of an environment of the autonomous
vehicle; and at least one processor programmed to: receive from the
image capture device, a plurality of images representative of the
environment of the autonomous vehicle; determine a traveled
trajectory of the autonomous vehicle along the road segment based,
at least in part, on analysis of one or more of the plurality of
images; determine a current location of the autonomous vehicle
along a predetermined road model trajectory based on analysis of
one or more of the plurality of images; determine a heading
direction for the autonomous vehicle based on the determined
traveled trajectory; and determine a steering direction for the
autonomous vehicle, relative to the heading direction, by comparing
the traveled trajectory to the predetermined road model trajectory
at the current location of the autonomous vehicle.
[0043] In some embodiments of the autonomous vehicle, the
comparison between the traveled trajectory and the predetermined
road model trajectory may include determination of a transformation
that reduces an error between the traveled trajectory and the
predetermined road model trajectory. The predetermined road model
trajectory may include a three-dimensional polynomial
representation of a target trajectory along the road segment. The
predetermined road model trajectory may be retrieved from one of a
database stored in a memory included in the autonomous vehicle and
a database accessible to the autonomous vehicle over a wireless
communications interface. Determination of the steering direction
may be further based on one or more additional cues, including one
or more of a left lane mark polynomial model, a right lane mark
polynomial model, holistic path prediction, motion of a forward
vehicle, determined free space ahead of the autonomous vehicle, and
virtual lanes or virtual lane constraints determined based on
positions of vehicles forward of the autonomous vehicle.
Determination of the steering direction may be based on weights
applied to the one or more additional cues
[0044] In some embodiments, a method of navigating an autonomous
vehicle may include receiving, from an image capture device, a
plurality of images representative of an environment of the
autonomous vehicle; determining a traveled trajectory of the
autonomous vehicle along the road segment based, at least in part,
on analysis of one or more of the plurality of images; determining
a current location of the autonomous vehicle along a predetermined
road model trajectory based on analysis of one or more of the
plurality of images; determining a heading direction for the
autonomous vehicle based on the determined traveled trajectory; and
determining a steering direction for the autonomous vehicle,
relative to the heading direction, by comparing the traveled
trajectory to the predetermined road model trajectory at the
current location of the autonomous vehicle.
[0045] In some embodiments of the method, comparing the traveled
trajectory to the predetermined road model trajectory may include
determining a transformation that reduces an error between the
traveled trajectory and the predetermined road model trajectory.
Determining a steering direction may be based on one or more
additional cues, including one or more of a left lane mark
polynomial model, a right lane mark polynomial model, holistic path
prediction, motion of a forward vehicle, determined free space
ahead of the autonomous vehicle, and virtual lanes or virtual lane
constraints determined based on positions of vehicles forward of
the autonomous vehicle. Determining the steering direction may
include applying weights to the one or more additional cues.
[0046] In some embodiments, a system for autonomously navigating a
vehicle through a road junction may include at least one processor
programmed to: receive from an image capture device at least one
image representative of an environment of the vehicle; analyze the
at least one image to identify two or more landmarks located in the
environment of the vehicle; determine, for each of the two or more
landmarks, a directional indicator relative to the vehicle;
determine a current location of the vehicle relative to the road
junction based on an intersection of the directional indicators for
the two or more landmarks; determine a heading for the vehicle
based on the directional indicators for the two or more landmarks;
and determine a steering angle for the vehicle by comparing the
vehicle heading with a predetermined road model trajectory at the
current location of the vehicle.
[0047] In some embodiments of the system, the predetermined road
model trajectory may include a three-dimensional polynomial
representation of a target trajectory along the road segment. The
two or more landmarks may include three or more landmarks. The at
least one processor may be further programmed to transmit a control
signal specifying the steering angle to a steering system of the
vehicle. The processor may be configured to retrieve the
predetermined road model trajectory from a database stored in a
memory included in the vehicle. The processor may be configured to
retrieve the predetermined road model trajectory from a database
accessible to the vehicle over a wireless communications interface.
The camera may be included in the vehicle. The processor may be
further programmed to determine the heading for the vehicle by:
determining a previous location of the vehicle relative to the road
junction based on the intersection of the directional indicators
for the two or more landmarks; and determining the heading based on
the previous location and the current location.
[0048] In some embodiments, an autonomous vehicle may include a
body; at least one image capture device configured to acquire at
least one image representative of an environment of the vehicle;
and at least one processor programmed to: receive from a camera at
least one image representative of an environment of the vehicle;
analyze the at least one image to identify two or more landmarks
located in the environment of the vehicle; determine, for each of
the two or more landmarks, a directional indicator relative to the
vehicle; determine a current location of the vehicle relative to
the road junction based on an intersection of the directional
indicators for the two or more landmarks; determine a heading for
the vehicle based on the directional indicators for the two or more
landmarks; and determine a steering angle for the vehicle by
comparing the vehicle heading with a predetermined road model
trajectory at the current location of the vehicle.
[0049] In some embodiments of the vehicle, the predetermined road
model trajectory may include a three-dimensional polynomial
representation of a target trajectory along the road segment. The
two or more landmarks may include three or more landmarks. The at
least one processor may be further programmed to transmit a control
signal specifying the steering angle to a steering system of the
vehicle. The predetermined road model trajectory may be retrieved
from one of a database stored in a memory included in the vehicle
and a database accessible to the vehicle over a wireless
communications interface. The processor may be further programmed
to determine a heading for the vehicle by: determining a previous
location of the vehicle relative to the road junction based on the
intersection of the directional indicators for the two or more
landmarks; and determining the heading based on the previous
location and the current location.
[0050] In some embodiments, a method of navigating an autonomous
vehicle may include receiving, from an image capture device, at
least one image representative of an environment of the vehicle;
analyzing, using at least one processor, the at least one image to
identify two or more landmarks located in the environment of the
vehicle; determining, for each of the two or more landmarks, a
directional indicator relative to the vehicle; determining a
current location of the vehicle relative to the road junction based
on an intersection of the directional indicators for the two or
more landmarks; determining a heading for the vehicle based on the
directional indicators for the two or more landmarks; and
determining a steering angle for the vehicle by comparing the
vehicle heading with a predetermined road model trajectory at the
current location of the vehicle.
[0051] In some embodiments of the method, the predetermined road
model trajectory may include a three-dimensional polynomial
representation of a target trajectory along the road segment. The
method may include retrieving the predetermined road model
trajectory from one of a database stored in a memory included in
the vehicle and a database accessible to the vehicle over a
wireless communications interface. The method may include
transmitting a control signal specifying the steering angle to a
steering system of the vehicle. Determining the heading for the
vehicle may include determining a previous location of the vehicle
relative to the road junction based on the intersection of the
directional indicators for the two or more landmarks; and
determining the heading based on the previous location and the
current location.
[0052] In some embodiments, a system for autonomously navigating a
vehicle based on a plurality of overlapping navigational maps may
include at least one processor programmed to: receive a first
navigational map for use in autonomously controlling the vehicle,
wherein the first navigational map is associated with a first road
segment; determine at least a first autonomous navigational
response for the vehicle along the first road segment based on
analysis of the first navigational map; receive a second
navigational map for use in autonomously controlling the vehicle,
wherein the second navigational map is associated with a second
road segment, wherein the first road segment is different from the
second road segment, and wherein the first road segment and the
second road segment overlap one another at an overlap segment;
determine at least a second autonomous navigational response for
the vehicle along the second road segment based on analysis of the
second navigational map; and determine at least a third autonomous
navigational response for the vehicle in the overlap segment based
on at least one of the first navigational map and the second
navigational map.
[0053] In some embodiments of the system, each of the plurality of
overlapping navigational maps may have its own coordinate frame.
Each of the plurality of overlapping navigational maps may include
a polynomial representation of a target trajectory along a road
segment. Each of the overlapping navigational maps may be a sparse
map having a data density of no more than 10 kilobytes per
kilometer. The overlap segment may have a length of at least 50
meters. The overlap segment may have a length of at least 100
meters. The at least one processor may be programmed to determine
the third autonomous navigational response based on both the first
navigational map and the second navigational map. The third
autonomous navigational response may be a combination of the first
autonomous navigational response and the second autonomous
navigational response. The third autonomous navigational response
may be an average of the first autonomous navigational response and
the second autonomous navigational response. The processor may be
further programmed to: determine an error between the first
autonomous navigational response and the second autonomous
navigational response; and determine the third autonomous
navigational response based on the second autonomous navigational
response when the error is less than a threshold error.
[0054] In some embodiments, an autonomous vehicle may include a
body; at least one image capture device configured to acquire at
least one image representative of an environment of the vehicle; at
least one processor programmed to: determine a current location of
the vehicle based on the at least one image; receive a first
navigational map associated with a first road segment; determine at
least a first autonomous navigational response for the vehicle
based on analysis of the first navigational map, when the current
location of the vehicle lies on the first navigational map; receive
a second navigational map associated with a second road segment
different from the second road segment, the first road segment and
the second road segment overlapping one another at an overlap
segment; determine at least a second autonomous navigational
response for the vehicle based on analysis of the second
navigational map when the current location of the vehicle lies on
the second navigational map; and determine at least a third
autonomous navigational response for the vehicle based on at least
one of the first navigational map and the second navigational map
when the current location of the vehicle lies in the overlap
segment.
[0055] In some embodiments of the autonomous vehicle, each of the
first navigational map and the second navigational map may have its
own coordinate frame. Each of the first navigational map and the
second navigational map may include a polynomial representation of
a target trajectory along a road segment. The at least one
processor may be programmed to determine the third autonomous
navigational response based on both the first navigational map and
the second navigational map. The third autonomous navigational
response may be a combination of the first autonomous navigational
response and the second autonomous navigational response. The
processor may be further programmed to: determine an error between
the first autonomous navigational response and the second
autonomous navigational response; and determine the third
autonomous navigational response based on the second autonomous
navigational response when the error is less than a threshold
error.
[0056] In some embodiments, a method of navigating an autonomous
vehicle may include receiving from an image capture device, at
least one image representative of an environment of the vehicle;
determining, using a processor associated with the vehicle, a
current location of the vehicle based on the at least one image;
receiving a first navigational map associated with a first road
segment; determining at least a first autonomous navigational
response for the vehicle based on analysis of the first
navigational map, when the current location of the vehicle lies on
the first navigational map; receiving a second navigational map
associated with a second road segment different from the second
road segment, the first road segment and the second road segment
overlapping one another at an overlap segment; determining at least
a second autonomous navigational response for the vehicle based on
analysis of the second navigational map when the current location
of the vehicle lies on the second navigational map; and determining
at least a third autonomous navigational response for the vehicle
based on at least one of the first navigational map and the second
navigational map when the current location of the vehicle lies in
the overlap segment.
[0057] In some embodiments of the method, each of the plurality of
overlapping navigational maps may have its own coordinate frame,
and each of the plurality of overlapping navigational maps may
include a polynomial representation of a target trajectory along a
road segment. Determining the third autonomous navigational
response may include determining a combination of the first
autonomous navigational response and the second autonomous
navigational response. The method may include determining an error
between the first autonomous navigational response and the second
autonomous navigational response; and determining the third
autonomous navigational response based on the second autonomous
navigational response when the error is less than a threshold
error.
[0058] In some embodiments, a system for sparse map autonomous
navigation of a vehicle along a road segment may include at least
one processor programmed to: receive a sparse map of the road
segment, wherein the sparse map has a data density of no more than
1 megabyte per kilometer; receive from a camera, at least one image
representative of an environment of the vehicle; analyze the sparse
map and the at least one image received from the camera; and
determine an autonomous navigational response for the vehicle based
solely on the analysis of the sparse map and the at least one image
received from the camera.
[0059] In some embodiments of the system, the sparse map may
include a polynomial representation of a target trajectory along
the road segment. The sparse map may include one or more recognized
landmarks. The recognized landmarks may be spaced apart in the
sparse map at a rate of no more than 0.5 per kilometer. The
recognized landmarks may be spaced apart in the sparse map at a
rate of no more than 1 per kilometer. The recognized landmarks may
be spaced apart in the sparse map at a rate of no more than 1 per
100 meters. The sparse map may have a data density of no more than
100 kilobytes per kilometer. The sparse map may have a data density
of no more than 10 kilobytes per kilometer.
[0060] In some embodiments, a method for sparse map autonomous
navigation of a vehicle along a road segment may include receiving
a sparse map of the road segment, wherein the sparse map has a data
density of no more than 1 megabyte per kilometer; receiving from a
camera, at least one image representative of an environment of the
vehicle; analyzing the sparse map and the at least one image
received from the camera; and determining an autonomous
navigational response for the vehicle based solely on the analysis
of the sparse map and the at least one image received from the
camera.
[0061] In some embodiments of the method, the sparse map may
include a polynomial representation of a target trajectory along
the road segment. The sparse map may include one or more recognized
landmarks. The recognized landmarks may be spaced apart in the
sparse map at a rate of no more than 0.5 per kilometer. The
recognized landmarks may be spaced apart in the sparse map at a
rate of no more than 1 per kilometer. The recognized landmarks may
be spaced apart in the sparse map at a rate of no more than 1 per
100 meters. The sparse map may have a data density of no more than
100 kilobytes per kilometer. The sparse map may have a data density
of no more than 10 kilobytes per kilometer.
[0062] In some embodiments, a non-transitory computer readable
medium may store instructions causing at least one processor to
perform sparse map autonomous navigation of a vehicle along a road
segment, which may include receiving a sparse map of the road
segment. The instructions may cause the processor to perform the
steps of: receiving a sparse map of the road segment, wherein the
sparse map has a data density of no more than 1 megabyte per
kilometer; receiving from a camera, at least one image
representative of an environment of the vehicle; analyzing the
sparse map and the at least one image received from the camera; and
determining an autonomous navigational response for the vehicle
based solely on the analysis of the sparse map and the at least one
image received from the camera.
[0063] In some embodiments of the non-transitory computer readable
medium, the sparse map may include a polynomial representation of a
target trajectory along the road segment. The sparse map may
include one or more recognized landmarks. The recognized landmarks
may be spaced apart in the sparse map at a rate of no more than 0.5
per kilometer.
[0064] In some embodiments, a system for autonomously navigating a
vehicle along a road segment based on a predetermined landmark
location may include at least one processor programmed to: receive
from a camera, at least one image representative of an environment
of the vehicle; determine a position of the vehicle along a
predetermined road model trajectory associated with the road
segment based, at least in part, on information associated with the
at least one image; identify a recognized landmark forward of the
vehicle based on the determined position, wherein the recognized
landmark is beyond a sight range of the camera; determine a current
distance between the vehicle and the recognized landmark by
comparing the determined position of the vehicle with a
predetermined position of the recognized landmark; and determine an
autonomous navigational response for the vehicle based on the
determined current distance.
[0065] In some embodiments of the system, the predetermined
position of the recognized landmark may be determined as an average
of a plurality of acquired position measurements associated with
the recognized landmark, wherein the plurality of acquired position
measurements are determined based on acquisition of at least one
environmental image, analysis of the at least one environmental
image to identify the recognized landmark in the environment,
reception of global positioning system (GPS) data, analysis of the
at least one environmental image to determine a relative position
of the recognized landmark with respect to the vehicle, and
determination of a globally localized position of the recognized
landmark based on at least the GPS data and the determined relative
position. The autonomous navigational response may include
application of brakes associated with the vehicle. The autonomous
navigational response may include modifying a steering angle of the
vehicle. The recognized landmark may include a stop line, a traffic
light, a stop sign, or a curve along the road segment. The camera
may be included in the vehicle.
[0066] In some embodiments, a method for autonomously navigating a
vehicle along a road segment based on a predetermined landmark
location may include receiving from a camera, at least one image
representative of an environment of the vehicle; determining a
position of the vehicle along a predetermined road model trajectory
associated with the road segment based, at least in part, on
information associated with the at least one image; identifying a
recognized landmark forward of the vehicle based on the determined
position, wherein the recognized landmark is beyond a sight range
of the camera; determining a current distance between the vehicle
and the recognized landmark by comparing the determined position of
the vehicle with a predetermined position of the recognized
landmark; and determining an autonomous navigational response for
the vehicle based on the determined current distance.
[0067] In some embodiments of the method, the predetermined
position of the recognized landmark may be determined as an average
of a plurality of acquired position measurements associated with
the recognized landmark, wherein the plurality of acquired position
measurements may be determined based on acquisition of at least one
environmental image, analysis of the at least one environmental
image to identify the recognized landmark in the environment,
reception of global positioning system (GPS) data, analysis of the
at least one environmental image to determine a relative position
of the recognized landmark with respect to the vehicle, and
determination of a globally localized position of the recognized
landmark based on at least the GPS data and the determined relative
position. The autonomous navigational response may include
application of brakes associated with the vehicle. The autonomous
navigational response may include modifying a steering angle of the
vehicle. The recognized landmark may include a stop line, a traffic
light, a stop sign, or a curve along the road segment. The camera
may be included in the vehicle.
[0068] In some embodiments, a non-transitory computer readable
medium may store instructions causing at least one processor to
perform autonomous navigation of a vehicle along a road segment.
The instructions may cause the processor to perform the steps of:
receiving from a camera, at least one image representative of an
environment of the vehicle; determining a position of the vehicle
along a predetermined road model trajectory associated with the
road segment based, at least in part, on information associated
with the at least one image; identifying a recognized landmark
forward of the vehicle based on the determined position, wherein
the recognized landmark is beyond a sight range of the camera;
determining a current distance between the vehicle and the
recognized landmark by comparing the determined position of the
vehicle with a predetermined position of the recognized landmark;
and determining an autonomous navigational response for the vehicle
based on the determined current distance.
[0069] In some embodiments of the non-transitory computer readable
medium, the autonomous navigational response may include
application of brakes associated with the vehicle. The autonomous
navigational response may include modifying a steering angle of the
vehicle. The recognized landmark may include a stop line, a traffic
light, a stop sign, or a curve along the road segment.
[0070] In some embodiments, a system for autonomously navigating a
vehicle along a road segment may include at least one processor
programmed to: receive, from at least one sensor, information
relating to one or more aspects of the road segment; determine a
local feature of the road segment based on the received
information; compare the local feature to a predetermined signature
feature for the road segment; determine a current location of the
vehicle along a predetermined road model trajectory associated with
the road segment based on the comparison of the local feature and
the predetermined signature feature; and determine an autonomous
steering action for the vehicle based on a direction of the
predetermined road model trajectory at the determined location.
[0071] In some embodiments of the system, the at least one
processor may be further programmed to: determine a heading
direction of the vehicle at the current location, and determine the
autonomous steering action by comparing the direction of the
predetermined road model trajectory with the heading direction. The
heading direction may be determined based on a travelled trajectory
of the vehicle. The at least one sensor may include an image
capture device configured to acquire at least one image
representative of an environment of the vehicle. The signature
feature may include a road width profile over at least a portion of
the road segment. The signature feature may include a lane width
profile over at least a portion of the road segment. The signature
feature may include a dashed line spacing profile over at least a
portion of the road segment. The signature feature may include a
predetermined number of road markings along at least a portion of
the road segment. The signature feature may include a road surface
profile over at least a portion of the road segment. The signature
feature may include a predetermined curvature associated with the
road segment. Determining the current location of the vehicle may
include comparing first parameter values indicative of a curvature
of the predetermined road model trajectory and second parameter
values indicative of a curvature of a measured trajectory for the
vehicle. The at least one sensor may include a suspension component
monitor.
[0072] In some embodiments, a vehicle may include a body; at least
one sensor configured to acquire information relating to one or
more aspects of the road segment; and at least one processor
programmed to: determine a local feature of the road segment based
on the information received from the at least one sensor; compare
the local feature to a predetermined signature feature for the road
segment; determine a current location of the vehicle along a
predetermined road model trajectory associated with the road
segment based on the comparison of the local feature and the
predetermined signature feature; and determine an autonomous
steering action for the vehicle based on a direction of the
predetermined road model trajectory at the current location.
[0073] In some embodiments of the vehicle, the signature feature
may include at least one of a road width profile over at least a
portion of the road segment, a lane width profile over at least a
portion of the road segment, a dashed line spacing profile over at
least a portion of the road segment, a predetermined number of road
markings along at least a portion of the road segment, a road
surface profile over at least a portion of the road segment, and a
predetermined curvature associated with the road segment. The
vehicle may include a suspension component monitor, wherein the
processor is further programmed to determine the local feature
based on signals from the suspension component monitor. The
processor may be further programmed to: determine a heading
direction of the vehicle; determine a direction of the
predetermined road model trajectory at the current location; and
determine the autonomous steering action by comparing the direction
with the heading direction.
[0074] In some embodiments, a method of navigating a vehicle may
include receiving, from at least one sensor, information relating
to one or more aspects of the road segment; determining, using at
least one processor, a local feature of the road segment based on
the information received from the at least one sensor; comparing
the received information to a predetermined signature feature for
the road segment; determining a current location of the vehicle
along a predetermined road model trajectory associated with the
road segment based on the comparison of the received information
and the predetermined signature feature; and determining an
autonomous steering action for the vehicle based on a direction of
the predetermined road model trajectory at the current
location.
[0075] In some embodiments, the method may include determining a
heading direction of the vehicle at the current location;
determining the direction of the predetermined road model
trajectory at the current location; and determining the autonomous
steering action by comparing the direction of the predetermined
road model trajectory with the heading direction. The local feature
may include at least one of a road width profile over at least a
portion of the road segment, a lane width profile over at least a
portion of the road segment, a dashed line spacing profile over at
least a portion of the road segment, a predetermined number of road
markings along at least a portion of the road segment, a road
surface profile over at least a portion of the road segment, and a
predetermined curvature associated with the road segment. The
method may include determining, using a suspension component
monitor, a road surface profile; comparing the road surface profile
with a predetermined road surface profile; and determining the
current location based on the comparison of the road surface
profile and the predetermined road surface profile.
[0076] In some embodiments, a system for autonomously navigating a
vehicle may include at least one processor programmed to: receive
from a rearward facing camera, at least one image representing an
area at a rear of the vehicle; analyze the at least one rearward
facing image to locate in the image a representation of at least
one landmark; determine at least one indicator of position of the
landmark relative to the vehicle; determine a forward trajectory
for the vehicle based, at least in part, upon the indicator of
position of the landmark relative to the vehicle; and cause the
vehicle to navigate along the determined forward trajectory.
[0077] In some embodiments of the system, the indicator of position
may include a distance between the vehicle and the landmark. The
indicator of position may include a relative angle between the
vehicle and the landmark. The landmark may include a road edge, a
lane marking, a reflector, a pole, a change in line pattern on a
road, or a road sign. The landmark may include a backside of a road
sign. The at least one processor may be further programmed to
determine a lane offset amount of the vehicle within a current lane
of travel based on the indicator of position of the landmark, and
wherein determination of the forward trajectory is further based on
the determined lane offset amount. The at least one processor may
be further programmed to receive from another camera, at least one
image representing another area of the vehicle, and wherein the
determination of the forward trajectory is further based on the at
least one image received from the another camera.
[0078] In some embodiments, a method of autonomously navigating a
vehicle may include receiving from a rearward facing camera, at
least one image representing an area at a rear of the vehicle;
[0079] analyzing the at least one rearward facing image to locate
in the image a representation of at least one landmark; determining
at least one indicator of position of the landmark relative to the
vehicle; determining a forward trajectory for the vehicle based, at
least in part, upon the indicator of position of the landmark
relative to the vehicle; and causing the vehicle to navigate along
the determined forward trajectory.
[0080] In some embodiments of the method, the indicator of position
may include a distance between the vehicle and the landmark. The
indicator of position may include a relative angle between the
vehicle and the landmark. The landmark may include a road edge, a
lane marking, a reflector, a pole, a change in line pattern on a
road, or a road sign. The landmark may include a backside of a road
sign. The method may include determining a lane offset amount of
the vehicle within a current lane of travel based on the indicator
of position of the landmark, and wherein the determining of the
forward trajectory may be based on the determined lane offset
amount.
[0081] In some embodiments, a vehicle may include a body; a
rearward facing camera; and at least one processor programmed to:
receive, via a rearward camera interface connecting the rearward
facing camera, at least one image representing an area at a rear of
the vehicle; analyze the at least one rearward facing image to
locate in the image a representation of at least one landmark;
determine at least one indicator of position of the landmark
relative to the vehicle; determine a forward trajectory for the
vehicle based, at least in part, upon the indicator of position of
the landmark relative to the vehicle; and
[0082] cause the vehicle to navigate along the determined forward
trajectory.
[0083] In some embodiments of the vehicle, the rearward facing
camera may be mounted on an object connected to the vehicle. The
object may be a trailer, a bike carrier, a ski/snowboard carrier, a
mounting base, or a luggage carrier. The rearward camera interface
may include a detachable interface. The rearward camera interface
may include a wireless interface.
[0084] In some embodiments, a system for navigating a vehicle by
determining a free space region in which a vehicle can travel may
include at least one processor programmed to: receive from an image
capture device, a plurality of images associated with an
environment of a vehicle; analyze at least one of the plurality of
images to identify a first free space boundary on a driver side of
the vehicle and extending forward of the vehicle, a second free
space boundary on a passenger side of the vehicle and extending
forward of the vehicle, and a forward free space boundary forward
of the vehicle and extending between the first free space boundary
and the second free space boundary; wherein the first free space
boundary, the second free space boundary, and the forward free
space boundary define a free space region forward of the vehicle;
determine a navigational path for the vehicle through the free
space region; and cause the vehicle to travel on at least a portion
of the determined navigational path within the free space region
forward of the vehicle.
[0085] In some embodiments of the system, the first free space
boundary may correspond to at least one of a road edge, a curb, a
barrier, a lane dividing structure, a parked vehicle, a tunnel
wall, or a bridge structure. The second free space boundary may
correspond to at least one of a road edge, a curb, a barrier, a
lane dividing structure, a parked vehicle, a tunnel wall, or a
bridge structure. The forward free space boundary may correspond to
a road horizon line. The at least one processor may be further
programmed to identify, based on analysis of the at least one of
the plurality of images, an obstacle forward of the vehicle and
exclude the identified obstacle from the free space region forward
of the vehicle. The obstacle may include a pedestrian. The obstacle
may include another vehicle. The obstacle may include debris. The
at least one processor may be further programmed to identify, based
on analysis of the at least one of the plurality of images, an
obstacle forward of the vehicle and exclude a region surrounding
the identified obstacle from the free space region forward of the
vehicle. The at least one processor may be further programmed to
determine the region surrounding the identified obstacle based on
one or more of the following: a speed of the vehicle, a type of the
obstacle, an image capture rate of the image capture device, and a
movement speed of the obstacle.
[0086] In some embodiments, a vehicle may include a body, the body
including a driver side and a passenger side; an image capture
device; and at least one processor programmed to: receive from the
image capture device, a plurality of images associated with an
environment of the vehicle; analyze at least one of the plurality
of images to identify a first free space boundary on the driver
side of the body and extending forward of the body, a second free
space boundary on the passenger side of the body and extending
forward of the body, and a forward free space boundary forward of
the body and extending between the first free space boundary and
the second free space boundary; wherein the first free space
boundary, the second free space boundary, and the forward free
space boundary define a free space region forward of the body;
determine a navigational path for the vehicle through the free
space region; and cause the vehicle to travel on at least a portion
of the determined navigational path within the free space region
forward of the vehicle.
[0087] In some embodiments, a method of navigating a vehicle by
determining a free space region in which a vehicle can travel may
include receiving from an image capture device, a plurality of
images associated with an environment of a vehicle; analyzing at
least one of the plurality of images to identify a first free space
boundary on a driver side of the vehicle and extending forward of
the vehicle, a second free space boundary on a passenger side of
the vehicle and extending forward of the vehicle, and a forward
free space boundary forward of the vehicle and extending between
the first free space boundary and the second free space boundary;
wherein the first free space boundary, the second free space
boundary, and the forward free space boundary define a free space
region forward of the vehicle; determining a navigational path for
the vehicle through the free space region; and causing the vehicle
to travel on at least a portion of the determined navigational path
within the free space region forward of the vehicle.
[0088] In some embodiments of the method, the first free space
boundary may correspond to at least one of a road edge, a curb, a
barrier, a lane dividing structure, a parked vehicle, a tunnel
wall, or a bridge structure. The second free space boundary may
correspond to at least one of a road edge, a curb, a barrier, a
lane dividing structure, a parked vehicle, a tunnel wall, or a
bridge structure. The forward free space boundary may correspond to
a road horizon line. The method may include identifying, based on
analysis of the at least one of the plurality of images, an
obstacle forward of the vehicle; and excluding the identified
obstacle from the free space region forward of the vehicle. The
obstacle may include a pedestrian. The obstacle may include another
vehicle. The obstacle may include debris. The method may include
identifying, based on analysis of the at least one of the plurality
of images, an obstacle forward of the vehicle; and excluding a
region surrounding the identified obstacle from the free space
region forward of the vehicle. The method may include determining
the region surrounding the identified obstacle based on one or more
of the following: a speed of the vehicle, a type of the obstacle,
an image capture rate of the image capture device, and a movement
speed of the obstacle.
[0089] In some embodiments, a system for navigating a vehicle on a
road with snow covering at least some lane markings and road edges
may include at least one processor programmed to: receive from an
image capture device, at least one environmental image forward of
the vehicle, including areas where snow covers at least some lane
markings and road edges; identify, based on an analysis of the at
least one image, at least a portion of the road that is covered
with snow and probable locations for road edges bounding the at
least a portion of the road that is covered with snow; and cause
the vehicle to navigate a navigational path that includes the
identified portion of the road and falls within the determined
probable locations for the road edges.
[0090] In some embodiments of the system, the analysis of the at
least one image may include identifying at least one tire track in
the snow. The analysis of the at least one image may include
identifying a change of light across a surface of the snow. The
analysis of the at least one image may include identifying a
plurality of trees along an edge of the road. The analysis of the
at least one image may include recognizing a change in curvature at
a surface of the snow. The recognized change in curvature may be
determined to correspond to a probable location of a road edge. The
analysis of the at least one image may include a pixel analysis of
the at least one image in which at least a first pixel is compared
to at least a second pixel in order to determine a feature
associated with a surface of the snow covering at least some lane
markings and road edges. The feature may correspond to an edge of a
tire track. The feature may correspond to an edge of the road. The
at least one processor may be further programmed to cause the
vehicle to navigate between determined edges of the road. The at
least one processor may be further programmed to cause the vehicle
to navigate by at least partially following tire tracks in the
snow.
[0091] In some embodiments, a method of navigating a vehicle on a
road with snow covering at least some lane markings and road edges
may include receiving from an image capture device, at least one
environmental image forward of the vehicle, including areas where
snow covers at least some lane markings and road edges;
identifying, based on an analysis of the at least one image, at
least a portion of the road that is covered with snow and probable
locations for road edges bounding the at least a portion of the
road that is covered with snow; and causing the vehicle to navigate
a navigational path that includes the identified portion of the
road and falls within the determined probable locations for the
road edges.
[0092] In some embodiments of the method, the analysis of the at
least one image may include identifying at least one tire track in
the snow. The analysis of the at least one image may include
identifying a change of light across a surface of the snow. The
analysis of the at least one image may include identifying a
plurality of trees along an edge of the road. The analysis of the
at least one image may include recognizing a change in curvature at
a surface of the snow. The recognized change in curvature may be
determined to correspond to a probable location of a road edge. The
analysis of the at least one image may include a pixel analysis of
the at least one image in which at least a first pixel is compared
to at least a second pixel in order to determine a feature
associated with a surface of the snow covering at least some lane
markings and road edges. The feature may correspond to an edge of a
tire track. The feature may correspond to an edge of the road. The
method may include causing the vehicle to navigate between
determined edges of the road. The method may include causing the
vehicle to navigate by at least partially following tire tracks in
the snow.
[0093] In some embodiments, a system for navigating a vehicle on a
road at least partially covered with snow may include at least one
processor programmed to: receive from an image capture device, a
plurality of images captured of an environment forward of the
vehicle, including areas where snow covers a road on which the
vehicle travels; analyze at least one of the plurality of images to
identify a first free space boundary on a driver side of the
vehicle and extending forward of the vehicle, a second free space
boundary on a passenger side of the vehicle and extending forward
of the vehicle, and a forward free space boundary forward of the
vehicle and extending between the first free space boundary and the
second free space boundary; wherein the first free space boundary,
the second free space boundary, and the forward free space boundary
define a free space region forward of the vehicle; determine a
first proposed navigational path for the vehicle through the free
space region; provide the at least one of the plurality of images
to a neural network and receive from the neural network a second
proposed navigational path for the vehicle based on analysis of the
at least one of the plurality of images by the neural network;
determine whether the first proposed navigational path agrees with
the second proposed navigational path; and cause the vehicle to
travel on at least a portion of the first proposed navigational
path if the first proposed navigational path is determined to agree
with the second proposed navigational path.
[0094] In some embodiments, a system for calibrating an indicator
of speed of an autonomous vehicle may include at least one
processor programmed to: receive from a camera a plurality of
images representative of an environment of the vehicle; analyze the
plurality of images to identify at least two recognized landmarks;
determine, based on known locations of the two recognized
landmarks, a value indicative of a distance between the at least
two recognized landmarks; determine, based on an output of at least
one sensor associated with the autonomous vehicle, a measured
distance between the at least two landmarks; and determine a
correction factor for the at least one sensor based on a comparison
of the value indicative of the distance between the at least to
recognized landmarks and the measured distance between the at least
two landmarks.
[0095] In some embodiments of the system, the correction factor may
be determined such that an operation on the determined distance
along the road segment by the correction factor matches the
distance value received via the wireless transceiver. The two
recognized landmarks may include one or more of a traffic sign, an
arrow marking, a lane marking, a dashed lane marking, a traffic
light, a stop line, a directional sign, a reflector, a landmark
beacon, or a lamppost. The at least one sensor may include a
speedometer associated with the vehicle. The known locations of the
two recognized landmarks may be received from a server based system
located remotely with respect to the vehicle. Each of the known
locations may constitute a refined location determined based on a
plurality of GPS-based measurements.
[0096] In some embodiments, a system for calibrating an indicator
of speed of an autonomous vehicle may include at least one
processor programmed to: determine a distance along a road segment
based on an output of at least one sensor associated with the
autonomous vehicle; receive, via a wireless transceiver, a distance
value associated with the road segment; and determine a correction
factor for the at least one sensor based on the determined distance
along the road segment and the distance value received via the
wireless transceiver.
[0097] In some embodiments of the system, the distance value
associated with the road segment, received via the wireless
transceiver, may be determined based on prior measurements made by
a plurality of measuring vehicles. The plurality of measuring
vehicles may include at least 100 measuring vehicles. The plurality
of measuring vehicles may include at least 1000 measuring vehicles.
The correction factor may be determined such that an operation on
the determined distance along the road segment by the correction
factor matches the distance value received via the wireless
transceiver. The at least one processor may be programmed to
determine a composite correction factor based on a plurality of
determined correction factors. The composite correction factor may
be determined by averaging the plurality of determined correction
factors. The composite correction factor may be determined by
finding a mean of the plurality of determined correction
factors.
[0098] In some embodiments, a vehicle may include a body; a camera;
and at least one processor programmed to: receive from the camera a
plurality of images representative of an environment of the
vehicle; analyze the plurality of images to identify at least two
recognized landmarks; determine, based on known locations of the
two recognized landmarks, a value indicative of a distance between
the at least two recognized landmarks; determine, based on an
output of at least one sensor associated with the autonomous
vehicle, a measured distance between the at least two landmarks;
and determine a correction factor for the at least one sensor based
on a comparison of the value indicative of the distance between the
at least to recognized landmarks and the measured distance between
the at least two landmarks.
[0099] In some embodiments of the vehicle, the at least one sensor
may include a speedometer associated with the vehicle. The two
recognized landmarks may include one or more of a traffic sign, an
arrow marking, a lane marking, a dashed lane marking, a traffic
light, a stop line, a directional sign, a reflector, a landmark
beacon, or a lamppost. The known locations of the two recognized
landmarks may be received from a server based system located
remotely with respect to the vehicle.
[0100] In some embodiments, a system for determining a lane
assignment for an autonomous vehicle along a road segment may
include at least one processor programmed to: receive from a camera
at least one image representative of an environment of the vehicle;
analyze the at least one image to identify at least one recognized
landmark; determine an indicator of a lateral offset distance
between the vehicle and the at least one recognized landmark; and
determine a lane assignment of the vehicle along the road segment
based on the indicator of the lateral offset distance between the
vehicle and the at least one recognized landmark.
[0101] In some embodiments of the system, the environment of the
vehicle may include the road segment, a number of lanes, and the at
least one recognized landmark. The at least one recognized landmark
may include at least one of a traffic sign, an arrow marking, a
lane marking, a dashed lane marking, a traffic light, a stop line,
a directional sign, a reflector, a landmark beacon, or a lamppost.
The at least one recognized landmark may include a sign for a
business. The lateral offset distance between the vehicle and the
at least one recognized landmark may be a sum of a first distance
between the vehicle and a first side of the road segment and a
second distance between the first side of the road and the at least
one recognized landmark. The determination of the indicator of the
lateral offset distance between the vehicle and the at least one
recognized landmark may be based on a predetermined position of the
at least one recognized landmark. The determination of the
indicator of the lateral offset distance between the vehicle and
the at least one recognized landmark may be based on a scale
associated with the at least one image. The determination of the
lane assignment may be further based on at least one of a width of
the road segment, a number of lanes of the road segment, and a lane
width. The determination of the lane assignment may be further
based on a predetermined road model trajectory associated with the
road segment. The at least one recognized landmark may include a
first recognized landmark on a first side of the vehicle and a
second recognized landmark on a second side of the vehicle and
wherein determination of the lane assignment of the vehicle along
the road segment is based on a first indicator of lateral offset
distance between the vehicle and the first recognized landmark and
a second indicator of lateral offset distance between the vehicle
and the second recognized landmark.
[0102] In some embodiments, a computer-implemented method for
determining a lane assignment for an autonomous vehicle along a
road segment may include the following operations performed by one
or more processors: receiving from a camera at least one image
representative of an environment of the vehicle; analyzing the at
least one image to identify at least one recognized landmark;
determining an indicator of a lateral offset distance between the
vehicle and the at least one recognized landmark; and determining a
lane assignment of the vehicle along the road segment based on the
indicator of the lateral offset distance between the vehicle and
the at least one recognized landmark.
[0103] In some embodiments of the method, the at least one
recognized landmark may include at least one of a traffic sign, an
arrow marking, a lane marking, a dashed lane marking, a traffic
light, a stop line, a directional sign, a reflector, a landmark
beacon, or a lamppost. The at least one recognized landmark may
include a sign for a business. The determination of the lane
assignment may be further based on a predetermined road model
trajectory associated with the road segment. The at least one
recognized landmark may include a first recognized landmark on a
first side of the vehicle and a second recognized landmark on a
second side of the vehicle and wherein determination of the lane
assignment of the vehicle along the road segment is based on a
first indicator of lateral offset distance between the vehicle and
the first recognized landmark and a second indicator of lateral
offset distance between the vehicle and the second recognized
landmark.
[0104] In some embodiments, a computer-readable storage medium may
include a set of instructions that are executable by at least one
processor to cause the at least one processor to perform a method
for determining a lane assignment for an autonomous vehicle along a
road segment. The method may include receiving from a camera at
least one image representative of an environment of the vehicle;
analyzing the at least one image to identify at least one
recognized landmark; determining an indicator of a lateral offset
distance between the vehicle and the at least one recognized
landmark; and determining a lane assignment of the vehicle along
the road segment based on the indicator of the lateral offset
distance between the vehicle and the at least one recognized
landmark.
[0105] In some embodiments of the computer-readable storage medium,
the at least one recognized landmark may include at least one of a
traffic sign, an arrow marking, a lane marking, a dashed lane
marking, a traffic light, a stop line, a directional sign, a
reflector, a landmark beacon, or a lamppost. The at least one
recognized landmark may include a sign for a business. The
determination of the lane assignment may be further based on a
predetermined road model trajectory associated with the road
segment. The at least one recognized landmark may include a first
recognized landmark on a first side of the vehicle and a second
recognized landmark on a second side of the vehicle and wherein
determination of the lane assignment of the vehicle along the road
segment is based on a first indicator of lateral offset distance
between the vehicle and the first recognized landmark and a second
indicator of lateral offset distance between the vehicle and the
second recognized landmark.
[0106] In some embodiments, a system for autonomously navigating a
vehicle along a road segment may include at least one processor
programmed to: receive from a camera at least one image
representative of an environment of the vehicle; analyze the at
least one image to identify at least one recognized landmark,
wherein the at least one recognized landmark is part of a group of
recognized landmarks, and identification of the at least one
recognized landmark is based, at least in part, upon one or more
landmark group characteristics associated with the group of
recognized landmarks; determine a current location of the vehicle
relative to a predetermined road model trajectory associated with
the road segment based, at least in part, on a predetermined
location of the recognized landmark; and determine an autonomous
steering action for the vehicle based on a direction of the
predetermined road model trajectory at the determined current
location of the vehicle relative to the predetermined road model
trajectory.
[0107] In some embodiments of the system, the at least one
recognized landmark may include at least one of a traffic sign, an
arrow marking, a lane marking, a dashed lane marking, a traffic
light, a stop line, a directional sign, a reflector, a landmark
beacon, or a lamppost. The at least one recognized landmark may
include a sign for a business. The predetermined road model
trajectory may include a three-dimensional polynomial
representation of a target trajectory along the road segment. The
at least one processor may be further programmed to determine a
current location of the vehicle along the predetermined road model
trajectory based on a vehicle velocity. The one or more landmark
group characteristics may include relative distances between
members of the group of recognized landmarks. The one or more
landmark group characteristics may include an ordering sequence of
members of the group of recognized landmarks. The one or more
landmark group characteristics may include a number of landmarks
included in the group of recognized landmarks. Identification of
the at least one recognized landmark may be based, at least in
part, upon a super landmark signature associated with the group of
recognized landmarks. The at least one processor may be programmed
to determine an autonomous steering action for the vehicle by
comparing a heading direction of the vehicle to the predetermined
road model trajectory at the determined current location of the
vehicle.
[0108] In some embodiments, a computer-implemented method for
autonomously navigating a vehicle along a road segment may include
the following operations performed by one or more processors:
receiving from a camera at least one image representative of an
environment of the vehicle; analyzing the at least one image to
identify at least one recognized landmark, wherein the at least one
recognized landmark is part of a group of recognized landmarks, and
identification of the at least one recognized landmark is based, at
least in part, upon one or more landmark group characteristics
associated with the group of recognized landmarks; determining,
relative to the vehicle, a current location of the vehicle relative
to a predetermined road model trajectory associated with the road
segment based, at least in part, on a predetermined location of the
recognized landmark; and determining an autonomous steering action
for the vehicle based on a direction of the predetermined road
model trajectory at the determined current location of the vehicle
relative to the predetermined road model trajectory.
[0109] In some embodiments of the method, the at least one
recognized landmark may include at least one of a traffic sign, an
arrow marking, a lane marking, a dashed lane marking, a traffic
light, a stop line, a directional sign, a reflector, a landmark
beacon, or a lamppost. The at least one recognized landmark may
include a sign for a business. The one or more landmark group
characteristics may include relative distances between members of
the group of recognized landmarks. The one or more landmark group
characteristics may include an ordering sequence of members of the
group of recognized landmarks.
[0110] In some embodiments, a computer-readable storage medium may
include a set of instructions that are executable by at least one
processor to cause the at least one processor to perform a method
for autonomously navigating a vehicle along a road segment. The
method may include receiving from a camera at least one image
representative of an environment of the vehicle; analyzing the at
least one image to identify at least one recognized landmark,
wherein the at least one recognized landmark is part of a group of
recognized landmarks, and identification of the at least one
recognized landmark is based, at least in part, upon one or more
landmark group characteristics associated with the group of
recognized landmarks; determining, relative to the vehicle, a
current location of the vehicle relative to a predetermined road
model trajectory associated with the road segment based, at least
in part, on a predetermined location of the recognized landmark;
and determining an autonomous steering action for the vehicle based
on a direction of the predetermined road model trajectory at the
determined current location of the vehicle relative to the
predetermined road model trajectory.
[0111] In some embodiments of the computer-readable storage medium,
the at least one landmark may include at least one of a traffic
sign, an arrow marking, a lane marking, a dashed lane marking, a
traffic light, a stop line, a directional sign, a reflector, a
landmark beacon, or a lamppost. The at least one recognized
landmark may include a sign for a business. The one or more
landmark group characteristics may include relative distances
between members of the group of recognized landmarks. The one or
more landmark group characteristics may include an ordering
sequence of members of the group of recognized landmarks.
[0112] In some embodiments, a navigation system for a vehicle may
include at least one processor programmed to: receive from a
camera, at least one environmental image associated with the
vehicle; determine a navigational maneuver for the vehicle based on
analysis of the at least one environmental image; cause the vehicle
to initiate the navigational maneuver; receive a user input,
associated with a user's navigational response different from the
initiated navigational maneuver; determine navigational situation
information relating to the vehicle based on the received user
input; and store the navigational situation information in
association with information relating to the user input.
[0113] In some embodiments of the system, the navigational maneuver
may be based on a recognized landmark identified in the at least
one environmental image. The information relating to the user input
may include information specifying at least one of a degree of a
turn of the vehicle, an amount of an acceleration of the vehicle,
and an amount of braking of the vehicle. The control system may
include at least one of a steering control, an acceleration
control, and a braking control. The navigational situation
information may include one or more images captured by a camera
onboard the vehicle. The user input may include at least one of
braking, steering, or accelerating. The navigational situation
information may include a location of the vehicle. The navigational
situation information may include at least one output of a sensor
onboard the vehicle. The sensor may be a speedometer. The sensor
may be an accelerometer. The sensor may be an IR sensor. The
navigational situation information may include a time of day. The
navigational situation information may include an indication of the
presence of a vision inhibitor. The vision inhibitor may be caused
by glare. The navigational situation information may be determined
based on the at least one environmental image. The system may
include a transmitter for sending the navigational situation
information to a server remote from the vehicle.
[0114] In some embodiments, a non-transitory computer-readable
medium may include instructions that are executable by at least one
processor to cause the at least one processor to perform a method.
The method may include receiving from a camera, at least one
environmental image associated with the vehicle; determining a
navigational maneuver for the vehicle based on analysis of the at
least one environmental image; causing the vehicle to initiate the
navigational maneuver; receiving a user input, associated with a
user's navigational response different from the initiated
navigational maneuver; determining navigational situation
information relating to the vehicle based on the received user
input; and storing the navigational situation information in
association with information relating to the user input.
[0115] In some embodiments, a navigation system for a vehicle may
include at least one processor programmed to: determine a
navigational maneuver for the vehicle based, at least in part, on a
comparison of a motion of the vehicle with respect to a
predetermined model representative of a road segment; receive from
a camera, at least one image representative of an environment of
the vehicle; determine, based on analysis of the at least one
image, an existence in the environment of the vehicle of a
navigational adjustment condition; cause the vehicle to adjust the
navigational maneuver based on the existence of the navigational
adjustment condition; and store information relating to the
navigational adjustment condition.
[0116] In some embodiments of the system, the navigational
adjustment condition may include a parked car. The navigational
adjustment condition may include a lane shift. The navigational
adjustment condition may include at least one of a newly
encountered traffic sign or a newly encountered traffic light. The
navigational adjustment condition may include an area of
construction. The processor may be further programmed to cause the
stored information relating to the navigational adjustment
condition to be transmitted to a road model management system for
determining whether an update to the predetermined model
representative of the road segment is warranted by the navigational
adjustment condition. The information stored relative to the
navigational adjustment condition may include at least one of an
indicator of location where the navigational adjustment condition
was encountered, an indication of the adjustment made to the
navigational maneuver, and the at least one image. The
predetermined model representative of the road segment may include
a three-dimensional spline representing a predetermined path of
travel along the road segment.
[0117] In some embodiments, a method for navigating a vehicle may
include determining a navigational maneuver for the vehicle based,
at least in part, on a comparison of a motion of the vehicle with
respect to a predetermined model representative of a road segment;
receiving from a camera, at least one image representative of an
environment of the vehicle; determining, based on analysis of the
at least one image, an existence in the environment of the vehicle
of a navigational adjustment condition; causing the vehicle to
adjust the navigational maneuver based on the existence of the
navigational adjustment condition; and storing information relating
to the navigational adjustment condition.
[0118] In some embodiments of the method, the navigational
adjustment condition may include a parked car. The navigational
adjustment condition may include a lane shift. The navigational
adjustment condition may include at least one of a newly
encountered traffic sign or a newly encountered traffic light. The
navigational adjustment condition may include an area of
construction. The method may include causing the stored information
relating to the navigational adjustment condition to be transmitted
to a road model management system for determining whether an update
to the predetermined model representative of the road segment is
warranted by the navigational adjustment condition. The information
stored relative to the navigational adjustment condition may
include at least one of an indicator of location where the
navigational adjustment condition was encountered, an indication of
the adjustment made to the navigational maneuver, and the at least
one image. The predetermined model representative of the road
segment may include a three-dimensional spline representing a
predetermined path of travel along the road segment.
[0119] In some embodiments, a non-transitory computer-readable
medium may include instructions that are executable by at least one
processor to cause the at least one processor to perform a method.
The method may include determining a navigational maneuver for the
vehicle based, at least in part, on a comparison of a motion of the
vehicle with respect to a predetermined model representative of a
road segment; receiving from a camera, at least one image
representative of an environment of the vehicle; determining, based
on analysis of the at least one image, an existence in the
environment of the vehicle of a navigational adjustment condition;
causing the vehicle to adjust the navigational maneuver based on
the existence of the navigational adjustment condition; and storing
information relating to the navigational adjustment condition.
[0120] In some embodiments of the computer-readable medium, the
method may include causing the stored information relating to the
navigational adjustment condition to be transmitted to a road model
management system for determining whether an update to the
predetermined model representative of the road segment is warranted
by the navigational adjustment condition. The information stored
relative to the navigational adjustment condition may include at
least one of an indicator of location where the navigational
adjustment condition was encountered, an indication of the
adjustment made to the navigational maneuver, and the at least one
image. The predetermined model representative of the road segment
may include a three-dimensional spline representing a predetermined
path of travel along the road segment.
[0121] In some embodiments, a system for interacting with a
plurality of autonomous vehicles may include a memory including a
predetermined model representative of at least one road segment;
and at least one processor programmed to: receive from each of the
plurality of autonomous vehicles navigational situation information
associated with an occurrence of an adjustment to a determined
navigational maneuver; analyze the navigational situation
information; determine, based on the analysis of the navigational
situation information, whether the adjustment to the determined
navigational maneuver was due to a transient condition; and update
the predetermined model representative of the at least one road
segment if the adjustment to the determined navigational maneuver
was not due to a transient condition.
[0122] In some embodiments of the system, the predetermined model
representative of at least one road segment may include a
three-dimensional spline representing a predetermined path of
travel along the at least one road segment. The update to the
predetermined model may include an update to the three-dimensional
spline representing a predetermined path of travel along the at
least one road segment. The adjustment to a determined navigational
maneuver may be resulted from a user intervention. The adjustment
to a determined navigational maneuver may be resulted from an
automatic determination, based on image analysis, of an existence
in a vehicle environment of a navigational adjustment condition.
The navigational situation information may include at least one
image representing an environment of an autonomous vehicle. The
navigational situation information may include a video representing
an environment of an autonomous vehicle. The transient condition
may be associated with a parked car, an intervening car, a
pedestrian, a low light condition, a glare condition, a temporary
barrier, or temporary roadwork.
[0123] In some embodiments, a method for interacting with a
plurality of autonomous vehicles may include receiving from each of
the plurality of autonomous vehicles navigational situation
information associated with an occurrence of an adjustment to a
determined navigational maneuver; analyzing the navigational
situation information; determining, based on the analysis of the
navigational situation information, whether the adjustment to the
determined navigational maneuver was due to a transient condition;
and updating a predetermined model representative of the at least
one road segment if the adjustment to the determined navigational
maneuver was not due to a transient condition.
[0124] In some embodiments of the method, the predetermined model
representative of at least one road segment may include a
three-dimensional spline representing a predetermined path of
travel along the at least one road segment. The update to the
predetermined model may include an update to the three-dimensional
spline representing a predetermined path of travel along the at
least one road segment. The adjustment to a determined navigational
maneuver may be resulted from a user intervention. The adjustment
to a determined navigational maneuver may be resulted from an
automatic determination, based on image analysis, of an existence
in a vehicle environment of a navigational adjustment condition.
The navigational situation information may include at least one
image representing an environment of an autonomous vehicle. The
navigational situation information may include a video representing
an environment of an autonomous vehicle. The transient condition
may be associated with a parked car, an intervening car, a
pedestrian, a low light condition, a glare condition, a temporary
barrier, or temporary roadwork.
[0125] In some embodiments, a non-transitory computer-readable
medium may include instructions that are executable by at least one
processor to cause the at least one processor to perform a method.
The method may include receiving from each of the plurality of
autonomous vehicles navigational situation information associated
with an occurrence of an adjustment to a determined navigational
maneuver; analyzing the navigational situation information;
determining, based on the analysis of the navigational situation
information, whether the adjustment to the determined navigational
maneuver was due to a transient condition; and updating the
predetermined model representative of the at least one road segment
if the adjustment to the determined navigational maneuver was not
due to a transient condition.
[0126] In some embodiments of the computer-readable medium, the
predetermined model representative of at least one road segment may
include a three-dimensional spline representing a predetermined
path of travel along the at least one road segment. Updating the
predetermined model may include an update to the three-dimensional
spline representing a predetermined path of travel along the at
least one road segment. The transient condition may be associated
with a parked car, an intervening car, a pedestrian, a low light
condition, a glare condition, a temporary barrier, or temporary
roadwork.
[0127] In some embodiments, a system for interacting with a
plurality of autonomous vehicles may include a memory including a
predetermined road model representative of at least one road
segment; and at least one processor programmed to: selectively
receive, from the plurality of autonomous vehicles, road
environment information based on navigation by the plurality of
autonomous vehicles through their respective road environments;
determine whether one or more updates to the predetermined road
model are required based on the road environment information; and
update the predetermined road model to include the one or more
updates.
[0128] In some embodiments of the system, the road model may
include a three-dimensional spline representing a predetermined
path of travel along the at least one road segment. Selectively
receiving the road environment information may include a limitation
on a frequency of information transmissions received from a
particular vehicle. Selectively receiving the road environment
information may include a limitation on a frequency of information
transmissions received from a group of vehicles. Selectively
receiving the road environment information may include a limitation
on a frequency of information transmissions received from vehicles
traveling within a particular geographic region. Selectively
receiving the road environment information may include a limitation
on a frequency of information transmissions received from vehicles
based on a determined model confidence level associated with a
particular geographic region. Selectively receiving the road
environment information may include a limitation on information
transmissions received from vehicles to only those transmissions
that include a potential discrepancy with respect to at least one
aspect of the predetermined road model.
[0129] In some embodiments, a method for interacting with a
plurality of autonomous vehicles may include selectively receiving,
from the plurality of autonomous vehicles, road environment
information based on navigation by the plurality of autonomous
vehicles through their respective road environments; determining
whether one or more updates to the predetermined road model are
required based on the road environment information; and updating
the predetermined road model to include the one or more
updates.
[0130] In some embodiments of the method, the road model may
include a three-dimensional spline representing a predetermined
path of travel along the at least one road segment. Selectively
receiving the road environment information may include a limitation
on a frequency of information transmissions received from a
particular vehicle. Selectively receiving the road environment
information may include a limitation on a frequency of information
transmissions received from a group of vehicles. Selectively
receiving the road environment information may include a limitation
on a frequency of information transmissions received from vehicles
traveling within a particular geographic region. Selectively
receiving the road environment information may include a limitation
on a frequency of information transmissions received from vehicles
based on a determined model confidence level associated with a
particular geographic region. Selectively receiving the road
environment information may include a limitation on information
transmissions received from vehicles to only those transmissions
that include a potential discrepancy with respect to at least one
aspect of the predetermined road model.
[0131] In some embodiments, a non-transitory computer-readable
medium may include instructions that are executable by at least one
processor to cause the at least one processor to perform a method.
The method may include selectively receiving, from the plurality of
autonomous vehicles, road environment information based on
navigation by the plurality of autonomous vehicles through their
respective road environments; determining whether one or more
updates to the predetermined road model are required based on the
road environment information; and updating the predetermined road
model to include the one or more updates.
[0132] In some embodiments of the computer-readable medium, the
road model may include a three-dimensional spline representing a
predetermined path of travel along the at least one road segment.
Selectively receiving the road environment information may include
a limitation on a frequency of information transmissions received
from a particular vehicle. Selectively receiving the road
environment information may include a limitation on a frequency of
information transmissions received from a group of vehicles.
Selectively receiving the road environment information may include
a limitation on a frequency of information transmissions received
from vehicles traveling within a particular geographic region.
Selectively receiving the road environment information may include
a limitation on information transmissions received from vehicles to
only those transmissions that include a potential discrepancy with
respect to at least one aspect of the predetermined road model.
[0133] Consistent with other disclosed embodiments, non-transitory
computer-readable storage media may store program instructions,
which are executed by at least one processing device and perform
any of the methods described herein.
[0134] The foregoing general description and the following detailed
description are exemplary and explanatory only and are not
restrictive of the claims.
BRIEF DESCRIPTION OF THE DRAWINGS
[0135] The accompanying drawings, which are incorporated in and
constitute a part of this disclosure, illustrate various disclosed
embodiments. In the drawings:
[0136] FIG. 1 is a diagrammatic representation of an exemplary
system consistent with the disclosed embodiments.
[0137] FIG. 2A is a diagrammatic side view representation of an
exemplary vehicle including a system consistent with the disclosed
embodiments.
[0138] FIG. 2B is a diagrammatic top view representation of the
vehicle and system shown in FIG. 2A consistent with the disclosed
embodiments.
[0139] FIG. 2C is a diagrammatic top view representation of another
embodiment of a vehicle including a system consistent with the
disclosed embodiments.
[0140] FIG. 2D is a diagrammatic top view representation of yet
another embodiment of a vehicle including a system consistent with
the disclosed embodiments.
[0141] FIG. 2E is a diagrammatic top view representation of yet
another embodiment of a vehicle including a system consistent with
the disclosed embodiments.
[0142] FIG. 2F is a diagrammatic representation of exemplary
vehicle control systems consistent with the disclosed
embodiments.
[0143] FIG. 3A is a diagrammatic representation of an interior of a
vehicle including a rearview mirror and a user interface for a
vehicle imaging system consistent with the disclosed
embodiments.
[0144] FIG. 3B is an illustration of an example of a camera mount
that is configured to be positioned behind a rearview mirror and
against a vehicle windshield consistent with the disclosed
embodiments.
[0145] FIG. 3C is an illustration of the camera mount shown in FIG.
3B from a different perspective consistent with the disclosed
embodiments.
[0146] FIG. 3D is an illustration of an example of a camera mount
that is configured to be positioned behind a rearview mirror and
against a vehicle windshield consistent with the disclosed
embodiments.
[0147] FIG. 4 is an exemplary block diagram of a memory configured
to store instructions for performing one or more operations
consistent with the disclosed embodiments.
[0148] FIG. 5A is a flowchart showing an exemplary process for
causing one or more navigational responses based on monocular image
analysis consistent with disclosed embodiments.
[0149] FIG. 5B is a flowchart showing an exemplary process for
detecting one or more vehicles and/or pedestrians in a set of
images consistent with the disclosed embodiments.
[0150] FIG. 5C is a flowchart showing an exemplary process for
detecting road marks and/or lane geometry information in a set of
images consistent with the disclosed embodiments.
[0151] FIG. 5D is a flowchart showing an exemplary process for
detecting traffic lights in a set of images consistent with the
disclosed embodiments.
[0152] FIG. 5E is a flowchart showing an exemplary process for
causing one or more navigational responses based on a vehicle path
consistent with the disclosed embodiments.
[0153] FIG. 5F is a flowchart showing an exemplary process for
determining whether a leading vehicle is changing lanes consistent
with the disclosed embodiments.
[0154] FIG. 6 is a flowchart showing an exemplary process for
causing one or more navigational responses based on stereo image
analysis consistent with the disclosed embodiments.
[0155] FIG. 7 is a flowchart showing an exemplary process for
causing one or more navigational responses based on an analysis of
three sets of images consistent with the disclosed embodiments.
[0156] FIG. 8 shows a sparse map for providing autonomous vehicle
navigation, consistent with the disclosed embodiments.
[0157] FIG. 9A illustrates a polynomial representation of a
portions of a road segment consistent with the disclosed
embodiments.
[0158] FIG. 9B illustrates a curve in three-dimensional space
representing a target trajectory of a vehicle, for a particular
road segment, included in a sparse map consistent with the
disclosed embodiments.
[0159] FIG. 10 illustrates example landmarks that may be included
in sparse map consistent with the disclosed embodiments.
[0160] FIG. 11A shows polynomial representations of trajectories
consistent with the disclosed embodiments.
[0161] FIGS. 11B and 11C show target trajectories along a
multi-lane road consistent with disclosed embodiments.
[0162] FIG. 11D shows an example road signature profile consistent
with disclosed embodiments.
[0163] FIG. 12 is a schematic illustration of a system that uses
crowd sourcing data received from a plurality of vehicles for
autonomous vehicle navigation, consistent with the disclosed
embodiments.
[0164] FIG. 13 illustrates an example autonomous vehicle road
navigation model represented by a plurality of three dimensional
splines, consistent with the disclosed embodiments.
[0165] FIG. 14 illustrates a block diagram of a server consistent
with the disclosed embodiments.
[0166] FIG. 15 illustrates a block diagram of a memory consistent
with the disclosed embodiments.
[0167] FIG. 16 illustrates a process of clustering vehicle
trajectories associated with vehicles, consistent with the
disclosed embodiments.
[0168] FIG. 17 illustrates a navigation system for a vehicle, which
may be used for autonomous navigation, consistent with the
disclosed embodiments.
[0169] FIG. 18 is a flowchart showing an example process for
processing vehicle navigation information for use in autonomous
vehicle navigation, consistent with the disclosed embodiments.
[0170] FIG. 19 is a flowchart showing an example process performed
by a navigation system of a vehicle, consistent with the disclosed
embodiments.
[0171] FIG. 20 shows an example diagram of a memory consistent with
the disclosed embodiments.
[0172] FIG. 21 is a flowchart illustrating an example process for
uploading a recommended trajectory to a server consistent with the
disclosed embodiments.
[0173] FIG. 22 illustrates an example environment including a
system for identifying a landmark for use in autonomous vehicle
navigation consistent with the disclosed embodiments.
[0174] FIG. 23 illustrates an example environment including a
system for identifying a landmark for use in autonomous vehicle
navigation consistent with the disclosed embodiments.
[0175] FIG. 24 illustrates a method of determining a condensed
signature representation of a landmark consistent with the
disclosed embodiments.
[0176] FIG. 25 illustrates another method of determining a
condensed signature representation of a landmark consistent with
the disclosed embodiments.
[0177] FIG. 26 illustrates an example block diagram of a memory
consistent with the disclosed embodiments.
[0178] FIG. 27 is a flowchart showing an exemplary process for
determining an identifier of a landmark consistent with the
disclosed embodiments.
[0179] FIG. 28 is a flowchart showing an exemplary process for
updating and distributing a vehicle road navigation model based on
an identifier consistent with the disclosed embodiments.
[0180] FIG. 29 illustrates an example block diagram of a system for
determining a location of a landmark for use in navigation of an
autonomous vehicle consistent with the disclosed embodiments.
[0181] FIG. 30 illustrates an example block diagram of a memory
consistent with the disclosed embodiments.
[0182] FIG. 31 illustrates an example scaling method for
determining a distance from a vehicle to a landmark consistent with
the disclosed embodiments.
[0183] FIG. 32 illustrates an example optical flow method for
determining a distance from a vehicle to a landmark consistent with
the disclosed embodiments.
[0184] FIG. 33A is a flowchart showing an example process for
determining a location of a landmark for use in navigation of an
autonomous vehicle consistent with the disclosed embodiments.
[0185] FIG. 33B is a flowchart showing an example process for
measuring a position of a landmark for use in navigation of an
autonomous vehicle consistent with the disclosed embodiments.
[0186] FIG. 34 is a diagrammatic top view representation of an
exemplary vehicle including a system consistent with the disclosed
embodiments in which the vehicle navigates using a landmark.
[0187] FIG. 35 is another diagrammatic top view representation of
an exemplary vehicle including a system consistent with the
disclosed embodiments in which the vehicle navigates using a
landmark.
[0188] FIG. 36 is a flowchart showing an exemplary process for
navigating an exemplary vehicle using a landmark.
[0189] FIG. 37 is a diagrammatic top view representation of an
exemplary autonomous vehicle including a system consistent with the
disclosed embodiments in which the autonomous vehicle navigates
using tail alignment.
[0190] FIG. 38 is another diagrammatic top view representation of
an exemplary autonomous vehicle including a system consistent with
the disclosed embodiments in which the autonomous vehicle navigates
using tail alignment.
[0191] FIG. 39 is a flowchart showing an exemplary process for
navigating an exemplary autonomous vehicle using tail
alignment.
[0192] FIG. 40 is a diagrammatic top view representation of an
exemplary vehicle including a system consistent with the disclosed
embodiments in which the vehicle navigates road junctions using two
or more landmarks.
[0193] FIG. 41 is a flowchart showing an exemplary process for
navigating an exemplary vehicle over road junctions using two or
more landmarks.
[0194] FIG. 42 is a diagrammatic top view representation of an
exemplary vehicle including a system consistent with the disclosed
embodiments in which the vehicle navigates using overlapping
maps.
[0195] FIGS. 43A, 43B, and 43C are flowcharts showing an exemplary
process for navigating an exemplary vehicle using overlapping
maps.
[0196] FIG. 44 shows an exemplary remote server in communication a
vehicle, consistent with the disclosed embodiments.
[0197] FIG. 45 shows a vehicle navigating along a multi-lane road,
consistent with disclosed embodiments.
[0198] FIG. 46 shows a vehicle navigating using target trajectories
along a multi-lane road, consistent with disclosed embodiments.
[0199] FIG. 47 shows an example of a road signature profile,
consistent with the disclosed embodiments.
[0200] FIG. 48 illustrates an exemplary environment, consistent
with the disclosed embodiments.
[0201] FIG. 49 is a flow chart showing an exemplary process for
sparse map autonomous vehicle navigation, consistent with the
disclosed embodiments
[0202] FIG. 50 illustrates an example environment for autonomous
navigation based on an expected landmark location consistent with
the disclosed embodiments.
[0203] FIG. 51 illustrates a configuration for autonomous
navigation consistent with the disclosed embodiments.
[0204] FIG. 52 illustrates another example environment for
autonomous navigation based on an expected landmark location
consistent with the disclosed embodiments.
[0205] FIG. 53 illustrates another example environment for
autonomous navigation based on an expected landmark location
consistent with the disclosed embodiments.
[0206] FIG. 54 is a flow chart showing an exemplary process for
autonomous navigation based on an expected landmark location
consistent with the disclosed embodiments.
[0207] FIG. 55 is a diagrammatic representation of exemplary
vehicle control systems consistent with the disclosed
embodiments.
[0208] FIG. 56 is diagrammatic top view representation of an
exemplary vehicle including a system consistent with the disclosed
embodiments in which the vehicle navigates using lane width
profiles or road width profiles.
[0209] FIG. 57 is graph showing an exemplary profile that may be
used by the vehicle control systems consistent with the disclosed
embodiments.
[0210] FIG. 58 is a diagrammatic top view representation of an
exemplary vehicle including a system consistent with the disclosed
embodiments in which the vehicle navigates using lengths or
spacings of road markings on a road segment.
[0211] FIG. 59 is a diagrammatic top view representation of an
exemplary vehicle including a system consistent with the disclosed
embodiments in which the vehicle navigates using information
regarding curvature of a road segment.
[0212] FIG. 60 is a flowchart showing an exemplary process for
navigating an exemplary vehicle using road signatures.
[0213] FIG. 61A is a diagrammatic side view representation of an
exemplary vehicle consistent with disclosed embodiments.
[0214] FIG. 61B is a diagrammatic side view representation of an
exemplary vehicle consistent with disclosed embodiments.
[0215] FIG. 62 is a diagrammatic top view representation of an
exemplary vehicle autonomously navigating on a road consistent with
disclosed embodiments.
[0216] FIG. 63 is a flowchart showing an exemplary process for
autonomously navigating a vehicle consistent with disclosed
embodiments.
[0217] FIG. 64 is a diagrammatic perspective view of an environment
captured by a forward facing image capture device on an exemplary
vehicle consistent with disclosed embodiments.
[0218] FIG. 65 is an exemplary image received from a forward facing
image capture device of a vehicle consistent with disclosed
embodiments.
[0219] FIG. 66 is a flowchart showing an exemplary process for
navigating a vehicle by determining a free space region in which
the vehicle can travel consistent with disclosed embodiments.
[0220] FIG. 67 is a diagrammatic top view representation of an
exemplary vehicle navigating on a road with snow covering at least
some lane markings and road edges consistent with disclosed
embodiments.
[0221] FIG. 68 is a flowchart showing an exemplary process for
navigating a vehicle on a road with snow covering at least some
lane markings and road edges consistent with disclosed
embodiments.
[0222] FIG. 69 is a diagrammatic top view representation of an
exemplary vehicle including a system for calibrating a speed of the
vehicle consistent with disclosed embodiments.
[0223] FIG. 70 is a flowchart showing an exemplary process for
calibrating a speed of a vehicle consistent with disclosed
embodiments.
[0224] FIG. 71 is another diagrammatic top view representation of
an exemplary vehicle including a system for calibrating a speed of
the vehicle consistent with disclosed embodiments.
[0225] FIG. 72 is a flowchart showing another exemplary process for
calibrating a speed of a vehicle consistent with disclosed
embodiments.
[0226] FIG. 73 is an illustration of a street view of an exemplary
road segment, consistent with disclosed embodiments.
[0227] FIG. 74 is an illustration of birds-eye view of an exemplary
road segment, consistent with disclosed embodiments.
[0228] FIG. 75 is a flowchart showing an exemplary process for
determining a lane assignment for a vehicle, consistent with
disclosed embodiments.
[0229] FIG. 76 is an illustration of a street view of an exemplary
road segment, consistent with disclosed embodiments.
[0230] FIG. 77A is an illustration of birds-eye view of an
exemplary road segment, consistent with disclosed embodiments.
[0231] FIG. 77B is an illustration of a street view of an exemplary
road segment consistent with disclosed embodiments.
[0232] FIG. 78 is a flowchart showing an exemplary process for
autonomously navigating a vehicle along a road segment, consistent
with disclosed embodiments.
[0233] FIG. 79A illustrates a plan view of a vehicle traveling on a
roadway approaching wintery and icy road conditions at a particular
location consistent with disclosed embodiments.
[0234] FIG. 79B illustrates a plan view of a vehicle traveling on a
roadway approaching a pedestrian consistent with disclosed
embodiments.
[0235] FIG. 79C illustrates a plan view of a vehicle traveling on a
roadway in close proximity to another vehicle consistent with
disclosed embodiments.
[0236] FIG. 79D illustrates a plan view of a vehicle traveling on a
roadway in a lane that is ending consistent with disclosed
embodiments.
[0237] FIG. 80 illustrates a diagrammatic side view representation
of an exemplary vehicle including the system consistent with the
disclosed embodiments.
[0238] FIG. 81 illustrates an example flowchart representing a
method for adaptive navigation of a vehicle based on user
intervention consistent with the disclosed embodiments.
[0239] FIG. 82A illustrates a plan view of a vehicle traveling on a
roadway with a parked car consistent with disclosed
embodiments.
[0240] FIG. 82B illustrates a plan view of a vehicle traveling on a
roadway in a lane that is ending consistent with the disclosed
embodiments.
[0241] FIG. 82C illustrates a plan view of a vehicle traveling on a
roadway approaching a pedestrian consistent with disclosed
embodiments.
[0242] FIG. 82D illustrates a plan view of a vehicle traveling on a
roadway approaching an area of construction consistent with the
disclosed embodiments.
[0243] FIG. 83 illustrates an example flowchart representing a
method for self-aware navigation of a vehicle consistent with the
disclosed embodiments.
[0244] FIG. 84A illustrates a plan view of a vehicle traveling on a
roadway with multiple parked cars consistent with the disclosed
embodiments.
[0245] FIG. 84B illustrates a plan view of a vehicle traveling on a
roadway with a car intervening directly in front of the vehicle
consistent with the disclosed embodiments.
[0246] FIG. 84C illustrates a plan view of a vehicle traveling on a
roadway with a temporary barrier directly in front of the vehicle
consistent with the disclosed embodiments.
[0247] FIG. 84D illustrates a plan view of a vehicle traveling on a
roadway with temporary roadwork directly in front of the vehicle
consistent with the disclosed embodiments.
[0248] FIG. 85A illustrates a plan view of a vehicle traveling on a
roadway with a pot hole directly in front of the vehicle consistent
with the disclosed embodiments.
[0249] FIG. 85B illustrates a plan view of a vehicle traveling on a
roadway with an animal and a pedestrian crossing in front of a
vehicle consistent with the disclosed embodiments.
[0250] FIG. 86 illustrates an example flowchart representing a
method for an adaptive road model manager consistent with disclosed
embodiments.
[0251] FIG. 87A illustrates a plan view of a single vehicle
traveling on an interstate roadway consistent with the disclosed
embodiments.
[0252] FIG. 87B illustrates a plan view of a group of vehicles
traveling on a city roadway consistent with the disclosed
embodiments.
[0253] FIG. 87C illustrates a plan view of a vehicle traveling on a
roadway within a particular rural geographic region consistent with
the disclosed embodiments.
[0254] FIG. 87D illustrates a plan view of a vehicle traveling on a
roadway with a lane shift consistent with the disclosed
embodiments.
[0255] FIG. 88 illustrates an example flowchart representing a
method for road model management based on selective feedback
consistent with the disclosed embodiments.
DETAILED DESCRIPTION
[0256] The following detailed description refers to the
accompanying drawings. Wherever possible, the same reference
numbers are used in the drawings and the following description to
refer to the same or similar parts. While several illustrative
embodiments are described herein, modifications, adaptations and
other implementations are possible. For example, substitutions,
additions or modifications may be made to the components
illustrated in the drawings, and the illustrative methods described
herein may be modified by substituting, reordering, removing, or
adding steps to the disclosed methods. Accordingly, the following
detailed description is not limited to the disclosed embodiments
and examples. Instead, the proper scope is defined by the appended
claims.
[0257] Autonomous Vehicle Overview
[0258] As used throughout this disclosure, the term "autonomous
vehicle" refers to a vehicle capable of implementing at least one
navigational change without driver input. A "navigational change"
refers to a change in one or more of steering, braking, or
acceleration of the vehicle. To be autonomous, a vehicle need not
be fully automatic (e.g., fully operation without a driver or
without driver input). Rather, an autonomous vehicle includes those
that can operate under driver control during certain time periods
and without driver control during other time periods. Autonomous
vehicles may also include vehicles that control only some aspects
of vehicle navigation, such as steering (e.g., to maintain a
vehicle course between vehicle lane constraints), but may leave
other aspects to the driver (e.g., braking). In some cases,
autonomous vehicles may handle some or all aspects of braking,
speed control, and/or steering of the vehicle.
[0259] As human drivers typically rely on visual cues and
observations order to control a vehicle, transportation
infrastructures are built accordingly, with lane markings, traffic
signs, and traffic lights are all designed to provide visual
information to drivers. In view of these design characteristics of
transportation infrastructures, an autonomous vehicle may include a
camera and a processing unit that analyzes visual information
captured from the environment of the vehicle. The visual
information may include, for example, components of the
transportation infrastructure (e.g., lane markings, traffic signs,
traffic lights, etc.) that are observable by drivers and other
obstacles (e.g., other vehicles, pedestrians, debris, etc.).
Additionally, an autonomous vehicle may also use stored
information, such as information that provides a model of the
vehicle's environment when navigating. For example, the vehicle may
use GPS data, sensor data (e.g., from an accelerometer, a speed
sensor, a suspension sensor, etc.), and/or other map data to
provide information related to its environment while it is
traveling, and the vehicle (as well as other vehicles) may use the
information to localize itself on the model.
[0260] In some embodiments in this disclosure, an autonomous
vehicle may use information obtained while navigating (e.g., from a
camera, GPS device, an accelerometer, a speed sensor, a suspension
sensor, etc.). In other embodiments, an autonomous vehicle may use
information obtained from past navigations by the vehicle (or by
other vehicles) while navigating. In yet other embodiments, an
autonomous vehicle may use a combination of information obtained
while navigating and information obtained from past navigations.
The following sections provide an overview of a system consistent
with the disclosed embodiments, following by an overview of a
forward-facing imaging system and methods consistent with the
system. The sections that follow disclose systems and methods for
constructing, using, and updating a sparse map for autonomous
vehicle navigation.
[0261] System Overview
[0262] FIG. 1 is a block diagram representation of a system 100
consistent with the exemplary disclosed embodiments. System 100 may
include various components depending on the requirements of a
particular implementation. In some embodiments, system 100 may
include a processing unit 110, an image acquisition unit 120, a
position sensor 130, one or more memory units 140, 150, a map
database 160, a user interface 170, and a wireless transceiver 172.
Processing unit 110 may include one or more processing devices. In
some embodiments, processing unit 110 may include an applications
processor 180, an image processor 190, or any other suitable
processing device. Similarly, image acquisition unit 120 may
include any number of image acquisition devices and components
depending on the requirements of a particular application. In some
embodiments, image acquisition unit 120 may include one or more
image capture devices (e.g., cameras), such as image capture device
122, image capture device 124, and image capture device 126. System
100 may also include a data interface 128 communicatively
connecting processing device 110 to image acquisition device 120.
For example, data interface 128 may include any wired and/or
wireless link or links for transmitting image data acquired by
image accusation device 120 to processing unit 110.
[0263] Wireless transceiver 172 may include one or more devices
configured to exchange transmissions over an air interface to one
or more networks (e.g., cellular, the Internet, etc.) by use of a
radio frequency, infrared frequency, magnetic field, or an electric
field. Wireless transceiver 172 may use any known standard to
transmit and/or receive data (e.g., Wi-Fi, Bluetooth.RTM.,
Bluetooth Smart, 802.15.4, ZigBee, etc.).
[0264] Both applications processor 180 and image processor 190 may
include various types of processing devices. For example, either or
both of applications processor 180 and image processor 190 may
include a microprocessor, preprocessors (such as an image
preprocessor), graphics processors, a central processing unit
(CPU), support circuits, digital signal processors, integrated
circuits, memory, or any other types of devices suitable for
running applications and for image processing and analysis. In some
embodiments, applications processor 180 and/or image processor 190
may include any type of single or multi-core processor, mobile
device microcontroller, central processing unit, etc. Various
processing devices may be used, including, for example, processors
available from manufacturers such as Intel.RTM., AMD.RTM., etc. and
may include various architectures (e.g., x86 processor, ARM.RTM.,
etc.).
[0265] In some embodiments, applications processor 180 and/or image
processor 190 may include any of the EyeQ series of processor chips
available from Mobileye.RTM.. These processor designs each include
multiple processing units with local memory and instruction sets.
Such processors may include video inputs for receiving image data
from multiple image sensors and may also include video out
capabilities. In one example, the EyeQ2.RTM. uses 90 nm-micron
technology operating at 332 Mhz. The EyeQ2.RTM. architecture
consists of two floating point, hyper-thread 32-bit RISC CPUs
(MIPS32.RTM. 34K.RTM. cores), five Vision Computing Engines (VCE),
three Vector Microcode Processors (VMP.RTM.), Denali 64-bit Mobile
DDR Controller, 128-bit internal Sonics Interconnect, dual 16-bit
Video input and 18-bit Video output controllers, 16 channels DMA
and several peripherals. The MIPS34K CPU manages the five VCEs,
three VMP.TM. and the DMA, the second MIPS34K CPU and the
multi-channel DMA as well as the other peripherals. The five VCEs,
three VMP.RTM. and the MIPS34K CPU can perform intensive vision
computations required by multi-function bundle applications. In
another example, the EyeQ3.RTM., which is a third generation
processor and is six times more powerful that the EyeQ2.RTM., may
be used in the disclosed embodiments.
[0266] Any of the processing devices disclosed herein may be
configured to perform certain functions. Configuring a processing
device, such as any of the described EyeQ processors or other
controller or microprocessor, to perform certain functions may
include programming of computer executable instructions and making
those instructions available to the processing device for execution
during operation of the processing device. In some embodiments,
configuring a processing device may include programming the
processing device directly with architectural instructions. In
other embodiments, configuring a processing device may include
storing executable instructions on a memory that is accessible to
the processing device during operation. For example, the processing
device may access the memory to obtain and execute the stored
instructions during operation.
[0267] While FIG. 1 depicts two separate processing devices
included in processing unit 110, more or fewer processing devices
may be used. For example, in some embodiments, a single processing
device may be used to accomplish the tasks of applications
processor 180 and image processor 190. In other embodiments, these
tasks may be performed by more than two processing devices.
Further, in some embodiments, system 100 may include one or more of
processing unit 110 without including other components, such as
image acquisition unit 120.
[0268] Processing unit 110 may comprise various types of devices.
For example, processing unit 110 may include various devices, such
as a controller, an image preprocessor, a central processing unit
(CPU), support circuits, digital signal processors, integrated
circuits, memory, or any other types of devices for image
processing and analysis. The image preprocessor may include a video
processor for capturing, digitizing and processing the imagery from
the image sensors. The CPU may comprise any number of
microcontrollers or microprocessors. The support circuits may be
any number of circuits generally well known in the art, including
cache, power supply, clock and input-output circuits. The memory
may store software that, when executed by the processor, controls
the operation of the system. The memory may include databases and
image processing software. The memory may comprise any number of
random access memories, read only memories, flash memories, disk
drives, optical storage, tape storage, removable storage and other
types of storage. In one instance, the memory may be separate from
the processing unit 110. In another instance, the memory may be
integrated into the processing unit 110.
[0269] Each memory 140, 150 may include software instructions that
when executed by a processor (e.g., applications processor 180
and/or image processor 190), may control operation of various
aspects of system 100. These memory units may include various
databases and image processing software. The memory units may
include random access memory, read only memory, flash memory, disk
drives, optical storage, tape storage, removable storage and/or any
other types of storage. In some embodiments, memory units 140, 150
may be separate from the applications processor 180 and/or image
processor 190. In other embodiments, these memory units may be
integrated into applications processor 180 and/or image processor
190.
[0270] Position sensor 130 may include any type of device suitable
for determining a location associated with at least one component
of system 100. In some embodiments, position sensor 130 may include
a GPS receiver. Such receivers can determine a user position and
velocity by processing signals broadcasted by global positioning
system satellites. Position information from position sensor 130
may be made available to applications processor 180 and/or image
processor 190.
[0271] In some embodiments, system 100 may include components such
as a speed sensor (e.g., a tachometer) for measuring a speed of
vehicle 200 and/or an accelerometer for measuring acceleration of
vehicle 200.
[0272] User interface 170 may include any device suitable for
providing information to or for receiving inputs from one or more
users of system 100. In some embodiments, user interface 170 may
include user input devices, including, for example, a touchscreen,
microphone, keyboard, pointer devices, track wheels, cameras,
knobs, buttons, etc. With such input devices, a user may be able to
provide information inputs or commands to system 100 by typing
instructions or information, providing voice commands, selecting
menu options on a screen using buttons, pointers, or eye-tracking
capabilities, or through any other suitable techniques for
communicating information to system 100.
[0273] User interface 170 may be equipped with one or more
processing devices configured to provide and receive information to
or from a user and process that information for use by, for
example, applications processor 180. In some embodiments, such
processing devices may execute instructions for recognizing and
tracking eye movements, receiving and interpreting voice commands,
recognizing and interpreting touches and/or gestures made on a
touchscreen, responding to keyboard entries or menu selections,
etc. In some embodiments, user interface 170 may include a display,
speaker, tactile device, and/or any other devices for providing
output information to a user.
[0274] Map database 160 may include any type of database for
storing map data useful to system 100. In some embodiments, map
database 160 may include data relating to the position, in a
reference coordinate system, of various items, including roads,
water features, geographic features, businesses, points of
interest, restaurants, gas stations, etc. Map database 160 may
store not only the locations of such items, but also descriptors
relating to those items, including, for example, names associated
with any of the stored features. In some embodiments, map database
160 may be physically located with other components of system 100.
Alternatively or additionally, map database 160 or a portion
thereof may be located remotely with respect to other components of
system 100 (e.g., processing unit 110). In such embodiments,
information from map database 160 may be downloaded over a wired or
wireless data connection to a network (e.g., over a cellular
network and/or the Internet, etc.).
[0275] Image capture devices 122, 124, and 126 may each include any
type of device suitable for capturing at least one image from an
environment. Moreover, any number of image capture devices may be
used to acquire images for input to the image processor. Some
embodiments may include only a single image capture device, while
other embodiments may include two, three, or even four or more
image capture devices. Image capture devices 122, 124, and 126 will
be further described with reference to FIGS. 2B-2E, below.
[0276] System 100, or various components thereof, may be
incorporated into various different platforms. In some embodiments,
system 100 may be included on a vehicle 200, as shown in FIG. 2A.
For example, vehicle 200 may be equipped with a processing unit 110
and any of the other components of system 100, as described above
relative to FIG. 1. While in some embodiments vehicle 200 may be
equipped with only a single image capture device (e.g., camera), in
other embodiments, such as those discussed in connection with FIGS.
2B-2E, multiple image capture devices may be used. For example,
either of image capture devices 122 and 124 of vehicle 200, as
shown in FIG. 2A, may be part of an ADAS (Advanced Driver
Assistance Systems) imaging set.
[0277] The image capture devices included on vehicle 200 as part of
the image acquisition unit 120 may be positioned at any suitable
location. In some embodiments, as shown in FIGS. 2A-2E and 3A-3C,
image capture device 122 may be located in the vicinity of the
rearview mirror. This position may provide a line of sight similar
to that of the driver of vehicle 200, which may aid in determining
what is and is not visible to the driver. Image capture device 122
may be positioned at any location near the rearview mirror, but
placing image capture device 122 on the driver side of the mirror
may further aid in obtaining images representative of the driver's
field of view and/or line of sight.
[0278] Other locations for the image capture devices of image
acquisition unit 120 may also be used. For example, image capture
device 124 may be located on or in a bumper of vehicle 200. Such a
location may be especially suitable for image capture devices
having a wide field of view. The line of sight of bumper-located
image capture devices can be different from that of the driver and,
therefore, the bumper image capture device and driver may not
always see the same objects. The image capture devices (e.g., image
capture devices 122, 124, and 126) may also be located in other
locations. For example, the image capture devices may be located on
or in one or both of the side mirrors of vehicle 200, on the roof
of vehicle 200, on the hood of vehicle 200, on the trunk of vehicle
200, on the sides of vehicle 200, mounted on, positioned behind, or
positioned in front of any of the windows of vehicle 200, and
mounted in or near light figures on the front and/or back of
vehicle 200, etc.
[0279] In addition to image capture devices, vehicle 200 may
include various other components of system 100. For example,
processing unit 110 may be included on vehicle 200 either
integrated with or separate from an engine control unit (ECU) of
the vehicle. Vehicle 200 may also be equipped with a position
sensor 130, such as a GPS receiver and may also include a map
database 160 and memory units 140 and 150.
[0280] As discussed earlier, wireless transceiver 172 may and/or
receive data over one or more networks (e.g., cellular networks,
the Internet, etc.). For example, wireless transceiver 172 may
upload data collected by system 100 to one or more servers, and
download data from the one or more servers. Via wireless
transceiver 172, system 100 may receive, for example, periodic or
on demand updates to data stored in map database 160, memory 140,
and/or memory 150. Similarly, wireless transceiver 172 may upload
any data (e.g., images captured by image acquisition unit 120, data
received by position sensor 130 or other sensors, vehicle control
systems, etc.) from by system 100 and/or any data processed by
processing unit 110 to the one or more servers.
[0281] System 100 may upload data to a server (e.g., to the cloud)
based on a privacy level setting. For example, system 100 may
implement privacy level settings to regulate or limit the types of
data (including metadata) sent to the server that may uniquely
identify a vehicle and or driver/owner of a vehicle. Such settings
may be set by user via, for example, wireless transceiver 172, be
initialized by factory default settings, or by data received by
wireless transceiver 172.
[0282] In some embodiments, system 100 may upload data according to
a "high" privacy level, and under setting a setting, system 100 may
transmit data (e.g., location information related to a route,
captured images, etc.) without any details about the specific
vehicle and/or driver/owner. For example, when uploading data
according to a "high" privacy setting, system 100 may not include a
vehicle identification number (VIN) or a name of a driver or owner
of the vehicle, and may instead of transmit data, such as captured
images and/or limited location information related to a route.
[0283] Other privacy levels are contemplated. For example, system
100 may transmit data to a server according to an "intermediate"
privacy level and include additional information not included under
a "high" privacy level, such as a make and/or model of a vehicle
and/or a vehicle type (e.g., a passenger vehicle, sport utility
vehicle, truck, etc.). In some embodiments, system 100 may upload
data according to a "low" privacy level. Under a "low" privacy
level setting, system 100 may upload data and include information
sufficient to uniquely identify a specific vehicle, owner/driver,
and/or a portion or entirely of a route traveled by the vehicle.
Such "low" privacy level data may include one or more of, for
example, a VIN, a driver/owner name, an origination point of a
vehicle prior to departure, an intended destination of the vehicle,
a make and/or model of the vehicle, a type of the vehicle, etc.
[0284] FIG. 2A is a diagrammatic side view representation of an
exemplary vehicle imaging system consistent with the disclosed
embodiments. FIG. 2B is a diagrammatic top view illustration of the
embodiment shown in FIG. 2A. As illustrated in FIG. 2B, the
disclosed embodiments may include a vehicle 200 including in its
body a system 100 with a first image capture device 122 positioned
in the vicinity of the rearview mirror and/or near the driver of
vehicle 200, a second image capture device 124 positioned on or in
a bumper region (e.g., one of bumper regions 210) of vehicle 200,
and a processing unit 110.
[0285] As illustrated in FIG. 2C, image capture devices 122 and 124
may both be positioned in the vicinity of the rearview mirror
and/or near the driver of vehicle 200. Additionally, while two
image capture devices 122 and 124 are shown in FIGS. 2B and 2C, it
should be understood that other embodiments may include more than
two image capture devices. For example, in the embodiments shown in
FIGS. 2D and 2E, first, second, and third image capture devices
122, 124, and 126, are included in the system 100 of vehicle
200.
[0286] As illustrated in FIG. 2D, image capture device 122 may be
positioned in the vicinity of the rearview mirror and/or near the
driver of vehicle 200, and image capture devices 124 and 126 may be
positioned on or in a bumper region (e.g., one of bumper regions
210) of vehicle 200. And as shown in FIG. 2E, image capture devices
122, 124, and 126 may be positioned in the vicinity of the rearview
mirror and/or near the driver seat of vehicle 200. The disclosed
embodiments are not limited to any particular number and
configuration of the image capture devices, and the image capture
devices may be positioned in any appropriate location within and/or
on vehicle 200.
[0287] It is to be understood that the disclosed embodiments are
not limited to vehicles and could be applied in other contexts. It
is also to be understood that disclosed embodiments are not limited
to a particular type of vehicle 200 and may be applicable to all
types of vehicles including automobiles, trucks, trailers, and
other types of vehicles.
[0288] The first image capture device 122 may include any suitable
type of image capture device. Image capture device 122 may include
an optical axis. In one instance, the image capture device 122 may
include an Aptina M9V024 WVGA sensor with a global shutter. In
other embodiments, image capture device 122 may provide a
resolution of 1280.times.960 pixels and may include a rolling
shutter. Image capture device 122 may include various optical
elements. In some embodiments one or more lenses may be included,
for example, to provide a desired focal length and field of view
for the image capture device. In some embodiments, image capture
device 122 may be associated with a 6 mm lens or a 12 mm lens. In
some embodiments, image capture device 122 may be configured to
capture images having a desired field-of-view (FOV) 202, as
illustrated in FIG. 2D. For example, image capture device 122 may
be configured to have a regular FOV, such as within a range of 40
degrees to 56 degrees, including a 46 degree FOV, 50 degree FOV, 52
degree FOV, or greater. Alternatively, image capture device 122 may
be configured to have a narrow FOV in the range of 23 to 40
degrees, such as a 28 degree FOV or 36 degree FOV. In addition,
image capture device 122 may be configured to have a wide FOV in
the range of 100 to 180 degrees. In some embodiments, image capture
device 122 may include a wide angle bumper camera or one with up to
a 180 degree FOV. In some embodiments, image capture device 122 may
be a 7.2 M pixel image capture device with an aspect ratio of about
2:1 (e.g., H.times.V=3800.times.1900 pixels) with about 100 degree
horizontal FOV. Such an image capture device may be used in place
of a three image capture device configuration. Due to significant
lens distortion, the vertical FOV of such an image capture device
may be significantly less than 50 degrees in implementations in
which the image capture device uses a radially symmetric lens. For
example, such a lens may not be radially symmetric which would
allow for a vertical FOV greater than 50 degrees with 100 degree
horizontal FOV.
[0289] The first image capture device 122 may acquire a plurality
of first images relative to a scene associated with the vehicle
200. Each of the plurality of first images may be acquired as a
series of image scan lines, which may be captured using a rolling
shutter. Each scan line may include a plurality of pixels.
[0290] The first image capture device 122 may have a scan rate
associated with acquisition of each of the first series of image
scan lines. The scan rate may refer to a rate at which an image
sensor can acquire image data associated with each pixel included
in a particular scan line.
[0291] Image capture devices 122, 124, and 126 may contain any
suitable type and number of image sensors, including CCD sensors or
CMOS sensors, for example. In one embodiment, a CMOS image sensor
may be employed along with a rolling shutter, such that each pixel
in a row is read one at a time, and scanning of the rows proceeds
on a row-by-row basis until an entire image frame has been
captured. In some embodiments, the rows may be captured
sequentially from top to bottom relative to the frame.
[0292] In some embodiments, one or more of the image capture
devices (e.g., image capture devices 122, 124, and 126) disclosed
herein may constitute a high resolution imager and may have a
resolution greater than 5 M pixel, 7 M pixel, 10 M pixel, or
greater.
[0293] The use of a rolling shutter may result in pixels in
different rows being exposed and captured at different times, which
may cause skew and other image artifacts in the captured image
frame. On the other hand, when the image capture device 122 is
configured to operate with a global or synchronous shutter, all of
the pixels may be exposed for the same amount of time and during a
common exposure period. As a result, the image data in a frame
collected from a system employing a global shutter represents a
snapshot of the entire FOV (such as FOV 202) at a particular time.
In contrast, in a rolling shutter application, each row in a frame
is exposed and data is capture at different times. Thus, moving
objects may appear distorted in an image capture device having a
rolling shutter. This phenomenon will be described in greater
detail below.
[0294] The second image capture device 124 and the third image
capturing device 126 may be any type of image capture device. Like
the first image capture device 122, each of image capture devices
124 and 126 may include an optical axis. In one embodiment, each of
image capture devices 124 and 126 may include an Aptina M9V024 WVGA
sensor with a global shutter. Alternatively, each of image capture
devices 124 and 126 may include a rolling shutter. Like image
capture device 122, image capture devices 124 and 126 may be
configured to include various lenses and optical elements. In some
embodiments, lenses associated with image capture devices 124 and
126 may provide FOVs (such as FOVs 204 and 206) that are the same
as, or narrower than, a FOV (such as FOV 202) associated with image
capture device 122. For example, image capture devices 124 and 126
may have FOVs of 40 degrees, 30 degrees, 26 degrees, 23 degrees, 20
degrees, or less.
[0295] Image capture devices 124 and 126 may acquire a plurality of
second and third images relative to a scene associated with the
vehicle 200. Each of the plurality of second and third images may
be acquired as a second and third series of image scan lines, which
may be captured using a rolling shutter. Each scan line or row may
have a plurality of pixels. Image capture devices 124 and 126 may
have second and third scan rates associated with acquisition of
each of image scan lines included in the second and third
series.
[0296] Each image capture device 122, 124, and 126 may be
positioned at any suitable position and orientation relative to
vehicle 200. The relative positioning of the image capture devices
122, 124, and 126 may be selected to aid in fusing together the
information acquired from the image capture devices. For example,
in some embodiments, a FOV (such as FOV 204) associated with image
capture device 124 may overlap partially or fully with a FOV (such
as FOV 202) associated with image capture device 122 and a FOV
(such as FOV 206) associated with image capture device 126.
[0297] Image capture devices 122, 124, and 126 may be located on
vehicle 200 at any suitable relative heights. In one instance,
there may be a height difference between the image capture devices
122, 124, and 126, which may provide sufficient parallax
information to enable stereo analysis. For example, as shown in
FIG. 2A, the two image capture devices 122 and 124 are at different
heights. There may also be a lateral displacement difference
between image capture devices 122, 124, and 126, giving additional
parallax information for stereo analysis by processing unit 110,
for example. The difference in the lateral displacement may be
denoted by d.sub.x, as shown in FIGS. 2C and 2D. In some
embodiments, fore or aft displacement (e.g., range displacement)
may exist between image capture devices 122, 124, and 126. For
example, image capture device 122 may be located 0.5 to 2 meters or
more behind image capture device 124 and/or image capture device
126. This type of displacement may enable one of the image capture
devices to cover potential blind spots of the other image capture
device(s).
[0298] Image capture devices 122 may have any suitable resolution
capability (e.g., number of pixels associated with the image
sensor), and the resolution of the image sensor(s) associated with
the image capture device 122 may be higher, lower, or the same as
the resolution of the image sensor(s) associated with image capture
devices 124 and 126. In some embodiments, the image sensor(s)
associated with image capture device 122 and/or image capture
devices 124 and 126 may have a resolution of 640.times.480,
1024.times.768, 1280.times.960, or any other suitable
resolution.
[0299] The frame rate (e.g., the rate at which an image capture
device acquires a set of pixel data of one image frame before
moving on to capture pixel data associated with the next image
frame) may be controllable. The frame rate associated with image
capture device 122 may be higher, lower, or the same as the frame
rate associated with image capture devices 124 and 126. The frame
rate associated with image capture devices 122, 124, and 126 may
depend on a variety of factors that may affect the timing of the
frame rate. For example, one or more of image capture devices 122,
124, and 126 may include a selectable pixel delay period imposed
before or after acquisition of image data associated with one or
more pixels of an image sensor in image capture device 122, 124,
and/or 126. Generally, image data corresponding to each pixel may
be acquired according to a clock rate for the device (e.g., one
pixel per clock cycle). Additionally, in embodiments including a
rolling shutter, one or more of image capture devices 122, 124, and
126 may include a selectable horizontal blanking period imposed
before or after acquisition of image data associated with a row of
pixels of an image sensor in image capture device 122, 124, and/or
126. Further, one or more of image capture devices 122, 124, and/or
126 may include a selectable vertical blanking period imposed
before or after acquisition of image data associated with an image
frame of image capture device 122, 124, and 126.
[0300] These timing controls may enable synchronization of frame
rates associated with image capture devices 122, 124, and 126, even
where the line scan rates of each are different. Additionally, as
will be discussed in greater detail below, these selectable timing
controls, among other factors (e.g., image sensor resolution,
maximum line scan rates, etc.) may enable synchronization of image
capture from an area where the FOV of image capture device 122
overlaps with one or more FOVs of image capture devices 124 and
126, even where the field of view of image capture device 122 is
different from the FOVs of image capture devices 124 and 126.
[0301] Frame rate timing in image capture device 122, 124, and 126
may depend on the resolution of the associated image sensors. For
example, assuming similar line scan rates for both devices, if one
device includes an image sensor having a resolution of
640.times.480 and another device includes an image sensor with a
resolution of 1280.times.960, then more time will be required to
acquire a frame of image data from the sensor having the higher
resolution.
[0302] Another factor that may affect the timing of image data
acquisition in image capture devices 122, 124, and 126 is the
maximum line scan rate. For example, acquisition of a row of image
data from an image sensor included in image capture device 122,
124, and 126 will require some minimum amount of time. Assuming no
pixel delay periods are added, this minimum amount of time for
acquisition of a row of image data will be related to the maximum
line scan rate for a particular device. Devices that offer higher
maximum line scan rates have the potential to provide higher frame
rates than devices with lower maximum line scan rates. In some
embodiments, one or more of image capture devices 124 and 126 may
have a maximum line scan rate that is higher than a maximum line
scan rate associated with image capture device 122. In some
embodiments, the maximum line scan rate of image capture device 124
and/or 126 may be 1.25, 1.5, 1.75, or 2 times or more than a
maximum line scan rate of image capture device 122.
[0303] In another embodiment, image capture devices 122, 124, and
126 may have the same maximum line scan rate, but image capture
device 122 may be operated at a scan rate less than or equal to its
maximum scan rate. The system may be configured such that one or
more of image capture devices 124 and 126 operate at a line scan
rate that is equal to the line scan rate of image capture device
122. In other instances, the system may be configured such that the
line scan rate of image capture device 124 and/or image capture
device 126 may be 1.25, 1.5, 1.75, or 2 times or more than the line
scan rate of image capture device 122.
[0304] In some embodiments, image capture devices 122, 124, and 126
may be asymmetric. That is, they may include cameras having
different fields of view (FOV) and focal lengths. The fields of
view of image capture devices 122, 124, and 126 may include any
desired area relative to an environment of vehicle 200, for
example. In some embodiments, one or more of image capture devices
122, 124, and 126 may be configured to acquire image data from an
environment in front of vehicle 200, behind vehicle 200, to the
sides of vehicle 200, or combinations thereof.
[0305] Further, the focal length associated with each image capture
device 122, 124, and/or 126 may be selectable (e.g., by inclusion
of appropriate lenses etc.) such that each device acquires images
of objects at a desired distance range relative to vehicle 200. For
example, in some embodiments image capture devices 122, 124, and
126 may acquire images of close-up objects within a few meters from
the vehicle. Image capture devices 122, 124, and 126 may also be
configured to acquire images of objects at ranges more distant from
the vehicle (e.g., 25 m, 50 m, 100 m, 150 m, or more). Further, the
focal lengths of image capture devices 122, 124, and 126 may be
selected such that one image capture device (e.g., image capture
device 122) can acquire images of objects relatively close to the
vehicle (e.g., within 10 m or within 20 m) while the other image
capture devices (e.g., image capture devices 124 and 126) can
acquire images of more distant objects (e.g., greater than 20 m, 50
m, 100 m, 150 m, etc.) from vehicle 200.
[0306] According to some embodiments, the FOV of one or more image
capture devices 122, 124, and 126 may have a wide angle. For
example, it may be advantageous to have a FOV of 140 degrees,
especially for image capture devices 122, 124, and 126 that may be
used to capture images of the area in the vicinity of vehicle 200.
For example, image capture device 122 may be used to capture images
of the area to the right or left of vehicle 200 and, in such
embodiments, it may be desirable for image capture device 122 to
have a wide FOV (e.g., at least 140 degrees).
[0307] The field of view associated with each of image capture
devices 122, 124, and 126 may depend on the respective focal
lengths. For example, as the focal length increases, the
corresponding field of view decreases.
[0308] Image capture devices 122, 124, and 126 may be configured to
have any suitable fields of view. In one particular example, image
capture device 122 may have a horizontal FOV of 46 degrees, image
capture device 124 may have a horizontal FOV of 23 degrees, and
image capture device 126 may have a horizontal FOV in between 23
and 46 degrees. In another instance, image capture device 122 may
have a horizontal FOV of 52 degrees, image capture device 124 may
have a horizontal FOV of 26 degrees, and image capture device 126
may have a horizontal FOV in between 26 and 52 degrees. In some
embodiments, a ratio of the FOV of image capture device 122 to the
FOVs of image capture device 124 and/or image capture device 126
may vary from 1.5 to 2.0. In other embodiments, this ratio may vary
between 1.25 and 2.25.
[0309] System 100 may be configured so that a field of view of
image capture device 122 overlaps, at least partially or fully,
with a field of view of image capture device 124 and/or image
capture device 126. In some embodiments, system 100 may be
configured such that the fields of view of image capture devices
124 and 126, for example, fall within (e.g., are narrower than) and
share a common center with the field of view of image capture
device 122. In other embodiments, the image capture devices 122,
124, and 126 may capture adjacent FOVs or may have partial overlap
in their FOVs. In some embodiments, the fields of view of image
capture devices 122, 124, and 126 may be aligned such that a center
of the narrower FOV image capture devices 124 and/or 126 may be
located in a lower half of the field of view of the wider FOV
device 122.
[0310] FIG. 2F is a diagrammatic representation of exemplary
vehicle control systems, consistent with the disclosed embodiments.
As indicated in FIG. 2F, vehicle 200 may include throttling system
220, braking system 230, and steering system 240. System 100 may
provide inputs (e.g., control signals) to one or more of throttling
system 220, braking system 230, and steering system 240 over one or
more data links (e.g., any wired and/or wireless link or links for
transmitting data). For example, based on analysis of images
acquired by image capture devices 122, 124, and/or 126, system 100
may provide control signals to one or more of throttling system
220, braking system 230, and steering system 240 to navigate
vehicle 200 (e.g., by causing an acceleration, a turn, a lane
shift, etc.). Further, system 100 may receive inputs from one or
more of throttling system 220, braking system 230, and steering
system 24 indicating operating conditions of vehicle 200 (e.g.,
speed, whether vehicle 200 is braking and/or turning, etc.).
Further details are provided in connection with FIGS. 4-7,
below.
[0311] As shown in FIG. 3A, vehicle 200 may also include a user
interface 170 for interacting with a driver or a passenger of
vehicle 200. For example, user interface 170 in a vehicle
application may include a touch screen 320, knobs 330, buttons 340,
and a microphone 350. A driver or passenger of vehicle 200 may also
use handles (e.g., located on or near the steering column of
vehicle 200 including, for example, turn signal handles), buttons
(e.g., located on the steering wheel of vehicle 200), and the like,
to interact with system 100. In some embodiments, microphone 350
may be positioned adjacent to a rearview mirror 310. Similarly, in
some embodiments, image capture device 122 may be located near
rearview mirror 310. In some embodiments, user interface 170 may
also include one or more speakers 360 (e.g., speakers of a vehicle
audio system). For example, system 100 may provide various
notifications (e.g., alerts) via speakers 360.
[0312] FIGS. 3B-3D are illustrations of an exemplary camera mount
370 configured to be positioned behind a rearview mirror (e.g.,
rearview mirror 310) and against a vehicle windshield, consistent
with disclosed embodiments. As shown in FIG. 3B, camera mount 370
may include image capture devices 122, 124, and 126. Image capture
devices 124 and 126 may be positioned behind a glare shield 380,
which may be flush against the vehicle windshield and include a
composition of film and/or anti-reflective materials. For example,
glare shield 380 may be positioned such that it aligns against a
vehicle windshield having a matching slope. In some embodiments,
each of image capture devices 122, 124, and 126 may be positioned
behind glare shield 380, as depicted, for example, in FIG. 3D. The
disclosed embodiments are not limited to any particular
configuration of image capture devices 122, 124, and 126, camera
mount 370, and glare shield 380. FIG. 3C is an illustration of
camera mount 370 shown in FIG. 3B from a front perspective.
[0313] As will be appreciated by a person skilled in the art having
the benefit of this disclosure, numerous variations and/or
modifications may be made to the foregoing disclosed embodiments.
For example, not all components are essential for the operation of
system 100. Further, any component may be located in any
appropriate part of system 100 and the components may be rearranged
into a variety of configurations while providing the functionality
of the disclosed embodiments. Therefore, the foregoing
configurations are examples and, regardless of the configurations
discussed above, system 100 can provide a wide range of
functionality to analyze the surroundings of vehicle 200 and
navigate vehicle 200 in response to the analysis.
[0314] As discussed below in further detail and consistent with
various disclosed embodiments, system 100 may provide a variety of
features related to autonomous driving and/or driver assist
technology. For example, system 100 may analyze image data,
position data (e.g., GPS location information), map data, speed
data, and/or data from sensors included in vehicle 200. System 100
may collect the data for analysis from, for example, image
acquisition unit 120, position sensor 130, and other sensors.
Further, system 100 may analyze the collected data to determine
whether or not vehicle 200 should take a certain action, and then
automatically take the determined action without human
intervention. For example, when vehicle 200 navigates without human
intervention, system 100 may automatically control the braking,
acceleration, and/or steering of vehicle 200 (e.g., by sending
control signals to one or more of throttling system 220, braking
system 230, and steering system 240). Further, system 100 may
analyze the collected data and issue warnings and/or alerts to
vehicle occupants based on the analysis of the collected data.
Additional details regarding the various embodiments that are
provided by system 100 are provided below.
[0315] Forward-Facing Multi-Imaging System
[0316] As discussed above, system 100 may provide drive assist
functionality that uses a multi-camera system. The multi-camera
system may use one or more cameras facing in the forward direction
of a vehicle. In other embodiments, the multi-camera system may
include one or more cameras facing to the side of a vehicle or to
the rear of the vehicle. In one embodiment, for example, system 100
may use a two-camera imaging system, where a first camera and a
second camera (e.g., image capture devices 122 and 124) may be
positioned at the front and/or the sides of a vehicle (e.g.,
vehicle 200). The first camera may have a field of view that is
greater than, less than, or partially overlapping with, the field
of view of the second camera. In addition, the first camera may be
connected to a first image processor to perform monocular image
analysis of images provided by the first camera, and the second
camera may be connected to a second image processor to perform
monocular image analysis of images provided by the second camera.
The outputs (e.g., processed information) of the first and second
image processors may be combined. In some embodiments, the second
image processor may receive images from both the first camera and
second camera to perform stereo analysis. In another embodiment,
system 100 may use a three-camera imaging system where each of the
cameras has a different field of view. Such a system may,
therefore, make decisions based on information derived from objects
located at varying distances both forward and to the sides of the
vehicle. References to monocular image analysis may refer to
instances where image analysis is performed based on images
captured from a single point of view (e.g., from a single camera).
Stereo image analysis may refer to instances where image analysis
is performed based on two or more images captured with one or more
variations of an image capture parameter. For example, captured
images suitable for performing stereo image analysis may include
images captured: from two or more different positions, from
different fields of view, using different focal lengths, along with
parallax information, etc.
[0317] For example, in one embodiment, system 100 may implement a
three camera configuration using image capture devices 122-126. In
such a configuration, image capture device 122 may provide a narrow
field of view (e.g., 34 degrees, or other values selected from a
range of about 20 to 45 degrees, etc.), image capture device 124
may provide a wide field of view (e.g., 150 degrees or other values
selected from a range of about 100 to about 180 degrees), and image
capture device 126 may provide an intermediate field of view (e.g.,
46 degrees or other values selected from a range of about 35 to
about 60 degrees). In some embodiments, image capture device 126
may act as a main or primary camera. Image capture devices 122-126
may be positioned behind rearview mirror 310 and positioned
substantially side-by-side (e.g., 6 cm apart). Further, in some
embodiments, as discussed above, one or more of image capture
devices 122-126 may be mounted behind glare shield 380 that is
flush with the windshield of vehicle 200. Such shielding may act to
minimize the impact of any reflections from inside the car on image
capture devices 122-126.
[0318] In another embodiment, as discussed above in connection with
FIGS. 3B and 3C, the wide field of view camera (e.g., image capture
device 124 in the above example) may be mounted lower than the
narrow and main field of view cameras (e.g., image devices 122 and
126 in the above example). This configuration may provide a free
line of sight from the wide field of view camera. To reduce
reflections, the cameras may be mounted close to the windshield of
vehicle 200, and may include polarizers on the cameras to damp
reflected light.
[0319] A three camera system may provide certain performance
characteristics. For example, some embodiments may include an
ability to validate the detection of objects by one camera based on
detection results from another camera. In the three camera
configuration discussed above, processing unit 110 may include, for
example, three processing devices (e.g., three EyeQ series of
processor chips, as discussed above), with each processing device
dedicated to processing images captured by one or more of image
capture devices 122-126.
[0320] In a three camera system, a first processing device may
receive images from both the main camera and the narrow field of
view camera, and perform vision processing of the narrow FOV camera
to, for example, detect other vehicles, pedestrians, lane marks,
traffic signs, traffic lights, and other road objects. Further, the
first processing device may calculate a disparity of pixels between
the images from the main camera and the narrow camera and create a
3D reconstruction of the environment of vehicle 200. The first
processing device may then combine the 3D reconstruction with 3D
map data or with 3D information calculated based on information
from another camera.
[0321] The second processing device may receive images from main
camera and perform vision processing to detect other vehicles,
pedestrians, lane marks, traffic signs, traffic lights, and other
road objects. Additionally, the second processing device may
calculate a camera displacement and, based on the displacement,
calculate a disparity of pixels between successive images and
create a 3D reconstruction of the scene (e.g., a structure from
motion). The second processing device may send the structure from
motion based 3D reconstruction to the first processing device to be
combined with the stereo 3D images.
[0322] The third processing device may receive images from the wide
FOV camera and process the images to detect vehicles, pedestrians,
lane marks, traffic signs, traffic lights, and other road objects.
The third processing device may further execute additional
processing instructions to analyze images to identify objects
moving in the image, such as vehicles changing lanes, pedestrians,
etc.
[0323] In some embodiments, having streams of image-based
information captured and processed independently may provide an
opportunity for providing redundancy in the system. Such redundancy
may include, for example, using a first image capture device and
the images processed from that device to validate and/or supplement
information obtained by capturing and processing image information
from at least a second image capture device.
[0324] In some embodiments, system 100 may use two image capture
devices (e.g., image capture devices 122 and 124) in providing
navigation assistance for vehicle 200 and use a third image capture
device (e.g., image capture device 126) to provide redundancy and
validate the analysis of data received from the other two image
capture devices. For example, in such a configuration, image
capture devices 122 and 124 may provide images for stereo analysis
by system 100 for navigating vehicle 200, while image capture
device 126 may provide images for monocular analysis by system 100
to provide redundancy and validation of information obtained based
on images captured from image capture device 122 and/or image
capture device 124. That is, image capture device 126 (and a
corresponding processing device) may be considered to provide a
redundant sub-system for providing a check on the analysis derived
from image capture devices 122 and 124 (e.g., to provide an
automatic emergency braking (AEB) system).
[0325] One of skill in the art will recognize that the above camera
configurations, camera placements, number of cameras, camera
locations, etc., are examples only. These components and others
described relative to the overall system may be assembled and used
in a variety of different configurations without departing from the
scope of the disclosed embodiments. Further details regarding usage
of a multi-camera system to provide driver assist and/or autonomous
vehicle functionality follow below.
[0326] FIG. 4 is an exemplary functional block diagram of memory
140 and/or 150, which may be stored/programmed with instructions
for performing one or more operations consistent with the disclosed
embodiments. Although the following refers to memory 140, one of
skill in the art will recognize that instructions may be stored in
memory 140 and/or 150.
[0327] As shown in FIG. 4, memory 140 may store a monocular image
analysis module 402, a stereo image analysis module 404, a velocity
and acceleration module 406, and a navigational response module
408. The disclosed embodiments are not limited to any particular
configuration of memory 140. Further, application processor 180
and/or image processor 190 may execute the instructions stored in
any of modules 402-408 included in memory 140. One of skill in the
art will understand that references in the following discussions to
processing unit 110 may refer to application processor 180 and
image processor 190 individually or collectively. Accordingly,
steps of any of the following processes may be performed by one or
more processing devices.
[0328] In one embodiment, monocular image analysis module 402 may
store instructions (such as computer vision software) which, when
executed by processing unit 110, performs monocular image analysis
of a set of images acquired by one of image capture devices 122,
124, and 126. In some embodiments, processing unit 110 may combine
information from a set of images with additional sensory
information (e.g., information from radar) to perform the monocular
image analysis. As described in connection with FIGS. 5A-5D below,
monocular image analysis module 402 may include instructions for
detecting a set of features within the set of images, such as lane
markings, vehicles, pedestrians, road signs, highway exit ramps,
traffic lights, hazardous objects, and any other feature associated
with an environment of a vehicle. Based on the analysis, system 100
(e.g., via processing unit 110) may cause one or more navigational
responses in vehicle 200, such as a turn, a lane shift, a change in
acceleration, and the like, as discussed below in connection with
navigational response module 408.
[0329] In one embodiment, stereo image analysis module 404 may
store instructions (such as computer vision software) which, when
executed by processing unit 110, performs stereo image analysis of
first and second sets of images acquired by a combination of image
capture devices selected from any of image capture devices 122,
124, and 126. In some embodiments, processing unit 110 may combine
information from the first and second sets of images with
additional sensory information (e.g., information from radar) to
perform the stereo image analysis. For example, stereo image
analysis module 404 may include instructions for performing stereo
image analysis based on a first set of images acquired by image
capture device 124 and a second set of images acquired by image
capture device 126. As described in connection with FIG. 6 below,
stereo image analysis module 404 may include instructions for
detecting a set of features within the first and second sets of
images, such as lane markings, vehicles, pedestrians, road signs,
highway exit ramps, traffic lights, hazardous objects, and the
like. Based on the analysis, processing unit 110 may cause one or
more navigational responses in vehicle 200, such as a turn, a lane
shift, a change in acceleration, and the like, as discussed below
in connection with navigational response module 408.
[0330] In one embodiment, velocity and acceleration module 406 may
store software configured to analyze data received from one or more
computing and electromechanical devices in vehicle 200 that are
configured to cause a change in velocity and/or acceleration of
vehicle 200. For example, processing unit 110 may execute
instructions associated with velocity and acceleration module 406
to calculate a target speed for vehicle 200 based on data derived
from execution of monocular image analysis module 402 and/or stereo
image analysis module 404. Such data may include, for example, a
target position, velocity, and/or acceleration, the position and/or
speed of vehicle 200 relative to a nearby vehicle, pedestrian, or
road object, position information for vehicle 200 relative to lane
markings of the road, and the like. In addition, processing unit
110 may calculate a target speed for vehicle 200 based on sensory
input (e.g., information from radar) and input from other systems
of vehicle 200, such as throttling system 220, braking system 230,
and/or steering system 240 of vehicle 200. Based on the calculated
target speed, processing unit 110 may transmit electronic signals
to throttling system 220, braking system 230, and/or steering
system 240 of vehicle 200 to trigger a change in velocity and/or
acceleration by, for example, physically depressing the brake or
easing up off the accelerator of vehicle 200.
[0331] In one embodiment, navigational response module 408 may
store software executable by processing unit 110 to determine a
desired navigational response based on data derived from execution
of monocular image analysis module 402 and/or stereo image analysis
module 404. Such data may include position and speed information
associated with nearby vehicles, pedestrians, and road objects,
target position information for vehicle 200, and the like.
Additionally, in some embodiments, the navigational response may be
based (partially or fully) on map data, a predetermined position of
vehicle 200, and/or a relative velocity or a relative acceleration
between vehicle 200 and one or more objects detected from execution
of monocular image analysis module 402 and/or stereo image analysis
module 404. Navigational response module 408 may also determine a
desired navigational response based on sensory input (e.g.,
information from radar) and inputs from other systems of vehicle
200, such as throttling system 220, braking system 230, and
steering system 240 of vehicle 200. Based on the desired
navigational response, processing unit 110 may transmit electronic
signals to throttling system 220, braking system 230, and steering
system 240 of vehicle 200 to trigger a desired navigational
response by, for example, turning the steering wheel of vehicle 200
to achieve a rotation of a predetermined angle. In some
embodiments, processing unit 110 may use the output of navigational
response module 408 (e.g., the desired navigational response) as an
input to execution of velocity and acceleration module 406 for
calculating a change in speed of vehicle 200.
[0332] FIG. 5A is a flowchart showing an exemplary process 500A for
causing one or more navigational responses based on monocular image
analysis, consistent with disclosed embodiments. At step 510,
processing unit 110 may receive a plurality of images via data
interface 128 between processing unit 110 and image acquisition
unit 120. For instance, a camera included in image acquisition unit
120 (such as image capture device 122 having field of view 202) may
capture a plurality of images of an area forward of vehicle 200 (or
to the sides or rear of a vehicle, for example) and transmit them
over a data connection (e.g., digital, wired, USB, wireless,
Bluetooth, etc.) to processing unit 110. Processing unit 110 may
execute monocular image analysis module 402 to analyze the
plurality of images at step 520, as described in further detail in
connection with FIGS. 5B-5D below. By performing the analysis,
processing unit 110 may detect a set of features within the set of
images, such as lane markings, vehicles, pedestrians, road signs,
highway exit ramps, traffic lights, and the like.
[0333] Processing unit 110 may also execute monocular image
analysis module 402 to detect various road hazards at step 520,
such as, for example, parts of a truck tire, fallen road signs,
loose cargo, small animals, and the like. Road hazards may vary in
structure, shape, size, and color, which may make detection of such
hazards more challenging. In some embodiments, processing unit 110
may execute monocular image analysis module 402 to perform
multi-frame analysis on the plurality of images to detect road
hazards. For example, processing unit 110 may estimate camera
motion between consecutive image frames and calculate the
disparities in pixels between the frames to construct a 3D-map of
the road. Processing unit 110 may then use the 3D-map to detect the
road surface, as well as hazards existing above the road
surface.
[0334] At step 530, processing unit 110 may execute navigational
response module 408 to cause one or more navigational responses in
vehicle 200 based on the analysis performed at step 520 and the
techniques as described above in connection with FIG. 4.
Navigational responses may include, for example, a turn, a lane
shift, a change in acceleration, and the like. In some embodiments,
processing unit 110 may use data derived from execution of velocity
and acceleration module 406 to cause the one or more navigational
responses. Additionally, multiple navigational responses may occur
simultaneously, in sequence, or any combination thereof. For
instance, processing unit 110 may cause vehicle 200 to shift one
lane over and then accelerate by, for example, sequentially
transmitting control signals to steering system 240 and throttling
system 220 of vehicle 200. Alternatively, processing unit 110 may
cause vehicle 200 to brake while at the same time shifting lanes
by, for example, simultaneously transmitting control signals to
braking system 230 and steering system 240 of vehicle 200.
[0335] FIG. 5B is a flowchart showing an exemplary process 500B for
detecting one or more vehicles and/or pedestrians in a set of
images, consistent with disclosed embodiments. Processing unit 110
may execute monocular image analysis module 402 to implement
process 500B. At step 540, processing unit 110 may determine a set
of candidate objects representing possible vehicles and/or
pedestrians. For example, processing unit 110 may scan one or more
images, compare the images to one or more predetermined patterns,
and identify within each image possible locations that may contain
objects of interest (e.g., vehicles, pedestrians, or portions
thereof). The predetermined patterns may be designed in such a way
to achieve a high rate of "false hits" and a low rate of "misses."
For example, processing unit 110 may use a low threshold of
similarity to predetermined patterns for identifying candidate
objects as possible vehicles or pedestrians. Doing so may allow
processing unit 110 to reduce the probability of missing (e.g., not
identifying) a candidate object representing a vehicle or
pedestrian.
[0336] At step 542, processing unit 110 may filter the set of
candidate objects to exclude certain candidates (e.g., irrelevant
or less relevant objects) based on classification criteria. Such
criteria may be derived from various properties associated with
object types stored in a database (e.g., a database stored in
memory 140). Properties may include object shape, dimensions,
texture, position (e.g., relative to vehicle 200), and the like.
Thus, processing unit 110 may use one or more sets of criteria to
reject false candidates from the set of candidate objects.
[0337] At step 544, processing unit 110 may analyze multiple frames
of images to determine whether objects in the set of candidate
objects represent vehicles and/or pedestrians. For example,
processing unit 110 may track a detected candidate object across
consecutive frames and accumulate frame-by-frame data associated
with the detected object (e.g., size, position relative to vehicle
200, etc.). Additionally, processing unit 110 may estimate
parameters for the detected object and compare the object's
frame-by-frame position data to a predicted position.
[0338] At step 546, processing unit 110 may construct a set of
measurements for the detected objects. Such measurements may
include, for example, position, velocity, and acceleration values
(relative to vehicle 200) associated with the detected objects. In
some embodiments, processing unit 110 may construct the
measurements based on estimation techniques using a series of
time-based observations such as Kalman filters or linear quadratic
estimation (LQE), and/or based on available modeling data for
different object types (e.g., cars, trucks, pedestrians, bicycles,
road signs, etc.). The Kalman filters may be based on a measurement
of an object's scale, where the scale measurement is proportional
to a time to collision (e.g., the amount of time for vehicle 200 to
reach the object). Thus, by performing steps 540-546, processing
unit 110 may identify vehicles and pedestrians appearing within the
set of captured images and derive information (e.g., position,
speed, size) associated with the vehicles and pedestrians. Based on
the identification and the derived information, processing unit 110
may cause one or more navigational responses in vehicle 200, as
described in connection with FIG. 5A, above.
[0339] At step 548, processing unit 110 may perform an optical flow
analysis of one or more images to reduce the probabilities of
detecting a "false hit" and missing a candidate object that
represents a vehicle or pedestrian. The optical flow analysis may
refer to, for example, analyzing motion patterns relative to
vehicle 200 in the one or more images associated with other
vehicles and pedestrians, and that are distinct from road surface
motion. Processing unit 110 may calculate the motion of candidate
objects by observing the different positions of the objects across
multiple image frames, which are captured at different times.
Processing unit 110 may use the position and time values as inputs
into mathematical models for calculating the motion of the
candidate objects. Thus, optical flow analysis may provide another
method of detecting vehicles and pedestrians that are nearby
vehicle 200. Processing unit 110 may perform optical flow analysis
in combination with steps 540-546 to provide redundancy for
detecting vehicles and pedestrians and increase the reliability of
system 100.
[0340] FIG. 5C is a flowchart showing an exemplary process 500C for
detecting road marks and/or lane geometry information in a set of
images, consistent with disclosed embodiments. Processing unit 110
may execute monocular image analysis module 402 to implement
process 500C. At step 550, processing unit 110 may detect a set of
objects by scanning one or more images. To detect segments of lane
markings, lane geometry information, and other pertinent road
marks, processing unit 110 may filter the set of objects to exclude
those determined to be irrelevant (e.g., minor potholes, small
rocks, etc.). At step 552, processing unit 110 may group together
the segments detected in step 550 belonging to the same road mark
or lane mark. Based on the grouping, processing unit 110 may
develop a model to represent the detected segments, such as a
mathematical model.
[0341] At step 554, processing unit 110 may construct a set of
measurements associated with the detected segments. In some
embodiments, processing unit 110 may create a projection of the
detected segments from the image plane onto the real-world plane.
The projection may be characterized using a 3rd-degree polynomial
having coefficients corresponding to physical properties such as
the position, slope, curvature, and curvature derivative of the
detected road. In generating the projection, processing unit 110
may take into account changes in the road surface, as well as pitch
and roll rates associated with vehicle 200. In addition, processing
unit 110 may model the road elevation by analyzing position and
motion cues present on the road surface. Further, processing unit
110 may estimate the pitch and roll rates associated with vehicle
200 by tracking a set of feature points in the one or more
images.
[0342] At step 556, processing unit 110 may perform multi-frame
analysis by, for example, tracking the detected segments across
consecutive image frames and accumulating frame-by-frame data
associated with detected segments. As processing unit 110 performs
multi-frame analysis, the set of measurements constructed at step
554 may become more reliable and associated with an increasingly
higher confidence level. Thus, by performing steps 550-556,
processing unit 110 may identify road marks appearing within the
set of captured images and derive lane geometry information. Based
on the identification and the derived information, processing unit
110 may cause one or more navigational responses in vehicle 200, as
described in connection with FIG. 5A, above.
[0343] At step 558, processing unit 110 may consider additional
sources of information to further develop a safety model for
vehicle 200 in the context of its surroundings. Processing unit 110
may use the safety model to define a context in which system 100
may execute autonomous control of vehicle 200 in a safe manner. To
develop the safety model, in some embodiments, processing unit 110
may consider the position and motion of other vehicles, the
detected road edges and barriers, and/or general road shape
descriptions extracted from map data (such as data from map
database 160). By considering additional sources of information,
processing unit 110 may provide redundancy for detecting road marks
and lane geometry and increase the reliability of system 100.
[0344] FIG. 5D is a flowchart showing an exemplary process 500D for
detecting traffic lights in a set of images, consistent with
disclosed embodiments. Processing unit 110 may execute monocular
image analysis module 402 to implement process 500D. At step 560,
processing unit 110 may scan the set of images and identify objects
appearing at locations in the images likely to contain traffic
lights. For example, processing unit 110 may filter the identified
objects to construct a set of candidate objects, excluding those
objects unlikely to correspond to traffic lights. The filtering may
be done based on various properties associated with traffic lights,
such as shape, dimensions, texture, position (e.g., relative to
vehicle 200), and the like. Such properties may be based on
multiple examples of traffic lights and traffic control signals and
stored in a database. In some embodiments, processing unit 110 may
perform multi-frame analysis on the set of candidate objects
reflecting possible traffic lights. For example, processing unit
110 may track the candidate objects across consecutive image
frames, estimate the real-world position of the candidate objects,
and filter out those objects that are moving (which are unlikely to
be traffic lights). In some embodiments, processing unit 110 may
perform color analysis on the candidate objects and identify the
relative position of the detected colors appearing inside possible
traffic lights.
[0345] At step 562, processing unit 110 may analyze the geometry of
a junction. The analysis may be based on any combination of: (i)
the number of lanes detected on either side of vehicle 200, (ii)
markings (such as arrow marks) detected on the road, and (iii)
descriptions of the junction extracted from map data (such as data
from map database 160). Processing unit 110 may conduct the
analysis using information derived from execution of monocular
analysis module 402. In addition, Processing unit 110 may determine
a correspondence between the traffic lights detected at step 560
and the lanes appearing near vehicle 200.
[0346] As vehicle 200 approaches the junction, at step 564,
processing unit 110 may update the confidence level associated with
the analyzed junction geometry and the detected traffic lights. For
instance, the number of traffic lights estimated to appear at the
junction as compared with the number actually appearing at the
junction may impact the confidence level. Thus, based on the
confidence level, processing unit 110 may delegate control to the
driver of vehicle 200 in order to improve safety conditions. By
performing steps 560-564, processing unit 110 may identify traffic
lights appearing within the set of captured images and analyze
junction geometry information. Based on the identification and the
analysis, processing unit 110 may cause one or more navigational
responses in vehicle 200, as described in connection with FIG. 5A,
above.
[0347] FIG. 5E is a flowchart showing an exemplary process 500E for
causing one or more navigational responses in vehicle 200 based on
a vehicle path, consistent with the disclosed embodiments. At step
570, processing unit 110 may construct an initial vehicle path
associated with vehicle 200. The vehicle path may be represented
using a set of points expressed in coordinates (x, z), and the
distance d, between two points in the set of points may fall in the
range of 1 to 5 meters. In one embodiment, processing unit 110 may
construct the initial vehicle path using two polynomials, such as
left and right road polynomials. Processing unit 110 may calculate
the geometric midpoint between the two polynomials and offset each
point included in the resultant vehicle path by a predetermined
offset (e.g., a smart lane offset), if any (an offset of zero may
correspond to travel in the middle of a lane). The offset may be in
a direction perpendicular to a segment between any two points in
the vehicle path. In another embodiment, processing unit 110 may
use one polynomial and an estimated lane width to offset each point
of the vehicle path by half the estimated lane width plus a
predetermined offset (e.g., a smart lane offset).
[0348] At step 572, processing unit 110 may update the vehicle path
constructed at step 570. Processing unit 110 may reconstruct the
vehicle path constructed at step 570 using a higher resolution,
such that the distance d.sub.k between two points in the set of
points representing the vehicle path is less than the distance
d.sub.i described above. For example, the distance d.sub.k may fall
in the range of 0.1 to 0.3 meters. Processing unit 110 may
reconstruct the vehicle path using a parabolic spline algorithm,
which may yield a cumulative distance vector S corresponding to the
total length of the vehicle path (i.e., based on the set of points
representing the vehicle path).
[0349] At step 574, processing unit 110 may determine a look-ahead
point (expressed in coordinates as (x.sub.l, z.sub.l)) based on the
updated vehicle path constructed at step 572. Processing unit 110
may extract the look-ahead point from the cumulative distance
vector S, and the look-ahead point may be associated with a
look-ahead distance and look-ahead time. The look-ahead distance,
which may have a lower bound ranging from 10 to 20 meters, may be
calculated as the product of the speed of vehicle 200 and the
look-ahead time. For example, as the speed of vehicle 200
decreases, the look-ahead distance may also decrease (e.g., until
it reaches the lower bound). The look-ahead time, which may range
from 0.5 to 1.5 seconds, may be inversely proportional to the gain
of one or more control loops associated with causing a navigational
response in vehicle 200, such as the heading error tracking control
loop. For example, the gain of the heading error tracking control
loop may depend on the bandwidth of a yaw rate loop, a steering
actuator loop, car lateral dynamics, and the like. Thus, the higher
the gain of the heading error tracking control loop, the lower the
look-ahead time.
[0350] At step 576, processing unit 110 may determine a heading
error and yaw rate command based on the look-ahead point determined
at step 574. Processing unit 110 may determine the heading error by
calculating the arctangent of the look-ahead point, e.g., arctan
(x.sub.l/z.sub.l). Processing unit 110 may determine the yaw rate
command as the product of the heading error and a high-level
control gain. The high-level control gain may be equal to:
(2/look-ahead time), if the look-ahead distance is not at the lower
bound. Otherwise, the high-level control gain may be equal to:
(2*speed of vehicle 200/look-ahead distance).
[0351] FIG. 5F is a flowchart showing an exemplary process 500F for
determining whether a leading vehicle is changing lanes, consistent
with the disclosed embodiments. At step 580, processing unit 110
may determine navigation information associated with a leading
vehicle (e.g., a vehicle traveling ahead of vehicle 200). For
example, processing unit 110 may determine the position, velocity
(e.g., direction and speed), and/or acceleration of the leading
vehicle, using the techniques described in connection with FIGS. 5A
and 5B, above. Processing unit 110 may also determine one or more
road polynomials, a look-ahead point (associated with vehicle 200),
and/or a snail trail (e.g., a set of points describing a path taken
by the leading vehicle), using the techniques described in
connection with FIG. 5E, above.
[0352] At step 582, processing unit 110 may analyze the navigation
information determined at step 580. In one embodiment, processing
unit 110 may calculate the distance between a snail trail and a
road polynomial (e.g., along the trail). If the variance of this
distance along the trail exceeds a predetermined threshold (for
example, 0.1 to 0.2 meters on a straight road, 0.3 to 0.4 meters on
a moderately curvy road, and 0.5 to 0.6 meters on a road with sharp
curves), processing unit 110 may determine that the leading vehicle
is likely changing lanes. In the case where multiple vehicles are
detected traveling ahead of vehicle 200, processing unit 110 may
compare the snail trails associated with each vehicle. Based on the
comparison, processing unit 110 may determine that a vehicle whose
snail trail does not match with the snail trails of the other
vehicles is likely changing lanes. Processing unit 110 may
additionally compare the curvature of the snail trail (associated
with the leading vehicle) with the expected curvature of the road
segment in which the leading vehicle is traveling. The expected
curvature may be extracted from map data (e.g., data from map
database 160), from road polynomials, from other vehicles' snail
trails, from prior knowledge about the road, and the like. If the
difference in curvature of the snail trail and the expected
curvature of the road segment exceeds a predetermined threshold,
processing unit 110 may determine that the leading vehicle is
likely changing lanes.
[0353] In another embodiment, processing unit 110 may compare the
leading vehicle's instantaneous position with the look-ahead point
(associated with vehicle 200) over a specific period of time (e.g.,
0.5 to 1.5 seconds). If the distance between the leading vehicle's
instantaneous position and the look-ahead point varies during the
specific period of time, and the cumulative sum of variation
exceeds a predetermined threshold (for example, 0.3 to 0.4 meters
on a straight road, 0.7 to 0.8 meters on a moderately curvy road,
and 1.3 to 1.7 meters on a road with sharp curves), processing unit
110 may determine that the leading vehicle is likely changing
lanes. In another embodiment, processing unit 110 may analyze the
geometry of the snail trail by comparing the lateral distance
traveled along the trail with the expected curvature of the snail
trail. The expected radius of curvature may be determined according
to the calculation:
(.delta..sub.z.sup.2+.delta..sub.x.sup.2)/2/(.delta..sub.x), where
.delta..sub.z represents the lateral distance traveled and
.delta..sub.z represents the longitudinal distance traveled. If the
difference between the lateral distance traveled and the expected
curvature exceeds a predetermined threshold (e.g., 500 to 700
meters), processing unit 110 may determine that the leading vehicle
is likely changing lanes. In another embodiment, processing unit
110 may analyze the position of the leading vehicle. If the
position of the leading vehicle obscures a road polynomial (e.g.,
the leading vehicle is overlaid on top of the road polynomial),
then processing unit 110 may determine that the leading vehicle is
likely changing lanes. In the case where the position of the
leading vehicle is such that, another vehicle is detected ahead of
the leading vehicle and the snail trails of the two vehicles are
not parallel, processing unit 110 may determine that the (closer)
leading vehicle is likely changing lanes.
[0354] At step 584, processing unit 110 may determine whether or
not leading vehicle 200 is changing lanes based on the analysis
performed at step 582. For example, processing unit 110 may make
the determination based on a weighted average of the individual
analyses performed at step 582. Under such a scheme, for example, a
decision by processing unit 110 that the leading vehicle is likely
changing lanes based on a particular type of analysis may be
assigned a value of "1" (and "0" to represent a determination that
the leading vehicle is not likely changing lanes). Different
analyses performed at step 582 may be assigned different weights,
and the disclosed embodiments are not limited to any particular
combination of analyses and weights.
[0355] FIG. 6 is a flowchart showing an exemplary process 600 for
causing one or more navigational responses based on stereo image
analysis, consistent with disclosed embodiments. At step 610,
processing unit 110 may receive a first and second plurality of
images via data interface 128. For example, cameras included in
image acquisition unit 120 (such as image capture devices 122 and
124 having fields of view 202 and 204) may capture a first and
second plurality of images of an area forward of vehicle 200 and
transmit them over a digital connection (e.g., USB, wireless,
Bluetooth, etc.) to processing unit 110. In some embodiments,
processing unit 110 may receive the first and second plurality of
images via two or more data interfaces. The disclosed embodiments
are not limited to any particular data interface configurations or
protocols.
[0356] At step 620, processing unit 110 may execute stereo image
analysis module 404 to perform stereo image analysis of the first
and second plurality of images to create a 3D map of the road in
front of the vehicle and detect features within the images, such as
lane markings, vehicles, pedestrians, road signs, highway exit
ramps, traffic lights, road hazards, and the like. Stereo image
analysis may be performed in a manner similar to the steps
described in connection with FIGS. 5A-5D, above. For example,
processing unit 110 may execute stereo image analysis module 404 to
detect candidate objects (e.g., vehicles, pedestrians, road marks,
traffic lights, road hazards, etc.) within the first and second
plurality of images, filter out a subset of the candidate objects
based on various criteria, and perform multi-frame analysis,
construct measurements, and determine a confidence level for the
remaining candidate objects. In performing the steps above,
processing unit 110 may consider information from both the first
and second plurality of images, rather than information from one
set of images alone. For example, processing unit 110 may analyze
the differences in pixel-level data (or other data subsets from
among the two streams of captured images) for a candidate object
appearing in both the first and second plurality of images. As
another example, processing unit 110 may estimate a position and/or
velocity of a candidate object (e.g., relative to vehicle 200) by
observing that the object appears in one of the plurality of images
but not the other or relative to other differences that may exist
relative to objects appearing if the two image streams. For
example, position, velocity, and/or acceleration relative to
vehicle 200 may be determined based on trajectories, positions,
movement characteristics, etc. of features associated with an
object appearing in one or both of the image streams.
[0357] At step 630, processing unit 110 may execute navigational
response module 408 to cause one or more navigational responses in
vehicle 200 based on the analysis performed at step 620 and the
techniques as described above in connection with FIG. 4.
Navigational responses may include, for example, a turn, a lane
shift, a change in acceleration, a change in velocity, braking, and
the like. In some embodiments, processing unit 110 may use data
derived from execution of velocity and acceleration module 406 to
cause the one or more navigational responses. Additionally,
multiple navigational responses may occur simultaneously, in
sequence, or any combination thereof.
[0358] FIG. 7 is a flowchart showing an exemplary process 700 for
causing one or more navigational responses based on an analysis of
three sets of images, consistent with disclosed embodiments. At
step 710, processing unit 110 may receive a first, second, and
third plurality of images via data interface 128. For instance,
cameras included in image acquisition unit 120 (such as image
capture devices 122, 124, and 126 having fields of view 202, 204,
and 206) may capture a first, second, and third plurality of images
of an area forward and/or to the side of vehicle 200 and transmit
them over a digital connection (e.g., USB, wireless, Bluetooth,
etc.) to processing unit 110. In some embodiments, processing unit
110 may receive the first, second, and third plurality of images
via three or more data interfaces. For example, each of image
capture devices 122, 124, 126 may have an associated data interface
for communicating data to processing unit 110. The disclosed
embodiments are not limited to any particular data interface
configurations or protocols.
[0359] At step 720, processing unit 110 may analyze the first,
second, and third plurality of images to detect features within the
images, such as lane markings, vehicles, pedestrians, road signs,
highway exit ramps, traffic lights, road hazards, and the like. The
analysis may be performed in a manner similar to the steps
described in connection with FIGS. 5A-5D and 6, above. For
instance, processing unit 110 may perform monocular image analysis
(e.g., via execution of monocular image analysis module 402 and
based on the steps described in connection with FIGS. 5A-5D, above)
on each of the first, second, and third plurality of images.
Alternatively, processing unit 110 may perform stereo image
analysis (e.g., via execution of stereo image analysis module 404
and based on the steps described in connection with FIG. 6, above)
on the first and second plurality of images, the second and third
plurality of images, and/or the first and third plurality of
images. The processed information corresponding to the analysis of
the first, second, and/or third plurality of images may be
combined. In some embodiments, processing unit 110 may perform a
combination of monocular and stereo image analyses. For example,
processing unit 110 may perform monocular image analysis (e.g., via
execution of monocular image analysis module 402) on the first
plurality of images and stereo image analysis (e.g., via execution
of stereo image analysis module 404) on the second and third
plurality of images. The configuration of image capture devices
122, 124, and 126--including their respective locations and fields
of view 202, 204, and 206--may influence the types of analyses
conducted on the first, second, and third plurality of images. The
disclosed embodiments are not limited to a particular configuration
of image capture devices 122, 124, and 126, or the types of
analyses conducted on the first, second, and third plurality of
images.
[0360] In some embodiments, processing unit 110 may perform testing
on system 100 based on the images acquired and analyzed at steps
710 and 720. Such testing may provide an indicator of the overall
performance of system 100 for certain configurations of image
capture devices 122, 124, and 126. For example, processing unit 110
may determine the proportion of "false hits" (e.g., cases where
system 100 incorrectly determined the presence of a vehicle or
pedestrian) and "misses."
[0361] At step 730, processing unit 110 may cause one or more
navigational responses in vehicle 200 based on information derived
from two of the first, second, and third plurality of images.
Selection of two of the first, second, and third plurality of
images may depend on various factors, such as, for example, the
number, types, and sizes of objects detected in each of the
plurality of images. Processing unit 110 may also make the
selection based on image quality and resolution, the effective
field of view reflected in the images, the number of captured
frames, the extent to which one or more objects of interest
actually appear in the frames (e.g., the percentage of frames in
which an object appears, the proportion of the object that appears
in each such frame, etc.), and the like.
[0362] In some embodiments, processing unit 110 may select
information derived from two of the first, second, and third
plurality of images by determining the extent to which information
derived from one image source is consistent with information
derived from other image sources. For example, processing unit 110
may combine the processed information derived from each of image
capture devices 122, 124, and 126 (whether by monocular analysis,
stereo analysis, or any combination of the two) and determine
visual indicators (e.g., lane markings, a detected vehicle and its
location and/or path, a detected traffic light, etc.) that are
consistent across the images captured from each of image capture
devices 122, 124, and 126. Processing unit 110 may also exclude
information that is inconsistent across the captured images (e.g.,
a vehicle changing lanes, a lane model indicating a vehicle that is
too close to vehicle 200, etc.). Thus, processing unit 110 may
select information derived from two of the first, second, and third
plurality of images based on the determinations of consistent and
inconsistent information.
[0363] Navigational responses may include, for example, a turn, a
lane shift, a change in acceleration, and the like. Processing unit
110 may cause the one or more navigational responses based on the
analysis performed at step 720 and the techniques as described
above in connection with FIG. 4. Processing unit 110 may also use
data derived from execution of velocity and acceleration module 406
to cause the one or more navigational responses. In some
embodiments, processing unit 110 may cause the one or more
navigational responses based on a relative position, relative
velocity, and/or relative acceleration between vehicle 200 and an
object detected within any of the first, second, and third
plurality of images. Multiple navigational responses may occur
simultaneously, in sequence, or any combination thereof.
[0364] Sparse Road Model for Autonomous Vehicle Navigation
[0365] In some embodiments, the disclosed systems and methods may
use a sparse map for autonomous vehicle navigation. For example,
the sparse map may provide sufficient information for navigating an
autonomous vehicle without storing and/or updating a large quantity
of data. As discussed below in further detail, an autonomous
vehicle may use the sparse map to navigate one or more roads based
on one or more stored trajectories.
[0366] Sparse Map for Autonomous Vehicle Navigation
[0367] In some embodiments, the disclosed systems and methods may
use a sparse map for autonomous vehicle navigation. For example,
the sparse map may provide sufficient information for navigation
without requiring excessive data storage or data transfer rates. As
discussed below in further detail, a vehicle (which may be an
autonomous vehicle) may use the sparse map to navigate one or more
roads. For example, in some embodiments, the sparse map may include
data related to a road and potentially landmarks along the road
that may be sufficient for vehicle navigation, but which also
exhibit small data footprints. For example, the sparse data maps
described in detail below may require significantly less storage
space and data transfer bandwidth as compared with digital maps
including detailed map information, such as image data collected
along a road. For example, rather than storing detailed
representations of a road segment, the sparse data map may store
three dimensional polynomial representations of preferred vehicle
paths along a road. These paths may require very little data
storage space. Further, in the described sparse data maps,
landmarks may be identified and included in the sparse map road
model to aid in navigation. These landmarks may be located at any
spacing suitable for enabling vehicle navigation, but in some
cases, such landmarks need not be identified and included in the
model at high densities and short spacings. Rather, in some cases,
navigation may be possible based on landmarks that are spaced apart
by at least 50 meters, at least 100 meters, at least 500 meters, at
least 1 kilometer, or at least 2 kilometers. As will be discussed
in more detail in other sections, the sparse map may be generated
based on data collected or measured by vehicles equipped with
various sensors and devices, such as image capture devices, Global
Positioning System sensors, motion sensors, etc., as the vehicles
travel along roadways. In some cases, the sparse map may be
generated based on data collected during multiple drives of one or
more vehicles along a particular roadway.
[0368] Consistent with disclosed embodiments, an autonomous vehicle
system may use a sparse map for navigation. At the core of the
sparse maps, one or more three-dimensional contours may represent
predetermined trajectories that autonomous vehicles may traverse as
they move along associated road segments. The sparse maps may also
include data representing one or more road features. Such road
features may include recognized landmarks, road signature profiles,
and any other road-related features useful in navigating a vehicle.
The sparse maps may enable autonomous navigation of a vehicle based
on relatively small amounts of data included in the sparse map. For
example, rather than including detailed representations of a road,
such as road edges, road curvature, images associated with road
segments, or data detailing other physical features associated with
a road segment, the disclosed embodiments of the sparse map may
require relatively little storage space (and relatively little
bandwidth when portions of the sparse map are transferred to a
vehicle), but may still adequately provide for autonomous vehicle
navigation. The small data footprint of the disclosed sparse maps,
discussed in further detail below, may be achieved in some
embodiments by storing representations of road-related elements
that require small amounts of data, but still enable autonomous
navigation. For example, rather than storing detailed
representations of various aspects of a road, the disclosed sparse
maps may store polynomial representations of one or more
trajectories that a vehicle may follow along the road. Thus, rather
than storing (or having to transfer) details regarding the physical
nature of the road to enable navigation along the road, using the
disclosed sparse maps, a vehicle may be navigated along a
particular road segment without, in some cases, having to interpret
physical aspects of the road, but rather, by aligning its path of
travel with a trajectory (e.g., a polynomial spline) along the
particular road segment. In this way, the vehicle may be navigated
based mainly upon the stored trajectory (e.g., a polynomial spline)
that may require much less storage space than an approach involving
storage of roadway images, road parameters, road layout, etc.
[0369] In addition to the stored polynomial representations of
trajectories along a road segment, the disclosed sparse maps may
also include small data objects that may represent a road feature.
In some embodiments, the small data objects may include digital
signatures, which are derived from a digital image (or a digital
signal) that was obtained by a sensor (e.g., a camera or other
sensor, such as a suspension sensor) onboard a vehicle traveling
along the road segment. The digital signature may have a reduced
size relative to the signal that was acquired by the sensor. In
some embodiments, the digital signature may be created to be
compatible with a classifier function that is configured to detect
and to identify the road feature from the signal that is acquired
by the sensor, for example during a subsequent drive. In some
embodiments, a digital signature may be created such that it has a
footprint that is as small as possible, while retaining the ability
to correlate or match the road feature with the stored signature
based on an image (or a digital signal generated by a sensor, if
the stored signature is not based on an image and/or includes other
data) of the road feature that is captured by a camera onboard a
vehicle traveling along the same road segment at a subsequent time.
In some embodiments, a size of the data objects may be further
associated with a uniqueness of the road feature. For example, for
a road feature that is detectable by a camera onboard a vehicle,
and where the camera system onboard the vehicle is coupled to a
classifier which is capable of distinguishing the image data
corresponding to that road feature as being associated with a
particular type of road feature, for example, a road sign, and
where such a road sign is locally unique in that area (e.g., there
is no identical road sign or road sign of the same type nearby), it
may be sufficient to store data indicating the type of the road
feature and its location.
[0370] As will be discussed in further detail below, road features
(e.g., landmarks along a road segment) may be stored as small data
objects that may represent a road feature in relatively few bytes,
while at the same time providing sufficient information for
recognizing and using such a feature for navigation. In just one
example, a road sign may be identified as a recognized landmark on
which navigation of a vehicle may be based. A representation of the
road sign may be stored in the sparse map to include, e.g., a few
bytes of data indicating a type of landmark (e.g., a stop sign) and
a few bytes of data indicating a location of the landmark.
Navigating based on such data-light representations of the
landmarks (e.g., using representations sufficient for locating,
recognizing, and navigating based upon the landmarks) may provide a
desired level of navigational functionality associated with sparse
maps without significantly increasing the data overhead associated
with the sparse maps. This lean representation of landmarks (and
other road features) may take advantage of the sensors and
processors included onboard such vehicles that are configured to
detect, identify, and/or classify certain road features. When, for
example, a sign or even a particular type of a sign is locally
unique (e.g., when there is no other sign or no other sign of the
same type) in a given area, the sparse map may use data indicating
a type of a landmark (a sign or a specific type of sign), and
during navigation (e.g., autonomous navigation) when a camera
onboard an autonomous vehicle captures an image of the area
including a sign (or of a specific type of sign), the processor may
process the image, detect the sign (if indeed present in the
image), classify it as a sign (or as a specific type of sign), and
correlate its location with the location of the sign as stored in
the sparse map.
[0371] In some embodiments, an autonomous vehicle may include a
vehicle body and a processor configured to receive data included in
a sparse map and generate navigational instructions for navigating
the vehicle along a road segment based on the data in the sparse
map.
[0372] FIG. 8 shows a sparse map 800 that vehicle 200 (which may be
an autonomous vehicle) may access for providing autonomous vehicle
navigation. Sparse map 800 may be stored in a memory, such as
memory 140 or 150. Such memory devices may include any types of
non-transitory storage devices or computer-readable media. For
example, in some embodiments, memory 140 or 150 may include hard
drives, compact discs, flash memory, magnetic based memory devices,
optical based memory devices, etc. In some embodiments, sparse map
800 may be stored in a database (e.g., map database 160) that may
be stored in memory 140 or 150, or other types of storage
devices.
[0373] In some embodiments, sparse map 800 may be stored on a
storage device or a non-transitory computer-readable medium
provided onboard vehicle 200 (e.g., a storage device included in a
navigation system onboard vehicle 200). A processor (e.g.,
processing unit 110) provided on vehicle 200 may access sparse map
800 stored in the storage device or computer-readable medium
provided onboard vehicle 200 in order to generate navigational
instructions for guiding the autonomous vehicle 200 as it traverses
a road segment.
[0374] Sparse map 800 need not be stored locally with respect to a
vehicle, however. In some embodiments, sparse map 800 may be stored
on a storage device or computer-readable medium provided on a
remote server that communicates with vehicle 200 or a device
associated with vehicle 200. A processor (e.g., processing unit
110) provided on vehicle 200 may receive data included in sparse
map 800 from the remove server and may execute the data for guiding
the autonomous driving of vehicle 200. In such embodiments, sparse
map 800 may be made accessible to a plurality of vehicles
traversing various road segments (e.g., tens, hundreds, thousands,
or millions of vehicles, etc.). It should be noted also that sparse
map 800 may include multiple sub-maps. For example, in some
embodiments, sparse map 800 may include hundreds, thousands,
millions, or more, of sub-maps that can be used in navigating a
vehicle. Such sub-maps may be referred to as local maps, and a
vehicle traveling along a roadway may access any number of local
maps relevant to a location in which the vehicle is traveling. The
local map sections of sparse map 800 may be stored with a Global
Navigation Satellite System (GNSS) key as an index to the database
of sparse map 800. Thus, while computation of steering angles for
navigating a host vehicle in the present system may be performed
without reliance upon a GNSS position of the host vehicle, road
features, or landmarks, such GNSS information may be used for
retrieval of relevant local maps.
[0375] Collection of data and generation of sparse map 800 is
covered in detail in other sections. In general, however, sparse
map 800 may be generated based on data collected from one or more
vehicles as they travel along roadways. For example, using sensors
aboard the one or more vehicles (e.g., cameras, speedometers, GPS,
accelerometers, etc.), the trajectories that the one or more
vehicles travel along a roadway may be recorded, and the polynomial
representation of a preferred trajectory for vehicles making
subsequent trips along the roadway may be determined based on the
collected trajectories travelled by the one or more vehicles.
Similarly, data collected by the one or more vehicles may aid in
identifying potential landmarks along a particular roadway. Data
collected from traversing vehicles may also be used to identify
road profile information, such as road width profiles, road
roughness profiles, traffic line spacing profiles, etc. Using the
collected information, sparse map 800 may be generated and
distributed (e.g., for local storage or via on-the-fly data
transmission) for use in navigating one or more autonomous
vehicles. Map generation may not end upon initial generation of the
map, however. As will be discussed in greater detail in other
sections, sparse map 800 may be continuously or periodically
updated based on data collected from vehicles as those vehicles
continue to traverse roadways included in sparse map 800.
[0376] Data recorded in sparse map 800 may include position
information based on Global Positioning System (GPS) data. For
example, location information may be included in sparse map 800 for
various map elements, including, for example, landmark locations,
road profile locations, etc. Locations for map elements included in
sparse map 800 may be obtained using GPS data collected from
vehicles traversing a roadway. For example, a vehicle passing an
identified landmark may determine a location of the identified
landmark using GPS position information associated with the vehicle
and a determination of a location of the identified landmark
relative to the vehicle (e.g., based on image analysis of data
collected from one or more cameras on board the vehicle). Such
location determinations of an identified landmark (or any other
feature included in sparse map 800) may be repeated as additional
vehicles pass the location of the identified landmark. Some or all
of the additional location determinations can be used to refine the
location information stored in sparse map 800 relative to the
identified landmark. For example, in some embodiments, multiple
position measurements relative to a particular feature stored in
sparse map 800 may be averaged together. Any other mathematical
operations, however, may also be used to refine a stored location
of a map element based on a plurality of determined locations for
the map element.
[0377] The sparse map of the disclosed embodiments may enable
autonomous navigation of a vehicle using relatively small amounts
of stored data. In some embodiments, sparse map 800 may have a data
density (e.g., including data representing the target trajectories,
landmarks, and any other stored road features) of less than 2 MB
per kilometer of roads, less than 1 MB per kilometer of roads, less
than 500 kB per kilometer of roads, or less than 100 kB per
kilometer of roads. In some embodiments, the data density of sparse
map 800 may be less than 10 kB per kilometer of roads or even less
than 2 kB per kilometer of roads (e.g., 1.6 kB per kilometer), or
no more than 10 kB per kilometer of roads, or no more than 20 kB
per kilometer of roads. In some embodiments, most if not all of the
roadways of the United States may be navigated autonomously using a
sparse map having a total of 4 GB or less of data. These data
density values may represent an average over an entire sparse map
800, over a local map within sparse map 800, and/or over a
particular road segment within sparse map 800.
[0378] As noted, sparse map 800 may include representations of a
plurality of target trajectories 810 for guiding autonomous driving
or navigation along a road segment. Such target trajectories may be
stored as three-dimensional splines. The target trajectories stored
in sparse map 800 may be determined based on two or more
reconstructed trajectories of prior traversals of vehicles along a
particular road segment. A road segment may be associated with a
single target trajectory or multiple target trajectories. For
example, on a two lane road, a first target trajectory may be
stored to represent an intended path of travel along the road in a
first direction, and a second target trajectory may be stored to
represent an intended path of travel along the road in another
direction (e.g., opposite to the first direction). Additional
target trajectories may be stored with respect to a particular road
segment. For example, on a multi-lane road one or more target
trajectories may be stored representing intended paths of travel
for vehicles in one or more lanes associated with the multi-lane
road. In some embodiments, each lane of a multi-lane road may be
associated with its own target trajectory. In other embodiments,
there may be fewer target trajectories stored than lanes present on
a multi-lane road. In such cases, a vehicle navigating the
multi-lane road may use any of the stored target trajectories to
guides its navigation by taking into account an amount of lane
offset from a lane for which a target trajectory is stored (e.g.,
if a vehicle is traveling in the left most lane of a three lane
highway, and a target trajectory is stored only for the middle lane
of the highway, the vehicle may navigate using the target
trajectory of the middle lane by accounting for the amount of lane
offset between the middle lane and the left-most lane when
generating navigational instructions).
[0379] In some embodiments, the target trajectory may represent an
ideal path that a vehicle should take as the vehicle travels. The
target trajectory may be located, for example, at an approximate
center of a lane of travel. In other cases, the target trajectory
may be located elsewhere relative to a road segment. For example, a
target trajectory may approximately coincide with a center of a
road, an edge of a road, or an edge of a lane, etc. In such cases,
navigation based on the target trajectory may include a determined
amount of offset to be maintained relative to the location of the
target trajectory. Moreover, in some embodiments, the determined
amount of offset to be maintained relative to the location of the
target trajectory may differ based on a type of vehicle (e.g., a
passenger vehicle including two axles may have a different offset
from a truck including more than two axles along at least a portion
of the target trajectory).
[0380] Sparse map 800 may also include data relating to a plurality
of predetermined landmarks 820 associated with particular road
segments, local maps, etc. As discussed in detail in other
sections, these landmarks may be used in navigation of the
autonomous vehicle. For example, in some embodiments, the landmarks
may be used to determine a current position of the vehicle relative
to a stored target trajectory. With this position information, the
autonomous vehicle may be able to adjust a heading direction to
match a direction of the target trajectory at the determined
location.
[0381] The plurality of landmarks 820 may be identified and stored
in sparse map 800 at any suitable spacing. In some embodiments,
landmarks may be stored at relatively high densities (e.g., every
few meters or more). In some embodiments, however, significantly
larger landmark spacing values may be employed. For example, in
sparse map 800, identified (or recognized) landmarks may be spaced
apart by 10 meters, 20 meters, 50 meters, 100 meters, 1 kilometer,
or 2 kilometers. In some cases, the identified landmarks may be
located at distances of even more than 2 kilometers apart. Between
landmarks, and therefore between determinations of vehicle position
relative to a target trajectory, the vehicle may navigate based on
dead reckoning in which it uses sensors to determine its ego motion
and estimate its position relative to the target trajectory.
Because errors may accumulate during navigation by dead reckoning,
over time the position determinations relative to the target
trajectory may become increasingly less accurate. The vehicle may
use landmarks occurring in sparse map 800 (and their known
locations) to remove the dead reckoning-induced errors in position
determination. In this way, the identified landmarks included in
sparse map 800 may serve as navigational anchors from which an
accurate position of the vehicle relative to a target trajectory
may be determined. Because a certain amount of error may be
acceptable in position location, an identified landmark need not
always be available to an autonomous vehicle. Rather, suitable
navigation may be possible even based on landmark spacings, as
noted above, of 10 meters, 20 meters, 50 meters, 100 meters, 500
meters, 1 kilometer, 2 kilometers, or more. In some embodiments, a
density of 1 identified landmark every 1 km of road may be
sufficient to maintain a longitudinal position determination
accuracy within 1 m. Thus, not every potential landmark appearing
along a road segment need be stored in sparse map 800.
[0382] In addition to target trajectories and identified landmarks,
sparse map 800 may include information relating to various other
road features. For example, FIG. 9A illustrates a representation of
curves along a particular road segment that may be stored in sparse
map 800. In some embodiments, a single lane of a road may be
modeled by a three-dimensional polynomial description of left and
right sides of the road. Such polynomials representing left and
right sides of a single lane are shown in FIG. 9A. Regardless of
how many lanes a road may have, the road may be represented using
polynomials in a way similar to that illustrated in FIG. 9A. For
example, left and right sides of a multi-lane road may be
represented by polynomials similar to those shown in FIG. 9A, and
intermediate lane markings included on a multi-lane road (e.g.,
dashed markings representing lane boundaries, solid yellow lines
representing boundaries between lanes traveling in different
directions, etc.) may also be represented using polynomials such as
those shown in FIG. 9A.
[0383] As shown in FIG. 9A, a lane 900 may be represented using
polynomials (e.g., a first order, second order, third order, or any
suitable order polynomials). For illustration, lane 900 is shown as
a two-dimensional lane and the polynomials are shown as
two-dimensional polynomials. Lane 900 includes a left side 910 and
a right side 920. In some embodiments, more than one polynomial may
be used to represent a location of each side of the road or lane
boundary. For example, each of left side 910 and right side 920 may
be represented by a plurality of polynomials of any suitable
length. In some cases, the polynomials may have a length of about
100 m, although other lengths greater than or less than 100 m may
also be used. Additionally, the polynomials can overlap with one
another in order to facilitate seamless transitions in navigating
based on subsequently encountered polynomials as a host vehicle
travels along a roadway. For example, each of left side 910 and
right side 920 may be represented by a plurality of third order
polynomials separated into segments of about 100 meters in length
(an example of the first predetermined range), and overlapping each
other by about 50 meters. The polynomials representing the left
side 910 and the right side 920 may or may not have the same order.
For example, in some embodiments, some polynomials may be second
order polynomials, some may be third order polynomials, and some
may be fourth order polynomials.
[0384] In the example shown in FIG. 9A, left side 910 of lane 900
is represented by two groups of third order polynomials. The first
group includes polynomial segments 911, 912, and 913. The second
group includes polynomial segments 914, 915, and 916. The two
groups, while substantially parallel to each other, follow the
locations of their respective sides of the road. Polynomial
segments 911-916 have a length of about 100 meters and overlap
adjacent segments in the series by about 50 meters. As noted
previously, however, polynomials of different lengths and different
overlap amounts may also be used. For example, the polynomials may
have lengths of 500 m, 1 km, or more, and the overlap amount may
vary from 0 to 50 m, 50 m to 100 m, or greater than 100 m.
Additionally, while FIG. 9A is shown as representing polynomials
extending in 2D space (e.g., on the surface of the paper), it is to
be understood that these polynomials may represent curves extending
in three dimensions (e.g., including a height component) to
represent elevation changes in a road segment in addition to X-Y
curvature.
[0385] Returning to the target trajectories of sparse map 800, FIG.
9B shows a three-dimensional polynomial representing a target
trajectory for a vehicle traveling along a particular road segment.
The target trajectory represents not only the X-Y path that a host
vehicle should travel along a particular road segment, but also the
elevation change that the host vehicle will experience when
traveling along the road segment. Thus, each target trajectory in
sparse map 800 may be represented by one or more three-dimensional
polynomials, like the three-dimensional polynomial 950 shown in
FIG. 9B. Sparse map 800 may include a plurality of trajectories
(e.g., millions or billions or more to represent trajectories of
vehicles along various road segments along roadways throughout the
world). In some embodiments, each target trajectory may correspond
to a spline connecting three-dimensional polynomial segments.
[0386] Regarding the data footprint of polynomial curves stored in
sparse map 800, in some embodiments, each third degree polynomial
may be represented by four parameters, each requiring four bytes of
data. Suitable representations may be obtained with third degree
polynomials requiring about 192 bytes of data for every 100 m. This
translates to approximately 200 kB per hour in data usage/transfer
requirements for a host vehicle traveling approximately 100
km/hr.
[0387] Sparse map 800 may describe the lanes network using a
combination of geometry descriptors and meta-data. The geometry may
be described by polynomials or splines as described above. The
meta-data may describe the number of lanes, special characteristics
(such as a car pool lane), and possibly other sparse labels. The
total footprint of such indicators may be negligible.
[0388] As previously noted, sparse map 800 may include a plurality
of predetermined landmarks associated with a road segment. Rather
than storing actual images of the landmarks and relying, for
example, on image recognition analysis based on captured images and
stored images, each landmark in sparse map 800 may be represented
and recognized using less data than a stored, actual image would
require. Data representing landmarks may include sufficient
information for describing or identifying the landmarks along a
road. Storing data describing characteristics of landmarks, rather
than the actual images of landmarks, may reduce the size of sparse
map 800.
[0389] FIG. 10 illustrates examples of types of landmarks that may
be represented in sparse map 800. The landmarks may include any
visible and identifiable objects along a road segment. The
landmarks may be selected such that they are fixed and do not
change often with respect to their locations and/or content. The
landmarks included in sparse map 800 may be useful in determining a
location of vehicle 200 with respect to a target trajectory as the
vehicle traverses a particular road segment. Examples of landmarks
may include traffic signs, directional signs, general signs (e.g.,
rectangular signs), roadside fixtures (e.g., lampposts, reflectors,
etc.), and any other suitable category. In some embodiments, lane
marks on the road, may also be included as landmarks in sparse map
800.
[0390] Examples of landmarks shown in FIG. 10 include traffic
signs, directional signs, roadside fixtures, and general signs.
Traffic signs may include, for example, speed limit signs (e.g.,
speed limit sign 1000), yield signs (e.g., yield sign 1005), route
number signs (e.g., route number sign 1010), traffic light signs
(e.g., traffic light sign 1015), stop signs (e.g., stop sign 1020).
Directional signs may include a sign that includes one or more
arrows indicating one or more directions to different places. For
example, directional signs may include a highway sign 1025 having
arrows for directing vehicles to different roads or places, an exit
sign 1030 having an arrow directing vehicles off a road, etc.
[0391] General signs may be unrelated to traffic. For example,
general signs may include billboards used for advertisement, or a
welcome board adjacent a border between two countries, states,
counties, cities, or towns. FIG. 10 shows a general sign 1040
("Joe's Restaurant"). Although general sign 1040 may have a
rectangular shape, as shown in FIG. 10, general sign 1040 may have
other shapes, such as square, circle, triangle, etc.
[0392] Landmarks may also include roadside fixtures. Roadside
fixtures may be objects that are not signs, and may not be related
to traffic or directions. For example, roadside fixtures may
include lampposts (e.g., lamppost 1035), power line posts, traffic
light posts, etc.
[0393] Landmarks may also include beacons that may be specifically
designed for usage in an autonomous vehicle navigation system. For
example, such beacons may include stand-alone structures placed at
predetermined intervals to aid in navigating a host vehicle. Such
beacons may also include visual/graphical information added to
existing road signs (e.g., icons, emblems, bar codes, etc.) that
may be identified or recognized by a vehicle traveling along a road
segment. Such beacons may also include electronic components. In
such embodiments, electronic beacons (e.g., RFID tags, etc.) may be
used to transmit non-visual information to a host vehicle. Such
information may include, for example, landmark identification
and/or landmark location information that a host vehicle may use in
determining its position along a target trajectory.
[0394] In some embodiments, the landmarks included in sparse map
800 may be represented by a data object of a predetermined size.
The data representing a landmark may include any suitable
parameters for identifying a particular landmark. For example, in
some embodiments, landmarks stored in sparse map 800 may include
parameters such as a physical size of the landmark (e.g., to
support estimation of distance to the landmark based on a known
size/scale), a distance to a previous landmark, lateral offset,
height, a type code (e.g., a landmark type--what type of
directional sign, traffic sign, etc.), a GPS coordinate (e.g., to
support global localization), and any other suitable parameters.
Each parameter may be associated with a data size. For example, a
landmark size may be stored using 8 bytes of data. A distance to a
previous landmark, a lateral offset, and height may be specified
using 12 bytes of data. A type code associated with a landmark such
as a directional sign or a traffic sign may require about 2 bytes
of data. For general signs, an image signature enabling
identification of the general sign may be stored using 50 bytes of
data storage. The landmark GPS position may be associated with 16
bytes of data storage. These data sizes for each parameter are
examples only, and other data sizes may also be used.
[0395] Representing landmarks in sparse map 800 in this manner may
offer a lean solution for efficiently representing landmarks in the
database. In some embodiments, signs may be referred to as semantic
signs and non-semantic signs. A semantic sign may include any class
of signs for which there's a standardized meaning (e.g., speed
limit signs, warning signs, directional signs, etc.). A
non-semantic sign may include any sign that is not associated with
a standardized meaning (e.g., general advertising signs, signs
identifying business establishments, etc.). For example, each
semantic sign may be represented with 38 bytes of data (e.g., 8
bytes for size; 12 bytes for distance to previous landmark, lateral
offset, and height; 2 bytes for a type code; and 16 bytes for GPS
coordinates). Sparse map 800 may use a tag system to represent
landmark types. In some cases, each traffic sign or directional
sign may be associated with its own tag, which may be stored in the
database as part of the landmark identification. For example, the
database may include on the order of 1000 different tags to
represent various traffic signs and on the order of about 10000
different tags to represent directional signs. Of course, any
suitable number of tags may be used, and additional tags may be
created as needed. General purpose signs may be represented in some
embodiments using less than about 100 bytes (e.g., about 86 bytes
including 8 bytes for size; 12 bytes for distance to previous
landmark, lateral offset, and height; 50 bytes for an image
signature; and 16 bytes for GPS coordinates).
[0396] Thus, for semantic road signs not requiring an image
signature, the data density impact to sparse map 800, even at
relatively high landmark densities of about 1 per 50 m, may be on
the order of about 760 bytes per kilometer (e.g., 20 landmarks per
km.times.38 bytes per landmark=760 bytes). Even for general purpose
signs including an image signature component, the data density
impact is about 1.72 kB per km (e.g., 20 landmarks per km.times.86
bytes per landmark=1,720 bytes). For semantic road signs, this
equates to about 76 kB per hour of data usage for a vehicle
traveling 100 km/hr. For general purpose signs, this equates to
about 170 kB per hour for a vehicle traveling 100 km/hr.
[0397] In some embodiments, a generally rectangular object, such as
a rectangular sign, may be represented in sparse map 800 by no more
than 100 byte of data. The representation of the generally
rectangular object (e.g., general sign 1040) in sparse map 800 may
include a condensed image signature (e.g., condensed image
signature 1045) associated with the generally rectangular object.
This condensed image signature may be used, for example, to aid in
identification of a general purpose sign, for example, as a
recognized landmark. Such a condensed image signature (e.g., image
information derived from actual image data representing an object)
may avoid a need for storage of an actual image of an object or a
need for comparative image analysis performed on actual images in
order to recognize landmarks.
[0398] Referring to FIG. 10, sparse map 800 may include or store a
condensed image signature 1045 associated with a general sign 1040,
rather than an actual image of general sign 1040. For example,
after an image capture device (e.g., image capture device 122, 124,
or 126) captures an image of general sign 1040, a processor (e.g.,
image processor 190 or any other processor that can process images
either aboard or remotely located relative to a host vehicle) may
perform an image analysis to extract/create condensed image
signature 1045 that includes a unique signature or pattern
associated with general sign 1040. In one embodiment, condensed
image signature 1045 may include a shape, color pattern, a
brightness pattern, or any other feature that may be extracted from
the image of general sign 1040 for describing general sign 1040.
For example, in FIG. 10, the circles, triangles, and stars shown in
condensed image signature 1045 may represent areas of different
colors. The pattern represented by the circles, triangles, and
stars may be stored in sparse map 800, e.g., within the 50 bytes
designated to include an image signature. Notably, the circles,
triangles, and stars are not necessarily meant to indicate that
such shapes are stored as part of the image signature. Rather,
these shapes are meant to conceptually represent recognizable areas
having discernible color differences, textual areas, graphical
shapes, or other variations in characteristics that may be
associated with a general purpose sign. Such condensed image
signatures can be used to identify a landmark in the form of a
general sign. For example, the condensed image signature can be
used to perform a same-not-same analysis based on a comparison of a
stored condensed image signature with image data captured, for
example, using a camera onboard an autonomous vehicle.
[0399] Returning to the target trajectories a host vehicle may use
to navigate a particular road segment, FIG. 11A shows polynomial
representations trajectories capturing during a process of building
or maintaining sparse map 800. A polynomial representation of a
target trajectory included in sparse map 800 may be determined
based on two or more reconstructed trajectories of prior traversals
of vehicles along the same road segment. In some embodiments, the
polynomial representation of the target trajectory included in
sparse map 800 may be an aggregation of two or more reconstructed
trajectories of prior traversals of vehicles along the same road
segment. In some embodiments, the polynomial representation of the
target trajectory included in sparse map 800 may be an average of
the two or more reconstructed trajectories of prior traversals of
vehicles along the same road segment. Other mathematical operations
may also be used to construct a target trajectory along a road path
based on reconstructed trajectories collected from vehicles
traversing along a road segment.
[0400] As shown in FIG. 11A, a road segment 1100 may be travelled
by a number of vehicles 200 at different times. Each vehicle 200
may collect data relating to a path that it took along the road
segment. The path traveled by a particular vehicle may be
determined based on camera data, accelerometer information, speed
sensor information, and/or GPS information, among other potential
sources. Such data may be used to reconstruct trajectories of
vehicles traveling along the road segment, and based on these
reconstructed trajectories, a target trajectory (or multiple target
trajectories) may be determined for the particular road segment.
Such target trajectories may represent a preferred path of a host
vehicle (e.g., guided by an autonomous navigation system) as it
travels along the road segment.
[0401] In the example shown in FIG. 11A, a first reconstructed
trajectory 1101 may be determined based on data received from a
first vehicle traversing road segment 1100 at a first time period
(e.g., day 1), a second reconstructed trajectory 1102 may be
obtained from a second vehicle traversing road segment 1100 at a
second time period (e.g., day 2), and a third reconstructed
trajectory 1103 may be obtained from a third vehicle traversing
road segment 1100 at a third time period (e.g., day 3). Each
trajectory 1101, 1102, and 1103 may be represented by a polynomial,
such as a three-dimensional polynomial. It should be noted that in
some embodiments, any of the reconstructed trajectories may be
assembled onboard the vehicles traversing road segment 1100.
[0402] Additionally, or alternatively, such reconstructed
trajectories may be determined on a server side based on
information received from vehicles traversing road segment 1100.
For example, in some embodiments, vehicles 200 may transmit data to
one or more servers relating to their motion along road segment
1100 (e.g., steering angle, heading, time, position, speed, sensed
road geometry, and/or sensed landmarks, among things). The server
may reconstruct trajectories for vehicles 200 based on the received
data. The server may also generate a target trajectory for guiding
navigation of autonomous vehicle that will travel along the same
road segment 1100 at a later time based on the first, second, and
third trajectories 1101, 1102, and 1103. While a target trajectory
may be associated with a single prior traversal of a road segment,
in some embodiments, each target trajectory included in sparse map
800 may be determined based on two or more reconstructed
trajectories of vehicles traversing the same road segment. In FIG.
11A, the target trajectory is represented by 1110. In some
embodiments, the target trajectory 1110 may be generated based on
an average of the first, second, and third trajectories 1101, 1102,
and 1103. In some embodiments, the target trajectory 1110 included
in sparse map 800 may be an aggregation (e.g., a weighted
combination) of two or more reconstructed trajectories.
[0403] FIGS. 11B and 11C further illustrate the concept of target
trajectories associated with road segments present within a
geographic region 1111. As shown in FIG. 11B, a first road segment
1120 within geographic region 1111 may include a multilane road,
which includes two lanes 1122 designated for vehicle travel in a
first direction and two additional lanes 1124 designated for
vehicle travel in a second direction opposite to the first
direction. Lanes 1122 and lanes 1124 may be separated by a double
yellow line 1123. Geographic region 1111 may also include a
branching road segment 1130 that intersects with road segment 1120.
Road segment 1130 may include a two-lane road, each lane being
designated for a different direction of travel. Geographic region
1111 may also include other road features, such as a stop line
1132, a stop sign 1134, a speed limit sign 1136, and a hazard sign
1138.
[0404] As shown in FIG. 11C, sparse map 800 may include a local map
1140 including a road model for assisting with autonomous
navigation of vehicles within geographic region 1111. For example,
local map 1140 may include target trajectories for one or more
lanes associated with road segments 1120 and/or 1130 within
geographic region 1111. For example, local map 1140 may include
target trajectories 1141 and/or 1142 that an autonomous vehicle may
access or rely upon when traversing lanes 1122. Similarly, local
map 1140 may include target trajectories 1143 and/or 1144 that an
autonomous vehicle may access or rely upon when traversing lanes
1124. Further, local map 1140 may include target trajectories 1145
and/or 1146 that an autonomous vehicle may access or rely upon when
traversing road segment 1130. Target trajectory 1147 represents a
preferred path an autonomous vehicle should follow when
transitioning from lanes 1120 (and specifically, relative to target
trajectory 1141 associated with a right-most lane of lanes 1120) to
road segment 1130 (and specifically, relative to a target
trajectory 1145 associated with a first side of road segment 1130.
Similarly, target trajectory 1148 represents a preferred path an
autonomous vehicle should follow when transitioning from road
segment 1130 (and specifically, relative to target trajectory 1146)
to a portion of road segment 1124 (and specifically, as shown,
relative to a target trajectory 1143 associated with a left lane of
lanes 1124.
[0405] Sparse map 800 may also include representations of other
road-related features associated with geographic region 1111. For
example, sparse map 800 may also include representations of one or
more landmarks identified in geographic region 1111. Such landmarks
may include a first landmark 1150 associated with stop line 1132, a
second landmark 1152 associated with stop sign 1134, a third
landmark associated with speed limit sign 1154, and a fourth
landmark 1156 associated with hazard sign 1138. Such landmarks may
be used, for example, to assist an autonomous vehicle in
determining its current location relative to any of the shown
target trajectories, such that the vehicle may adjust its heading
to match a direction of the target trajectory at the determined
location.
[0406] In some embodiments, sparse may 800 may also include road
signature profiles. Such road signature profiles may be associated
with any discernible/measurable variation in at least one parameter
associated with a road. For example, in some cases, such profiles
may be associated with variations in surface roughness of a
particular road segment, variations in road width over a particular
road segment, variations in distances between dashed lines painted
along a particular road segment, variations in road curvature along
a particular road segment, etc. FIG. 11D shows an example of a road
signature profile 1160. While profile 1160 may represent any of the
parameters mentioned above, or others, in one example, profile 1160
may represent a measure of road surface roughness, as obtained, for
example, by monitoring one or more sensors providing outputs
indicative of an amount of suspension displacement as a vehicle
travels a particular road segment. Alternatively, profile 1160 may
represent variation in road width, as determined based on image
data obtained via a camera onboard a vehicle traveling a particular
road segment. Such profiles may be useful, for example, in
determining a particular location of an autonomous vehicle relative
to a particular target trajectory. That is, as it traverses a road
segment, an autonomous vehicle may measure a profile associated
with one or more parameters associated with the road segment. If
the measured profile can be correlated/matched with a predetermined
profile that plots the parameter variation with respect to position
along the road segment, then the measured and predetermined
profiles may be used (e.g., by overlaying corresponding sections of
the measured and predetermined profiles) in order to determine a
current position along the road segment and, therefore, a current
position relative to a target trajectory for the road segment.
[0407] In some embodiments, sparse map 800 may include different
trajectories based on different characteristics associated with a
user of autonomous vehicles, environmental conditions, and/or other
parameters relating to driving. For example, in some embodiments,
different trajectories may be generated based on different user
preferences and/or profiles. Sparse map 800 including such
different trajectories may be provided to different autonomous
vehicles of different users. For example, some users may prefer to
avoid toll roads, while others may prefer to take the shortest or
fastest routes, regardless of whether there is a toll road on the
route. The disclosed systems may generate different sparse maps
with different trajectories based on such different user
preferences or profiles. As another example, some users may prefer
to travel in a fast moving lane, while others may prefer to
maintain a position in the central lane at all times.
[0408] Different trajectories may be generated and included in
sparse map 800 based on different environmental conditions, such as
day and night, snow, rain, fog, etc. Autonomous vehicles driving
under different environmental conditions may be provided with
sparse map 800 generated based on such different environmental
conditions. In some embodiments, cameras provided on autonomous
vehicles may detect the environmental conditions, and may provide
such information back to a server that generates and provides
sparse maps. For example, the server may generate or update an
already generated sparse map 800 to include trajectories that may
be more suitable or safer for autonomous driving under the detected
environmental conditions. The update of sparse map 800 based on
environmental conditions may be performed dynamically as the
autonomous vehicles are traveling along roads.
[0409] Other different parameters relating to driving may also be
used as a basis for generating and providing different sparse maps
to different autonomous vehicles. For example, when an autonomous
vehicle is traveling at a high speed, turns may be tighter.
Trajectories associated with specific lanes, rather than roads, may
be included in sparse map 800 such that the autonomous vehicle may
maintain within a specific lane as it follows a specific
trajectory. When an image captured by a camera onboard the
autonomous vehicle indicates that the vehicle has drifted outside
of the lane (e.g., crossed the lane mark), an action may be
triggered within the vehicle to bring the vehicle back to the
designated lane according to the specific trajectory.
[0410] Constructing a Road Model for Autonomous Vehicle
Navigation
[0411] In some embodiments, the disclosed systems and methods may
construct a road model for autonomous vehicle navigation. For
example, the road model may include crowd sourced data. The
disclosed systems and methods may refine the crowd sourced data
based on observed local conditions. Further, the disclosed systems
and methods may determine a refined trajectory for an autonomous
vehicle based on sensor information. Still further, the disclosed
systems and methods may identify landmarks for use in the road
model, as well refine the positions of the landmarks in the road
model. These systems and methods are disclosed in further detail in
the following sections.
[0412] Crowd Sourcing Data for Autonomous Vehicle Navigation
[0413] In some embodiments, the disclosed systems and methods may
construct a road model for autonomous vehicle navigation. For
example, disclosed systems and methods may use crowd sourced data
for generation of an autonomous vehicle road model that one or more
autonomous vehicles may use to navigate along a system of roads. By
crowd sourcing, it means that data are received from various
vehicles (e.g., autonomous vehicles) travelling on a road segment
at different times and such data are used to generate and/or update
the road model. The model may, in turn, be transmitted to the
vehicles or other vehicles later travelling along the road segment
for assisting autonomous vehicle navigation. The road model may
include a plurality of target trajectories representing preferred
trajectories that autonomous vehicles should follow as they
traverse a road segment. The target trajectories may be the same as
a reconstructed actual trajectory collected from a vehicle
traversing a road segment, which may be transmitted from the
vehicle to a server. In some embodiments, the target trajectories
may be different from actual trajectories that one or more vehicles
previously took when traversing a road segment. The target
trajectories may be generated based on actual trajectories (e.g.,
through averaging or any other suitable operation).
[0414] The vehicle trajectory data that a vehicle may upload to a
server may correspond with the actual reconstructed trajectory for
the vehicle, or it may correspond to a recommended trajectory,
which may be based on or related to the actual reconstructed
trajectory of the vehicle, but may differ from the actual
reconstructed trajectory. For example, vehicles may modify their
actual, reconstructed trajectories and submit (e.g., recommend) to
the server the modified actual trajectories. The road model may use
the recommended, modified trajectories as target trajectories for
autonomous navigation of other vehicles.
[0415] In addition to trajectory information, other information for
potential use in building a sparse data map 800 may include
information relating to potential landmark candidates. For example,
through crowd sourcing of information, the disclosed systems and
methods may identify potential landmarks in an environment and
refine landmark positions. The landmarks may be used by a
navigation system of autonomous vehicles to determine and/or adjust
the position of the vehicle along the target trajectories.
[0416] The reconstructed trajectories that a vehicle may generate
as it travels along a road may be obtained by any suitable method.
In some embodiments, the reconstructed trajectories may be
developed by stitching together segments of motion for the vehicle,
using, e.g., ego motion estimation (e.g., three dimensional
translation and three dimensional rotation of the camera, and hence
the body of the vehicle). The rotation and translation estimation
may be determined based on analysis of images captured by one or
more image capture devices along with information from other
sensors or devices, such as inertial sensors and speed sensors. For
example, the inertial sensors may include an accelerometer or other
suitable sensors configured to measure changes in translation
and/or rotation of the vehicle body. The vehicle may include a
speed sensor that measures a speed of the vehicle.
[0417] In some embodiments, the ego motion of the camera (and hence
the vehicle body) may be estimated based on an optical flow
analysis of the captured images. An optical flow analysis of a
sequence of images identifies movement of pixels from the sequence
of images, and based on the identified movement, determines motions
of the vehicle. The ego motion may be integrated over time and
along the road segment to reconstruct a trajectory associated with
the road segment that the vehicle has followed.
[0418] Data (e.g., reconstructed trajectories) collected by
multiple vehicles in multiple drives along a road segment at
different times may be used to construct the road model (e.g.,
including the target trajectories, etc.) included in sparse data
map 800. Data collected by multiple vehicles in multiple drives
along a road segment at different times may also be averaged to
increase an accuracy of the model. In some embodiments, data
regarding the road geometry and/or landmarks may be received from
multiple vehicles that travel through the common road segment at
different times. Such data received from different vehicles may be
combined to generate the road model and/or to update the road
model.
[0419] The disclosed systems and methods may enable autonomous
vehicle navigation (e.g., steering control) with low footprint
models, which may be collected by the autonomous vehicles
themselves without the aid of expensive surveying equipment. To
support the autonomous navigation (e.g., steering applications),
the road model may include the geometry of the road, its lane
structure, and landmarks that may be used to determine the location
or position of vehicles along a trajectory included in the model.
Generation of the road model may be performed by a remote server
that communicates with vehicles travelling on the road and that
receives data from the vehicles. The data may include sensed data,
trajectories reconstructed based on the sensed data, and/or
recommended trajectories that may represent modified reconstructed
trajectories. The server may transmit the model back to the
vehicles or other vehicles that later travel on the road to aid in
autonomous navigation.
[0420] The geometry of a reconstructed trajectory (and also a
target trajectory) along a road segment may be represented by a
curve in three dimensional space, which may be a spline connecting
three dimensional polynomials. The reconstructed trajectory curve
may be determined from analysis of a video stream or a plurality of
images captured by a camera installed on the vehicle. In some
embodiments, a location is identified in each frame or image that
is a few meters ahead of the current position of the vehicle. This
location is where the vehicle is expected to travel to in a
predetermined time period. This operation may be repeated frame by
frame, and at the same time, the vehicle may compute the camera's
ego motion (rotation and translation). At each frame or image, a
short range model for the desired path is generated by the vehicle
in a reference frame that is attached to the camera. The short
range models may be stitched together to obtain a three dimensional
model of the road in some coordinate frame, which may be an
arbitrary or predetermined coordinate frame. The three dimensional
model of the road may then be fitted by a spline, which may include
or connect one or more polynomials of suitable orders.
[0421] To conclude the short range road model at each frame, one or
more detection modules may be used. For example, a bottom-up lane
detection module may be used. The bottom-up lane detection module
may be useful when lane marks are drawn on the road. This module
may look for edges in the image and assembles them together to form
the lane marks. A second module may be used together with the
bottom-up lane detection module. The second module is an end-to-end
deep neural network, which may be trained to predict the correct
short range path from an input image. In both modules, the road
model may be detected in the image coordinate frame and transformed
to a three dimensional space that may be virtually attached to the
camera.
[0422] Although the reconstructed trajectory modeling method may
introduce an accumulation of errors due to the integration of ego
motion over a long period of time, which may include a noise
component, such errors may be inconsequential as the generated
model may provide sufficient accuracy for navigation over a local
scale. In addition, it is possible to cancel the integrated error
by using external sources of information, such as satellite images
or geodetic measurements. For example, the disclosed systems and
methods may use a GNSS receiver to cancel accumulated errors.
However, the GNSS positioning signals may not be always available
and accurate. The disclosed systems and methods may enable a
steering application that depends weakly on the availability and
accuracy of GNSS positioning. In such systems, the usage of the
GNSS signals may be limited. For example, in some embodiments, the
disclosed systems may use the GNSS signals for database indexing
purposes only.
[0423] In some embodiments, the range scale (e.g., local scale)
that may be relevant for an autonomous vehicle navigation steering
application may be on the order of 50 meters, 100 meters, 200
meters, 300 meters, etc. Such distances may be used, as the
geometrical road model is mainly used for two purposes: planning
the trajectory ahead and localizing the vehicle on the road model.
In some embodiments, the planning task may use the model over a
typical range of 40 meters ahead (or any other suitable distance
ahead, such as 20 meters, 30 meters, 50 meters), when the control
algorithm steers the vehicle according to a target point located
1.3 seconds ahead (or any other time such as 1.5 seconds, 1.7
seconds, 2 seconds, etc.). The localization task uses the road
model over a typical range of 60 meters behind the car (or any
other suitable distances, such as 50 meters, 100 meters, 150
meters, etc.), according to a method called "tail alignment"
described in more detail in another section. The disclosed systems
and methods may generate a geometrical model that has sufficient
accuracy over particular range, such as 100 meters, such that a
planned trajectory will not deviate by more than, for example, 30
cm from the lane center.
[0424] As explained above, a three dimensional road model may be
constructed from detecting short range sections and stitching them
together. The stitching may be enabled by computing a six degree
ego motion model, using the videos and/or images captured by the
camera, data from the inertial sensors that reflect the motions of
the vehicle, and the host vehicle velocity signal. The accumulated
error may be small enough over some local range scale, such as of
the order of 100 meters. All this may be completed in a single
drive over a particular road segment.
[0425] In some embodiments, multiple drives may be used to average
the resulted model, and to increase its accuracy further. The same
car may travel the same route multiple times, or multiple cars may
send their collected model data to a central server. In any case, a
matching procedure may be performed to identify overlapping models
and to enable averaging in order to generate target trajectories.
The constructed model (e.g., including the target trajectories) may
be used for steering once a convergence criterion is met.
Subsequent drives may be used for further model improvements and in
order to accommodate infrastructure changes.
[0426] Sharing of driving experience (such as sensed data) between
multiple cars becomes feasible if they are connected to a central
server. Each vehicle client may store a partial copy of a universal
road model, which may be relevant for its current position. A
bidirectional update procedure between the vehicles and the server
may be performed by the vehicles and the server. The small
footprint concept discussed above enables the disclosed systems and
methods to perform the bidirectional updates using a very small
bandwidth.
[0427] Information relating to potential landmarks may also be
determined and forwarded to a central server. For example, the
disclosed systems and methods may determine one or more physical
properties of a potential landmark based on one or more images that
include the landmark. The physical properties may include a
physical size (e.g., height, width) of the landmark, a distance
from a vehicle to a landmark, a distance between the landmark to a
previous landmark, the lateral position of the landmark (e.g., the
position of the landmark relative to the lane of travel), the GPS
coordinates of the landmark, a type of landmark, identification of
text on the landmark, etc. For example, a vehicle may analyze one
or more images captured by a camera to detect a potential landmark,
such as a speed limit sign. The vehicle may determine a distance
from the vehicle to the landmark based on the analysis of the one
or more images. In some embodiments, the distance may be determined
based on analysis of images of the landmark using a suitable image
analysis method, such as a scaling method and/or an optical flow
method. In some embodiments, the disclosed systems and methods may
be configured to determine a type or classification of a potential
landmark. In case the vehicle determines that a certain potential
landmark corresponds to a predetermined type or classification
stored in a sparse map, it may be sufficient for the vehicle to
communicate to the server an indication of the type or
classification of the landmark, along with its location. The server
may store such indications. At a later time, other vehicles may
capture an image of the landmark, process the image (e.g., using a
classifier), and compare the result from processing the image to
the indication stored in the server with regard to the type of
landmark. There may be various types of landmarks, and different
types of landmarks may be associated with different types of data
to be uploaded to and stored in the server, different processing
onboard the vehicle may detects the landmark and communicate
information about the landmark to the server, and the system
onboard the vehicle may receive the landmark data from the server
and use the landmark data for identifying a landmark in autonomous
navigation.
[0428] In some embodiments, multiple autonomous vehicles travelling
on a road segment may communicate with a server. The vehicles (or
clients) may generate a curve describing its drive (e.g., through
ego motion integration) in an arbitrary coordinate frame. The
vehicles may detect landmarks and locate them in the same frame.
The vehicles may upload the curve and the landmarks to the server.
The server may collect data from vehicles over multiple drives, and
generate a unified road model. The server may distribute the model
to clients (e.g., vehicles). The server may continuously or
periodically update the model when receiving new data from the
vehicles. For example, the server may process the new data to
evaluate whether it includes information that should trigger an
updated, or creation of new data on the server. The server may
distribute the updated model or the updates to the vehicles for
providing autonomous vehicle navigation.
[0429] The server may use one or more criteria for determining
whether new data received from the vehicles should trigger an
update to the model or trigger creation of new data. For example,
when the new data indicates that a previously recognized landmark
at a specific location no longer exists, or is replaced by another
landmark, the server may determine that the new data should trigger
an update to the model. As another example, when the new data
indicates that a road segment has been closed, and when this has
been corroborated by data received from other vehicles, the server
may determine that the new data should trigger an update to the
model.
[0430] The server may distribute the updated model (or the updated
portion of the model) to one or more vehicles that are traveling on
the road segment, with which the updates to the model are
associated. The server may also distribute the updated model to
vehicles that are about to travel on the road segment, or vehicles
whose planned trip includes the road segment, with which the
updates to the model are associated. For example, while an
autonomous vehicle is traveling along another road segment before
reaching the road segment with which an update is associated, the
server may distribute the updates or updated model to the
autonomous vehicle before it reaches the road segment.
[0431] In some embodiments, the remote server may collect
trajectories and landmarks from multiple clients (e.g., vehicles
that travel along a common road segment). The server may match
curves using landmarks and create an average road model based on
the trajectories collected from the multiple vehicles. The server
may also compute a graph of roads and the most probable path at
each node or conjunction of the road segment.
[0432] The server may average landmark properties received from
multiple vehicles that travelled along the common road segment,
such as the distances between one landmark to another (e.g., a
previous one along the road segment) as measured by multiple
vehicles, to determine an arc-length parameter and support
localization along the path and speed calibration for each client
vehicle. The server may average the physical dimensions of a
landmark measured by multiple vehicles travelled along the common
road segment and recognized the same landmark. The averaged
physical dimensions may be used to support distance estimation,
such as the distance from the vehicle to the landmark. The server
may average lateral positions of a landmark (e.g., position from
the lane in which vehicles are travelling in to the landmark)
measured by multiple vehicles travelled along the common road
segment and recognized the same landmark. The averaged lateral
potion may be used to support lane assignment. The server may
average the GPS coordinates of the landmark measured by multiple
vehicles travelled along the same road segment and recognized the
same landmark. The averaged GPS coordinates of the landmark may be
used to support global localization or positioning of the landmark
in the road model.
[0433] In some embodiments, the server may identify model changes,
such as constructions, detours, new signs, removal of signs, etc.,
based on data received from the vehicles. The server may
continuously or periodically or instantaneously update the model
upon receiving new data from the vehicles. The server may
distribute updates to the model or the updated model to vehicles
for providing autonomous navigation.
[0434] In some embodiments, the server may analyze driver
interventions during the autonomous driving. The server may analyze
data received from the vehicle at the time and location where
intervention occurs, and/or data received prior to the time the
intervention occurred. The server may identify certain portions of
the data that caused or are closely related to the intervention,
for example, data indicating a temporary lane closure setup, data
indicating a pedestrian in the road. The server may update the
model based on the identified data. For example, the server may
modify one or more trajectories stored in the model.
[0435] Consistent with disclosed embodiments, the system can store
information obtained during autonomous navigation (or regular
driver-controlled navigation) for use in later traversals along the
same road. The system may share that information with other
vehicles when they navigate along the road. Each client system may
then further refine the crowd sourced data based on observed local
conditions.
[0436] FIG. 12 is a schematic illustration of a system that uses
crowd sourcing data for autonomous vehicle navigation. FIG. 12
shows a road segment 1200 that includes one or more lanes. A
plurality of vehicles 1205, 1210, 1215, 1220, and 1225 may travel
on road segment 1200 at the same time or at different times
(although shown as appearing on road segment 1200 at the same time
in FIG. 12). At least one of vehicles 1205-1225 may be an
autonomous vehicle. For simplicity of the present example, all of
the vehicles 1205-1225 are presumed to be autonomous vehicles. Each
vehicle may be similar to vehicles disclosed in other embodiments
(e.g., vehicle 200), and may include components or devices included
in or associated with vehicles disclosed in other embodiments. Each
vehicle may be equipped with an image capture device or camera
(e.g., image capture device 122 or camera 122). Each vehicle may
communicate with a remote server 1230 via one or more networks
(e.g., over a cellular network and/or the Internet, etc.) through
wireless communication paths 1235, as indicated by the dashed
lines. Each vehicle may transmit data to server 1230 and receive
data from server 1230. For example, server 1230 may collect data
from multiple vehicles travelling on the road segment 1200 at
different times, and may process the collected data to generate an
autonomous vehicle road navigation model, or an update to the
model. Server 1230 may transmit the autonomous vehicle road
navigation model or the update to the model to the vehicles that
transmitted data to server 1230. Server 1230 may transmit the
autonomous vehicle road navigation model or the update to the model
to other vehicles that travel on road segment 1200 at later
times.
[0437] As vehicles 1205-1225 travel on road segment 1200,
navigation information collected (e.g., detected, sensed, or
measured) by vehicles 1205-1225 may be transmitted to server 1230.
In some embodiments, the navigation information may be associated
with the common road segment 1200. The navigation information may
include a trajectory associated with each of the vehicles 1205-1225
as each vehicle travels over road segment 1200. In some
embodiments, the trajectory may be reconstructed based on data
sensed by various sensors and devices provided on vehicle 1205. For
example, the trajectory may be reconstructed based on at least one
of accelerometer data, speed data, landmarks data, road geometry or
profile data, vehicle positioning data, and ego motion data. In
some embodiments, the trajectory may be reconstructed based on data
from inertial sensors, such as accelerometer, and the velocity of
vehicle 1205 sensed by a speed sensor. In addition, in some
embodiments, the trajectory may be determined (e.g., by a processor
onboard each of vehicles 1205-1225) based on sensed ego motion of
the camera, which may indicate three dimensional translation and/or
three dimensional rotations (or rotational motions). The ego motion
of the camera (and hence the vehicle body) may be determined from
analysis of one or more images captured by the camera.
[0438] In some embodiments, the trajectory of vehicle 1205 may be
determined by a processor provided aboard vehicle 1205 and
transmitted to server 1230. In other embodiments, server 1230 may
receive data sensed by the various sensors and devices provided in
vehicle 1205, and determine the trajectory based on the data
received from vehicle 1205.
[0439] In some embodiments, the navigation information transmitted
from vehicles 1205-1225 to server 1230 may include data regarding
the road geometry or profile. The geometry of road segment 1200 may
include lane structure and/or landmarks. The lane structure may
include the total number of lanes of road segment 1200, the type of
lanes (e.g., one-way lane, two-way lane, driving lane, passing
lane, etc.), markings on lanes, width of lanes, etc. In some
embodiments, the navigation information may include a lane
assignment, e.g., which lane of a plurality of lanes a vehicle is
traveling in. For example, the lane assignment may be associated
with a numerical value "3" indicating that the vehicle is traveling
on the third lane from the left or right. As another example, the
lane assignment may be associated with a text value "center lane"
indicating the vehicle is traveling on the center lane.
[0440] Server 1230 may store the navigation information on a
non-transitory computer-readable medium, such as a hard drive, a
compact disc, a tape, a memory, etc. Server 1230 may generate
(e.g., through a processor included in server 1230) at least a
portion of an autonomous vehicle road navigation model for the
common road segment 1200 based on the navigation information
received from the plurality of vehicles 1205-1225. Server 1230 may
determine a trajectory associated with each lane based on crowd
sourced data (e.g., navigation information) received from multiple
vehicles (e.g., 1205-1225) that travel on a lane of road segment at
different times. Server 1230 may generate the autonomous vehicle
road navigation model or a portion of the model (e.g., an updated
portion) based on a plurality of trajectories determined based on
the crowd sourced navigation data. Server 1230 may transmit the
model or the updated portion of the model to one or more of
autonomous vehicles 1205-1225 traveling on road segment 1200 or any
other autonomous vehicles that travel on road segment at a later
time for updating an existing autonomous vehicle road navigation
model provided in a navigation system of the vehicles. The
autonomous vehicle road navigation model may be used by the
autonomous vehicles in autonomously navigating along the common
road segment 1200.
[0441] In some embodiments, the autonomous vehicle road navigation
model may be included in a sparse map (e.g., sparse map 800
depicted in FIG. 8). Sparse map 800 may include sparse recording of
data related to road geometry and/or landmarks along a road, which
may provide sufficient information for guiding autonomous
navigation of an autonomous vehicle, yet does not require excessive
data storage. In some embodiments, the autonomous vehicle road
navigation model may be stored separately from sparse map 800, and
may use map data from sparse map 800 when the model is executed for
navigation. In some embodiments, the autonomous vehicle road
navigation model may use map data included in sparse map 800 for
determining target trajectories along road segment 1200 for guiding
autonomous navigation of autonomous vehicles 1205-1225 or other
vehicles that later travel along road segment 1200. For example,
when the autonomous vehicle road navigation model is executed by a
processor included in a navigation system of vehicle 1205, the
model may cause the processor to compare the trajectories
determined based on the navigation information received from
vehicle 1205 with predetermined trajectories included in sparse map
800 to validate and/or correct the current traveling course of
vehicle 1205.
[0442] In the autonomous vehicle road navigation model, the
geometry of a road feature or target trajectory may be encoded by a
curve in a three-dimensional space. In one embodiment, the curve
may be a three dimensional spline including one or more connecting
three dimensional polynomials. As one of skill in the art would
understand, a spline may be a numerical function that is piece-wise
defined by a series of polynomials for fitting data. A spline for
fitting the three dimensional geometry data of the road may include
a linear spline (first order), a quadratic spline (second order), a
cubic spline (third order), or any other splines (other orders), or
a combination thereof. The spline may include one or more three
dimensional polynomials of different orders connecting (e.g.,
fitting) data points of the three dimensional geometry data of the
road. In some embodiments, the autonomous vehicle road navigation
model may include a three dimensional spline corresponding to a
target trajectory along a common road segment (e.g., road segment
1200) or a lane of the road segment 1200.
[0443] The autonomous vehicle road navigation model may include
other information, such as identification of at least one landmark
along road segment 1200. The landmark may be visible within a field
of view of a camera (e.g., camera 122) installed on each of
vehicles 1205-1225. In some embodiments, camera 122 may capture an
image of a landmark. A processor (e.g., processor 180, 190, or
processing unit 110) provided on vehicle 1205 may process the image
of the landmark to extract identification information for the
landmark. The landmark identification information, rather than an
actual image of the landmark, may be stored in sparse map 800. The
landmark identification information may require much less storage
space than an actual image Other sensors or systems (e.g., GPS
system) may also provide certain identification information of the
landmark (e.g., position of landmark). The landmark may include at
least one of a traffic sign, an arrow marking, a lane marking, a
dashed lane marking, a traffic light, a stop line, a directional
sign (e.g., a highway exit sign with an arrow indicating a
direction, a highway sign with arrows pointing to different
directions or places), a landmark beacon, or a lamppost. A landmark
beacon refers to a device (e.g., an RFID device) installed along a
road segment that transmits or reflects a signal to a receiver
installed on a vehicle, such that when the vehicle passes by the
device, the beacon received by the vehicle and the location of the
device (e.g., determined from GPS location of the device) may be
used as a landmark to be included in the autonomous vehicle road
navigation model and/or the sparse map 800.
[0444] The identification of at least one landmark may include a
position of the at least one landmark. The position of the landmark
may be determined based on position measurements performed using
sensor systems (e.g., Global Positioning Systems, inertial based
positioning systems, landmark beacon, etc.) associated with the
plurality of vehicles 1205-1225. In some embodiments, the position
of the landmark may be determined by averaging the position
measurements detected, collected, or received by sensor systems on
different vehicles 1205-1225 through multiple drives. For example,
vehicles 1205-1225 may transmit position measurements data to
server 1230, which may average the position measurements and use
the averaged position measurement as the position of the landmark.
The position of the landmark may be continuously refined by
measurements received from vehicles in subsequent drives.
[0445] The identification of the landmark may include a size of the
landmark. The processor provided on a vehicle (e.g., 1205) may
estimate the physical size of the landmark based on the analysis of
the images. Server 1230 may receive multiple estimates of the
physical size of the same landmark from different vehicles over
different drives. Server 1230 may average the different estimates
to arrive at a physical size for the landmark, and store that
landmark size in the road model. The physical size estimate may be
used to further determine or estimate a distance from the vehicle
to the landmark. The distance to the landmark may be estimated
based on the current speed of the vehicle and a scale of expansion
based on the position of the landmark appearing in the images
relative to the focus of expansion of the camera. For example, the
distance to landmark may be estimated by Z=V*dt*R/D, where V is the
speed of vehicle, R is the distance in the image from the landmark
at time t1 to the focus of expansion, and D is the change in
distance for the landmark in the image from t1 to t2. dt represents
the (t241). For example, the distance to landmark may be estimated
by Z=V*dt*R/D, where V is the speed of vehicle, R is the distance
in the image between the landmark and the focus of expansion, dt is
a time interval, and D is the image displacement of the landmark
along the epipolar line. Other equations equivalent to the above
equation, such as Z=V*.omega./.DELTA..omega., may be used for
estimating the distance to the landmark. Here, V is the vehicle
speed, .omega. is an image length (like the object width), and
.DELTA..omega. is the change of that image length in a unit of
time.
When the physical size of the landmark is known, the distance to
the landmark may also be determined based on the following
equation: Z=f*W/.omega.), where f is the focal length, W is the
size of the landmark (e.g., height or width), .omega. is the number
of pixels when the landmark leaves the image. From the above
equation, a change in distance Z may be calculated using
.DELTA.Z=f*W*.DELTA.w/.omega..sup.2+f*.DELTA.W/.omega., where
.DELTA.W decays to zero by averaging, and where .DELTA..omega. is
the number of pixels representing a bounding box accuracy in the
image. A value estimating the physical size of the landmark may be
calculated by averaging multiple observations at the server side.
The resulting error in distance estimation may be very small. There
are two sources of error that may occur when using the formula
above, namely .DELTA.W and .DELTA..omega.. Their contribution to
the distance error is given by
.DELTA.Z=f*W*.DELTA..omega./.omega..sup.2+f*.DELTA.W/o). However,
.DELTA.W decays to zero by averaging; hence .DELTA.Z is determined
by .DELTA..omega. (e.g., the inaccuracy of the bounding box in the
image).
[0446] For landmarks of unknown dimensions, the distance to the
landmark may be estimated by tracking feature points on the
landmark between successive frames. For example, certain features
appearing on a speed limit sign may be tracked between two or more
image frames. Based on these tracked features, a distance
distribution per feature point may be generated. The distance
estimate may be extracted from the distance distribution. For
example, the most frequent distance appearing in the distance
distribution may be used as the distance estimate. As another
example, the average of the distance distribution may be used as
the distance estimate.
[0447] FIG. 13 illustrates an example autonomous vehicle road
navigation model represented by a plurality of three dimensional
splines 1301, 1302, and 1303. The curves 1301-1303 shown in FIG. 13
are for illustration purpose only. Each spline may include one or
more three dimensional polynomials connecting a plurality of data
points 1310. Each polynomial may be a first order polynomial, a
second order polynomial, a third order polynomial, or a combination
of any suitable polynomials having different orders. Each data
point 1310 may be associated with the navigation information
received from vehicles 1205-1225. In some embodiments, each data
point 1310 may be associated with data related to landmarks (e.g.,
size, location, and identification information of landmarks) and/or
road signature profiles (e.g., road geometry, road roughness
profile, road curvature profile, road width profile). In some
embodiments, some data points 1310 may be associated with data
related to landmarks, and others may be associated with data
related to road signature profiles.
[0448] FIG. 14 illustrates a block diagram of server 1230. Server
1230 may include a communication unit 1405, which may include both
hardware components (e.g., communication control circuits,
switches, and antenna), and software components (e.g.,
communication protocols, computer codes). Server 1230 may
communicate with vehicles 1205-1225 through communication unit
1405. For example, server 1230 may receive, through communication
unit 1405, navigation information transmitted from vehicles
1205-1225. Server 1230 may distribute, through communication unit
1405, the autonomous vehicle road navigation model to one or more
autonomous vehicles.
[0449] Server 1230 may include one or more storage devices 1410,
such as a hard drive, a compact disc, a tape, etc. Storage device
1410 may be configured to store data, such as navigation
information received from vehicles 1205-1225 and/or the autonomous
vehicle road navigation model that server 1230 generates based on
the navigation information. Storage device 1410 may be configured
to store any other information, such as a sparse map (e.g., sparse
map 800 discussed in connection with FIG. 8).
[0450] In addition to or in place of storage device 1410, server
1230 may include a memory 1415. Memory 1415 may be similar to or
different from memory 140 or 150. Memory 1415 may be a
non-transitory memory, such as a flash memory, a random access
memory, etc. Memory 1415 may be configured to store data, such as
computer codes or instructions executable by a processor (e.g.,
processor 1420), map data (e.g., data of sparse map 800), the
autonomous vehicle road navigation model, and/or navigation
information received from vehicles 1205-1225.
[0451] Server 1230 may include a processor 1420 configured to
execute computer codes or instructions stored in memory 1415 to
perform various functions. For example, processor 1420 may analyze
the navigation information received from vehicles 1205-1225, and
generate the autonomous vehicle road navigation model based on the
analysis. Processor 1420 may control communication unit 1405 to
distribute the autonomous vehicle road navigation model to one or
more autonomous vehicles (e.g., one or more of vehicles 1205-1225
or any vehicle that travels on road segment 1200 at a later time).
Processor 1420 may be similar to or different from processor 180,
190, or processing unit 110.
[0452] FIG. 15 illustrates a block diagram of memory 1415, which
may store computer codes or instructions for performing one or more
operations for processing vehicle navigation information for use in
autonomous vehicle navigation. As shown in FIG. 15, memory 1415 may
store one or more modules for performing the operations for
processing vehicle navigation information. For example, memory 1415
may include a model generating module 1505 and a model distributing
module 1510. Processor 1420 may execute the instructions stored in
any of modules 1505 and 1510 included in memory 1415.
[0453] Model generating module 1505 may store instructions which,
when executed by processor 1420, may generate at least a portion of
an autonomous vehicle road navigation model for a common road
segment (e.g., road segment 1200) based on navigation information
received from vehicles 1205-1225. For example, in generating the
autonomous vehicle road navigation model, processor 1420 may
cluster vehicle trajectories along the common road segment 1200
into different clusters. Processor 1420 may determine a target
trajectory along the common road segment 1200 based on the
clustered vehicle trajectories for each of the different clusters.
Such an operation may include finding a mean or average trajectory
of the clustered vehicle trajectories (e.g., by averaging data
representing the clustered vehicle trajectories) in each cluster.
In some embodiments, the target trajectory may be associated with a
single lane of the common road segment 1200. The autonomous vehicle
road navigation model may include a plurality of target
trajectories each associated with a separate lane of the common
road segment 1200. In some embodiments, the target trajectory may
be associated with the common road segment 1200 instead of a single
lane of the road segment 1200. The target trajectory may be
represented by a three dimensional spline. In some embodiments, the
spline may be defined by less than 10 kilobytes per kilometer, less
than 20 kilobytes per kilometer, less than 100 kilobytes per
kilometer, less than 1 megabyte per kilometer, or any other
suitable storage size per kilometer.
[0454] The road model and/or sparse map may store trajectories
associated with a road segment. These trajectories may be referred
to as target trajectories, which are provided to autonomous
vehicles for autonomous navigation. The target trajectories may be
received from multiple vehicles, or may be generated based on
actual trajectories or recommended trajectories (actual
trajectories with some modifications) received from multiple
vehicles. The target trajectories included in the road model or
sparse map may be continuously updated (e.g., averaged) with new
trajectories received from other vehicles.
[0455] Vehicles travelling on a road segment may collect data by
various sensors. The data may include landmarks, road signature
profile, vehicle motion (e.g., accelerometer data, speed data),
vehicle position (e.g., GPS data), and may either reconstruct the
actual trajectories themselves, or transmit the data to a server,
which will reconstruct the actual trajectories for the vehicles. In
some embodiments, the vehicles may transmit data relating to a
trajectory (e.g., a curve in an arbitrary reference frame),
landmarks data, and lane assignment along traveling path to server
1230. Various vehicles travelling along the same road segment at
multiple drives may have different trajectories. Server 1230 may
identify routes or trajectories associated with each lane from the
trajectories received from vehicles through a clustering
process.
[0456] FIG. 16 illustrates a process of clustering vehicle
trajectories associated with vehicles 1205-1225 for determining a
target trajectory for the common road segment (e.g., road segment
1200). The target trajectory or a plurality of target trajectories
determined from the clustering process may be included in the
autonomous vehicle road navigation model or sparse map 800. In some
embodiments, vehicles 1205-1225 traveling along road segment 1200
may transmit a plurality of trajectories 1600 to server 1230. In
some embodiments, server 1230 may generate trajectories based on
landmark, road geometry, and vehicle motion information received
from vehicles 1205-1225. To generate the autonomous vehicle road
navigation model, server 1230 may cluster vehicle trajectories 1600
into a plurality of clusters 1605-1630, as shown in FIG. 16.
[0457] Clustering may be performed using various criteria. In some
embodiments, all drives in a cluster may be similar with respect to
the absolute heading along the road segment 1200. The absolute
heading may be obtained from GPS signals received by vehicles
1205-1225. In some embodiments, the absolute heading may be
obtained using dead reckoning. Dead reckoning, as one of skill in
the art would understand, may be used to determine the current
position and hence heading of vehicles 1205-1225 by using
previously determined position, estimated speed, etc. Trajectories
clustered by absolute heading may be useful for identifying routes
along the roadways.
[0458] In some embodiments, all the drives in a cluster may be
similar with respect to the lane assignment (e.g., in the same lane
before and after a junction) along the drive on road segment 1200.
Trajectories clustered by lane assignment may be useful for
identifying lanes along the roadways. In some embodiments, both
criteria (e.g., absolute heading and lane assignment) may be used
for clustering.
[0459] In each cluster 1605-1630, trajectories may be averaged to
obtain a target trajectory associated with the specific cluster.
For example, the trajectories from multiple drives associated with
the same lane cluster may be averaged. The averaged trajectory may
be a target trajectory associate with a specific lane. To average a
cluster of trajectories, server 1230 may select a reference frame
of an arbitrary trajectory C0. For all other trajectories (C1, . .
. , Cn), server 1230 may find a rigid transformation that maps Ci
to C0, where i=1, 2, . . . , n, where n is a positive integer
number, corresponding to the total number of trajectories included
in the cluster. Server 1230 may compute a mean curve or trajectory
in the C0 reference frame.
[0460] In some embodiments, the landmarks may define an arc length
matching between different drives, which may be used for alignment
of trajectories with lanes. In some embodiments, lane marks before
and after a junction may be used for alignment of trajectories with
lanes.
[0461] To assemble lanes from the trajectories, server 1230 may
select a reference frame of an arbitrary lane. Server 1230 may map
partially overlapping lanes to the selected reference frame. Server
1230 may continue mapping until all lanes are in the same reference
frame. Lanes that are next to each other may be aligned as if they
were the same lane, and later they may be shifted laterally.
[0462] Landmarks recognized along the road segment may be mapped to
the common reference frame, first at the lane level, then at the
junction level. For example, the same landmarks may be recognized
multiple times by multiple vehicles in multiple drives. The data
regarding the same landmarks received in different drives may be
slightly different. Such data may be averaged and mapped to the
same reference frame, such as the C0 reference frame. Additionally
or alternatively, the variance of the data of the same landmark
received in multiple drives may be calculated.
[0463] In some embodiments, each lane of road segment 120 may be
associated with a target trajectory and certain landmarks. The
target trajectory or a plurality of such target trajectories may be
included in the autonomous vehicle road navigation model, which may
be used later by other autonomous vehicles travelling along the
same road segment 1200. Landmarks identified by vehicles 1205-1225
while the vehicles travel along road segment 1200 may be recorded
in association with the target trajectory. The data of the target
trajectories and landmarks may be continuously or periodically
updated with new data received from other vehicles in subsequent
drives.
[0464] For localization of an autonomous vehicle, the disclosed
systems and methods may use an extended Kalman filter. The location
of the vehicle may be determined based on three dimensional
position data and/or three dimensional orientation data, prediction
of future location ahead of vehicle's current location by
integration of ego motion. The localization of vehicle may be
corrected or adjusted by image observations of landmarks. For
example, when vehicle detects a landmark within an image captured
by the camera, the landmark may be compared to a known landmark
stored within the road model or sparse map 800. The known landmark
may have a known location (e.g., GPS data) along a target
trajectory stored in the road model and/or sparse map 800. Based on
the current speed and images of the landmark, the distance from the
vehicle to the landmark may be estimated. The location of the
vehicle along a target trajectory may be adjusted based on the
distance to the landmark and the landmark's known location (stored
in the road model or sparse map 800). The landmark's
position/location data (e.g., mean values from multiple drives)
stored in the road model and/or sparse map 800 may be presumed to
be accurate.
[0465] In some embodiments, the disclosed system may form a closed
loop subsystem, in which estimation of the vehicle six degrees of
freedom location (e.g., three dimensional position data plus three
dimensional orientation data) may be used for navigating (e.g.,
steering the wheel of) the autonomous vehicle to reach a desired
point (e.g., 1.3 second ahead in the stored). In turn, data
measured from the steering and actual navigation may be used to
estimate the six degrees of freedom location.
[0466] In some embodiments, poles along a road, such as lampposts
and power or cable line poles may be used as landmarks for
localizing the vehicles. Other landmarks such as traffic signs,
traffic lights, arrows on the road, stop lines, as well as static
features or signatures of an object along the road segment may also
be used as landmarks for localizing the vehicle. When poles are
used for localization, the x observation of the poles (i.e., the
viewing angle from the vehicle) may be used, rather than the y
observation (i.e., the distance to the pole) since the bottoms of
the poles may be occluded and sometimes they are not on the road
plane.
[0467] FIG. 17 illustrates a navigation system for a vehicle, which
may be used for autonomous navigation. For illustration, the
vehicle is referenced as vehicle 1205. The vehicle shown in FIG. 17
may be any other vehicle disclosed herein, including, for example,
vehicles 1210, 1215, 1220, and 1225, as well as vehicle 200 shown
in other embodiments. As shown in FIG. 12, vehicle 1205 may
communicate with server 1230. Vehicle 1205 may include an image
capture device 122 (e.g., camera 122). Vehicle 1205 may include a
navigation system 1700 configured for providing navigation guidance
for vehicle 1205 to travel on a road (e.g., road segment 1200).
Vehicle 1205 may also include other sensors, such as a speed sensor
1720 and an accelerometer 1725. Speed sensor 1720 may be configured
to detect the speed of vehicle 1205. Accelerometer 1725 may be
configured to detect an acceleration or deceleration of vehicle
1205. Vehicle 1205 shown in FIG. 17 may be an autonomous vehicle,
and the navigation system 1700 may be used for providing navigation
guidance for autonomous driving. Alternatively, vehicle 1205 may
also be a non-autonomous, human-controlled vehicle, and navigation
system 1700 may still be used for providing navigation
guidance.
[0468] Navigation system 1700 may include a communication unit 1705
configured to communicate with server 1230 through communication
path 1235. Navigation system 1700 may include a GPS unit 1710
configured to receive and process GPS signals. Navigation system
1700 may include at least one processor 1715 configured to process
data, such as GPS signals, map data from sparse map 800 (which may
be stored on a storage device provided onboard vehicle 1205 or
received from server 1230), road geometry sensed by a road profile
sensor 1730, images captured by camera 122, and/or autonomous
vehicle road navigation model received from server 1230. The road
profile sensor 1730 may include different types of devices for
measuring different types of road profile, such as road surface
roughness, road width, road elevation, road curvature, etc. For
example, the road profile sensor 1730 may include a device that
measures the motion of a suspension of vehicle 1205 to derive the
road roughness profile. In some embodiments, the road profile
sensor 1730 may include radar sensors to measure the distance from
vehicle 1205 to road sides (e.g., barrier on the road sides),
thereby measuring the width of the road. In some embodiments, the
road profile sensor 1730 may include a device configured for
measuring the up and down elevation of the road. In some
embodiment, the road profile sensor 1730 may include a device
configured to measure the road curvature. For example, a camera
(e.g., camera 122 or another camera) may be used to capture images
of the road showing road curvatures. Vehicle 1205 may use such
images to detect road curvatures.
[0469] The at least one processor 1715 may be programmed to
receive, from camera 122, at least one environmental image
associated with vehicle 1205. The at least one processor 1715 may
analyze the at least one environmental image to determine
navigation information related to the vehicle 1205. The navigation
information may include a trajectory related to the travel of
vehicle 1205 along road segment 1200. The at least one processor
1715 may determine the trajectory based on motions of camera 122
(and hence the vehicle), such as three dimensional translation and
three dimensional rotational motions. In some embodiments, the at
least one processor 1715 may determine the translation and
rotational motions of camera 122 based on analysis of a plurality
of images acquired by camera 122. In some embodiments, the
navigation information may include lane assignment information
(e.g., in which lane vehicle 1205 is travelling along road segment
1200). The navigation information transmitted from vehicle 1205 to
server 1230 may be used by server 1230 to generate and/or update an
autonomous vehicle road navigation model, which may be transmitted
back from server 1230 to vehicle 1205 for providing autonomous
navigation guidance for vehicle 1205.
[0470] The at least one processor 1715 may also be programmed to
transmit the navigation information from vehicle 1205 to server
1230. In some embodiments, the navigation information may be
transmitted to server 1230 along with road information. The road
location information may include at least one of the GPS signal
received by the GPS unit 1710, landmark information, road geometry,
lane information, etc. The at least one processor 1715 may receive,
from server 1230, the autonomous vehicle road navigation model or a
portion of the model. The autonomous vehicle road navigation model
received from server 1230 may include at least one update based on
the navigation information transmitted from vehicle 1205 to server
1230. The portion of the model transmitted from server 1230 to
vehicle 1205 may include an updated portion of the model. The at
least one processor 1715 may cause at least one navigational
maneuver (e.g., steering such as making a turn, braking,
accelerating, passing another vehicle, etc.) by vehicle 1205 based
on the received autonomous vehicle road navigation model or the
updated portion of the model.
[0471] The at least one processor 1715 may be configured to
communicate with various sensors and components included in vehicle
1205, including communication unit 1705, GPS unit 1715, camera 122,
speed sensor 1720, accelerometer 1725, and road profile sensor
1730. The at least one processor 1715 may collect information or
data from various sensors and components, and transmit the
information or data to server 1230 through communication unit 1705.
Alternatively or additionally, various sensors or components of
vehicle 1205 may also communicate with server 1230 and transmit
data or information collected by the sensors or components to
server 1230.
[0472] In some embodiments, vehicles 1205-1225 may communicate with
each other, and may share navigation information with each other,
such that at least one of the vehicles 1205-1225 may generate the
autonomous vehicle road navigation model based on information
shared by other vehicles. In some embodiments, vehicles 1205-1225
may share navigation information with each other and each vehicle
may update its own the autonomous vehicle road navigation model
provided in the vehicle. In some embodiments, at least one of the
vehicles 1205-1225 (e.g., vehicle 1205) may function as a hub
vehicle. The at least one processor 1715 of the hub vehicle (e.g.,
vehicle 1205) may perform some or all of the functions performed by
server 1230. For example, the at least one processor 1715 of the
hub vehicle may communicate with other vehicles and receive
navigation information from other vehicles. The at least one
processor 1715 of the hub vehicle may generate the autonomous
vehicle road navigation model or an update to the model based on
the shared information received from other vehicles. The at least
one processor 1715 of the hub vehicle may transmit the autonomous
vehicle road navigation model or the update to the model to other
vehicles for providing autonomous navigation guidance.
[0473] FIG. 18 is a flowchart showing an example process 1800 for
processing vehicle navigation information for use in autonomous
vehicle navigation. Process 1800 may be performed by server 1230 or
processor 1715 included in a hub vehicle. In some embodiments,
process 1800 may be used for aggregating vehicle navigation
information to provide an autonomous vehicle road navigation model
or to update the model. Process 1800 may include receiving
navigation information from a plurality of vehicles (step 1805).
For example, server 1230 may receive the navigation information
from vehicles 1205-1225. The navigation information may be
associated with a common road segment (e.g., road segment 1200)
along which the vehicles 1205-1225 travel. Process 1800 may include
storing the navigation information associated with the common road
segment (step 1810). For example, server 1230 may store the
navigation information in storage device 1410 and/or memory 1415.
Process 1800 may include generating at least a portion of an
autonomous vehicle road navigation model based on the navigation
information (step 1815). For example, server 1230 may generate at
least a portion of the autonomous vehicle road navigation model for
common road segment 1200 based on the navigation information
received from vehicles 1205-1225 that travel on the common road
segment 1200. Process 1800 may further include distributing the
autonomous vehicle road navigation model to one or more autonomous
vehicles (step 1820). For example, server 1230 may distribute the
autonomous vehicle road navigation model or a portion (e.g., an
update) of the model to vehicles 1205-1225, or any other vehicles
later travel on road segment 1200 for use in autonomously
navigating the vehicles along road segment 1200.
[0474] Process 1800 may include additional operations or steps. For
example, generating the autonomous vehicle road navigation model
may include clustering vehicle trajectories received from vehicles
1205-1225 along road segment 1200 into a plurality of clusters.
Process 1800 may include determining a target trajectory along
common road segment 1200 by averaging the clustered vehicle
trajectories in each cluster. Process 1800 may also include
associating the target trajectory with a single lane of common road
segment 1200. Process 1800 may include determining a three
dimensional spline to represent the target trajectory in the
autonomous vehicle road navigation model.
[0475] FIG. 19 is a flowchart showing an example process 1900
performed by a navigation system of a vehicle. Process 1900 may be
performed by processor 1715 included in navigation system 1700.
Process 1900 may include receiving, from a camera, at least one
environmental image associated with the vehicle (step 1905). For
example, processor 1715 may receive, from camera 122, at least one
environmental image associated with vehicle 1205. Camera 122 may
capture one or more images of the environment surrounding vehicle
1205 as vehicle 1205 travels along road segment 1200. Process 1900
may include analyzing the at least one environmental image to
determine navigation information related to the vehicle (step
1910). For example, processor 1715 may analyze the environmental
images received from camera 122 to determine navigation
information, such as a trajectory of travel along road segment
1200. Processor 1715 may determine the trajectory of travel of
vehicle 1205 based on camera ego motions (e.g., three dimensional
translation and/or three dimensional rotational motions) sensed by,
e.g., the analysis of the images.
[0476] Process 1900 may include transmitting the navigation
information from the vehicle to a server (step 1915). In some
embodiments, the navigation information may be transmitted along
with road information from the vehicle to server 1230. For example,
processor 1715 may transmit, via communication unit 1705, the
navigation information along with road information, such as the
lane assignment, road geometry, from vehicle 1205 to server 1230.
Process 1900 may include receiving from the server an autonomous
vehicle road navigation model or a portion of the model (step
1920). For example, processor 1715 may receive the autonomous
vehicle road navigation model or a portion of the model from server
1230. The model or the portion of the model may include at least
one update to the model based on the navigation information
transmitted from vehicle 1205. Processor 1715 may update an
existing model provided in navigation system 1700 of vehicle 1205.
Process 1900 may include causing at least one navigational maneuver
by the vehicle based on the autonomous vehicle road navigation
model (step 1925). For example, processor 1715 may cause vehicle
1205 to steer, make a turn, change lanes, accelerate, brake, stop,
etc. Processor 1715 may send signals to at least one of throttling
system 220, braking system 230, and steering system 240 to cause
vehicle 1205 to perform the navigational maneuver.
[0477] Process 1900 may include other operations or steps performed
by processor 1715. For example, the navigation information may
include a target trajectory for vehicles to travel along a road
segment, and process 1900 may include clustering, by processor
1715, vehicle trajectories related to multiple vehicles travelling
on the road segment and determining the target trajectory based on
the clustered vehicle trajectories. Clustering vehicle trajectories
may include clustering, by processor 1715, the multiple
trajectories related to the vehicles travelling on the road segment
into a plurality of clusters based on at least one of the absolute
heading of vehicles or lane assignment of the vehicles. Generating
the target trajectory may include averaging, by processor 1715, the
clustered trajectories. Other processes or steps performed by
server 1230, as described above, may also be included in process
1900.
[0478] The disclosed systems and methods may include other
features. For example, the disclosed systems may use local
coordinates, rather than global coordinates. For autonomous
driving, some systems may present data in world coordinates. For
example, longitude and latitude coordinates on the earth surface
may be used. In order to use the map for steering, the host vehicle
must know its position and orientation relative to the map. It
seems natural to use a GPS device on board, in order to position
the vehicle on the map and in order to find the rotation
transformation between the body reference frame and the world
reference frame (say, North, East and Down). Once the body
reference frame is aligned with the map reference frame, then the
desired route may be expressed in the body reference frame and the
steering commands may be computed or generated.
[0479] However, one possible issue with this strategy is that
current GPS technology does not usually provide the body location
and pose with sufficient accuracy and availability. To overcome
this problem, it has been proposed to use landmarks whose world
coordinates are known. The idea is to construct very detailed maps
(called High Definition or HD maps), that contain landmarks of
different kinds. The assumption is that the vehicle is equipped
with a sensor that can detect and locate the landmarks in its own
reference frame. Once the relative position between the vehicle and
the landmarks is found, the landmarks' world coordinates are taken
from the HD map, and the vehicle can use them to compute its own
location and pose.
[0480] This method is still using the global world coordinate
system as a mediator that establishes the alignment between the map
and the body reference frames. Namely, the landmarks are used in
order to compensate for the limitations of the GPS device onboard
the vehicles. The landmarks, together with an HD map, may enable to
compute the precise vehicle pose in global coordinates, and hence
the map-body alignment problem is solved.
[0481] In the disclosed systems and methods, instead of using one
global map of the world, many map pieces or local maps may be used
for autonomous navigation. Each piece of a map or each local map
may define its own coordinate frame. These coordinate frames may be
arbitrary. The vehicle's coordinates in the local maps may not need
to indicate where the vehicle is located on the surface of earth.
Moreover, the local maps may not be required to be accurate over
large scales, meaning there may be no rigid transformation that can
embed a local map in the global world coordinate system.
[0482] There are two main processes associated with this
representation of the world, one relates to the generation of the
maps and the other relates to using them. With respect to maps
generation, this type of representation may be created and
maintained by crowd sourcing. There may be no need to apply
sophisticated survey equipment, because the use of HD maps is
limited, and hence crowd sourcing becomes feasible. With respect to
usage, an efficient method to align the local map with the body
reference frame without going through a standard world coordinate
system may be employed. Hence there may be no need, at least in
most scenarios and circumstances, to have a precise estimation of
the vehicle location and pose in global coordinates. The memory
footprint of the local maps may be kept very small.
[0483] The principle underlying the maps generation is the
integration of ego motion. The vehicles sense the motion of the
camera in space (3D translation and 3D rotation). The vehicles or
the server may reconstruct the trajectory of the vehicle by
integration of ego motion over time, and this integrated path may
be used as a model for the road geometry. This process may be
combined with sensing of close range lane marks, and then the
reconstructed route may reflect the path that a vehicle should
follow, and not the particular path that it did follow. In other
words, the reconstructed route or trajectory may be modified based
on the sensed data relating to close range lane marks, and the
modified reconstructed trajectory may be used as a recommended
trajectory or target trajectory, which may be saved in the road
model or sparse map for use by other vehicles navigating the same
road segment.
[0484] In some embodiments, the map coordinate system may be
arbitrary. A camera reference frame may be selected at an arbitrary
time, and used as the map origin. The integrated trajectory of the
camera may be expressed in the coordinate system of that particular
chosen frame. The value of the route coordinates in the map may not
directly represent a location on earth.
[0485] The integrated path may accumulate errors. This may be due
to the fact that the sensing of the ego motion may not be
absolutely accurate. The result of the accumulated error is that
the local map may diverge, and the local map may not be regarded as
a local copy of the global map. The larger the size of the local
map piece, the larger the deviation from the "true" geometry on
earth.
[0486] The arbitrariness and the divergence of the local maps may
not be a design principle but rather may be a consequence. These
properties may be a consequence of the integration method, which
may be applied in order to construct the maps in a crowd sourcing
manner (by vehicles traveling along the roads). However, vehicles
may successfully use the local maps for steering.
[0487] The proposed map may diverge over long distances. Since the
map is used to plan a trajectory in the immediate vicinity of the
vehicle, the effect of the divergence may be acceptable. At any
time instance, the system (e.g., server 1230 or vehicle 1205) may
repeat the alignment procedure, and use the map to predict the road
location (in the camera coordinate frame) some 1.3 seconds ahead
(or any other seconds, such as 1.5 seconds, 1.0 second, 1.8
seconds, etc.). As long as the accumulated error over that distance
is small enough, then the steering command provided for autonomous
driving may be used.
[0488] In some embodiments, a local map may focus on a local area,
and may not cover a too large area. This means that a vehicle that
is using a local map for steering in autonomous driving, may arrive
at some point to the end of the map and may have to switch to
another local piece of map. The switching may be enabled by the
local maps overlapping each other. Once the vehicle enters the area
that is common to both maps, the system (e.g., server 1230 or
vehicle 1205) may continue to generate steering commands based on a
first local map (the map that is being used), but at the same time
the system may localize the vehicle on the other map (or second
local map) that overlaps with the first local map. In other words,
the system may simultaneously align the present coordinate frame of
the camera both with the coordinate frame of the first map and with
the coordinate frame of the second map. When the new alignment is
established, the system may switch to the other map and plan the
vehicle trajectory there.
[0489] The disclosed systems may include additional features, one
of which is related to the way the system aligns the coordinate
frames of the vehicle and the map. As explained above that
landmarks may be used for alignment, assuming the vehicle may
measure its relative position to them. This is useful in autonomous
driving, but sometimes it may result in a demand for a large number
of landmarks and hence a large memory footprint. The disclosed
systems may therefore use an alignment procedure that addresses
this problem. In the alignment procedure, the system may compute a
1D estimator for the location of the vehicle along the road, using
sparse landmarks and integration of ego speed. The system may use
the shape of the trajectory itself to compute the rotation part of
the alignment, using a tail alignment method discussed in details
below in other sections. The idea is that the vehicle reconstructs
its own trajectory while driving the "tail" and computes a rotation
around its assumed position along the road, in order to align the
tail with the map.
[0490] In the disclosed systems and methods, a GPS device may still
be used. Global coordinates may be used for indexing the database
that stores the trajectories and/or landmarks. The relevant piece
of local map and the relevant landmarks in the vicinity of the
vehicles may be be stored in memory and retrieved from the memory
using global GPS coordinates. However, in some embodiments, the
global coordinates may not be used for path planning, and may not
be accurate. In one example, the usage of global coordinates may be
limited for indexing of the information.
[0491] In situations where "tail alignment" cannot function well,
the system may compute the vehicle's pose using a larger number of
landmarks. This may be a rare case, and hence the impact on the
memory footprint may be moderate. Road intersections are examples
of such situations.
[0492] The disclosed systems and methods may use semantic landmarks
(e.g., traffic signs), since they can be reliably detected from the
scene and matched with the landmarks stored in the road model or
sparse map. In some cases the disclosed systems may use
non-semantic landmarks (e.g., general purpose signs) as well, and
in such cases the non-semantic landmarks may be attached to an
appearance signature, as discussed above. The system may use a
learning method for the generation of signatures that follows the
"same or not-same" recognition paradigm.
[0493] For example, given many drives with GPS coordinates along
them, the disclosed systems may produce the underlying road
structure junctions and road segments. The roads are assumed to be
far enough from each other to be able to differentiate them using
the GPS. Only a coarse grained map may be needed. To generate the
underlying road structure graph, the space may be divided into a
lattice of a given resolution (e.g., 50 m by 50 m). Every drive may
be seen as an ordered list of lattice sites. The system may color
every lattice site belonging to a drive to produce an image of the
merged drives. The colored lattice points may be represented as
nodes on the merged drives. The drives passing from one node to
another may be represented as links. The system may fill small
holes in the image, to avoid differentiating lanes and correct for
GPS errors. The system may use a suitable thinning algorithm (e.g.,
an algorithm named "Zhang-Suen" thinning algorithm) to obtain the
skeleton of the image. This skeleton may represent the underlying
road structure, and junctions may be found using a mask (e.g., a
point connected to at least three others). After the junctions are
found, the segments may be the skeleton parts that connect them. To
match the drives back to the skeleton, the system may use a Hidden
Markov Model. Every GPS point may be associated with a lattice site
with a probability inverse to its distance from that site. Use a
suitable algorithm (e.g., an algorithm named the "Viterbi"
algorithm) to match GPS points to lattice sites, while not allowing
consecutive GPS points to match to non neighboring lattice
sites.
[0494] A plurality of methods may be used for mapping the drives
back to the map. For example, a first solution may include keeping
track during the thinning process. A second solution may use
proximity matching. A third solution may use hidden Markov model.
The hidden Markov model assumes an underlying hidden state for
every observation, and assigns probabilities for a given
observation given the state, and for a state given the previous
state. A Viterbi algorithm may be used to find the most probable
states given a list of observations.
[0495] The disclosed systems and methods may include additional
features. For example, the disclosed systems and methods may detect
highway entrances/exits. Multiple drives in the same area may be
merged using GPS data to the same coordinate system. The system may
use visual feature points for mapping and localization.
[0496] In some embodiments, generic visual features may be used as
landmarks for the purpose of registering the position and
orientation of a moving vehicle, in one drive (localization phase),
relative to a map generated by vehicles traversing the same stretch
of road in previous drives (mapping phase). These vehicles may be
equipped with calibrated cameras imaging the vehicle surroundings
and GPS receivers. The vehicles may communicate with a central
server (e.g., server 1230) that maintains an up-to-date map
including these visual landmarks connected to other significant
geometric and semantic information (e.g. lane structure, type and
position of road signs, type and position of road marks, shape of
nearby drivable ground area delineated by the position of physical
obstacles, shape of previously driven vehicle path when controlled
by human driver, etc.). The total amount of data that may be
communicated between the central server and vehicles per length of
road is small, both in the mapping and localization phases.
[0497] In the mapping phase, the disclosed systems (e.g., vehicles
or server) may detect feature points (FPs) and compute their
descriptors (e.g. using the FAST/BRISK/ORB detectors and
descriptors or a detector/descriptor pair that was trained using
the database discussed below). The system may track FPs between
frames in which they appear using their motion in the image plane
and by matching their descriptors using e.g. Euclidean or Hamming
distance in descriptor space. The system may use tracked FPs to
estimate camera motion and world positions of objects on which FPs
were detected and tracked. The system may classify FPs as ones that
will likely be detected in future drives (e.g. FPs detected on
momentarily moving objects, parked cars and shadow texture will
likely not reappear in future drives). This reproducibility
classification (RC) may be a function of both the intensities in a
region of the image pyramid surrounding the detected FP, the motion
of the tracked FP in the image plane, the extent of viewpoints in
which it was successfully tracked/detected and its relative 3D
position. In some embodiments, the vehicles may send representative
FP descriptors (computed from a set of observations), estimated 3D
position relative to vehicle and momentary vehicle GPS coordinates
to server 1230.
[0498] During the mapping phase, when communication bandwidth
between the mapping vehicles and central server is limited, the
vehicles may send FPs to the server at a high frequency when the
presence of FPs or other semantic landmarks in the map (such as
road signs and lane structure) is limited and insufficient for the
purpose of localization. Although vehicles in the mapping phase may
send FPs at a low spatial frequency these may be agglomerated in
the server. Detection of reoccurring FPs may also be performed by
the server and the server may store the set of reoccurring FPs.
Visual appearance of landmarks may at least in some cases be
sensitive to the time of day or the season in which they were
captured. To increase reproducibility probability of FPs, these may
be binned by the server into time-of-day and season bins.
[0499] The vehicles may send the server other semantic and
geometric information in the nearby FP coordinate system (lane
shape, structure of road plane, 3D position of obstacles, free
space in mapping clip momentary coordinate system, path driven by
human driver in a setup drive to a parking location).
[0500] In a localization phase, the server may send a map
containing landmarks in the form of FP positions and descriptors to
vehicles. Feature points (FPs) may be detected and tracked in near
real time within a set of current consecutive frames. Tracked FPs
may be used to estimate camera motion and world positions of FPs.
Currently detected FP descriptors may be searched to match a list
of map FPs having GPS coordinates within an estimated finite GPS
uncertainty radius from the momentary GPS reading. Matching may be
done by searching all pairs of current and mapping FPs that
minimize an Euclidean or Hamming distance in descriptor space.
Using the FP matches and their current and map positions, rotation
and translation between the momentary vehicle position and the
local map coordinate system may be registered.
[0501] The disclosed systems and methods may include a method for
training a reproducibility classifier. Training may be performed in
one of the following schemes in order of growing labeling cost and
resulting classifier accuracy.
[0502] In the first scheme, a database including a large number of
clips recorded by vehicle cameras with matching momentary vehicle
GPS position may be collected. This database may include a
representative sample of drives (with respect to various
properties: e.g., time of day, season, weather condition, type of
roadway). Feature points (FPs) extracted from frames of different
drives at a similar GPS position and heading may be potentially
matched within a GPS uncertainty radius. Unmatched FPs may be
labeled unreproducible and those matched may be labeled
reproducible. A classifier may then be trained to predict the
reproducibility label of an FP given its appearance in the image
pyramid, its momentary position relative to the vehicle and the
extent of viewpoints positions in which it was successfully
tracked.
[0503] In the second scheme, FP pairs extracted from the clip
database described in the first scheme may also be labeled by a
human responsible for annotating FP matches between clips.
[0504] In a third scheme, a database augmenting that of the first
scheme with precise vehicle position, vehicle orientation and image
pixel depth using Light Detection And Ranging (LIDAR) measurements
may be used to accurately match world positions in different
drives. Feature point descriptors may then be computed at the image
region corresponding to these world points at different viewpoints
and drive times. The classifier may then be trained to predict the
average distance in descriptor space a descriptor is located from
its matched descriptors. In this case reproducibility may be
measured by likely having a low descriptor distance.
[0505] Uploading Recommended, Not Actual Trajectories
[0506] Consistent with disclosed embodiments, the system may
generate an autonomous vehicle road navigation model based on the
observed trajectories of vehicles traversing a common road segment
(e.g., which may correspond to the trajectory information forwarded
to a server by a vehicle). The observed trajectories, however, may
not correspond to actual trajectories taken by vehicles traversing
a road segment. Rather, in certain situations, the trajectories
uploaded to the server may be modified with respect to actual
reconstructed trajectories determined by the vehicles. For example,
a vehicle system, while reconstructing a trajectory actually taken,
may use sensor information (e.g., analysis of images provided by a
camera) to determine that its own trajectory may not be the
preferred trajectory for a road segment. For example, the vehicle
may determine based on image data from onboard cameras that it is
not driving in a center of a lane or that it crossed over a lane
boundary for a determined period of time. In such cases, among
others, a refinement to the vehicle's reconstructed trajectory (the
actual path traversed) may be made based on information derived
from the sensor output. The refined trajectory, not the actual
trajectory, may then be uploaded to the server for potential use in
building or updating sparse data map 800.
[0507] Referring to FIGS. 12 and 17, vehicle 1205 may communicate
with server 1230. Vehicle 1205 may be an autonomous vehicle or a
traditional, primarily human-controlled vehicle. Vehicle 1205 may
collect (or detect, sense, measure) data regarding road segment
1200 as vehicle 1205 travels along road segment 1200. The collected
data may include navigation information, such as road geometry,
recognized landmark including signs, road markings, etc. Vehicle
1205 may transmit the collected data to server 1230. Server 1230
may generate and/or update an autonomous vehicle road navigation
model based on the data received from vehicle 1205. The autonomous
vehicle road navigation model may include a plurality of target
trajectories representing preferred paths of travel along
particular road segments.
[0508] As shown in FIG. 17, vehicle 1205 may include navigation
system 1700. Navigation system may include a storage device (e.g.,
a hard drive, a memory) configured for storing the autonomous
vehicle road navigation model and/or map data (e.g., map data of
sparse map 800). It should be noted that the storage device may
store a local copy of the entire road model from sparse data map
800. Alternately, the storage device may store only portions of
sparse data maps (e.g., local maps) provided to the navigating
vehicle as needed. In such embodiments, the local maps may be
stored only temporarily in the storage device and may be purged
from the storage device upon receipt of one or more newly received
local maps or after a vehicle is determined to have exited a
particular navigational area or zone. Navigation system 1700 may
include at least one processor 1715.
[0509] Navigation system 1700 may include one or more sensors, such
as camera 122, GPS unit 1710, road profile sensor 1730, speed
sensor 1720, and accelerometer 1725. Vehicle 1205 may include other
sensors, such as radar sensors. The sensors included in vehicle
1205 may collect data related to road segment 1200 as vehicle 1205
travels along road segment 1200.
[0510] The processor 1715 may be configured to receive, from the
one or more sensors, outputs indicative of a motion of vehicle
1205. For example, accelerometer 1725 may output signals indicating
three dimensional translation and/or three dimensional rotational
motions of camera 122. Speed sensor may output a speed of vehicle
1205. Road profile sensor 1730 may output signals indicating road
roughness, road width, road elevation, road curvature, which may be
used to determine the motion or trajectory of the vehicle 1205.
[0511] Processor 1715 may determine an actual trajectory of vehicle
1205 based on the outputs from the one or more sensors. For
example, based on analysis of images output from camera 122,
processor 1715 may identify landmarks along road segment 1200.
Landmarks may include traffic signs (e.g., speed limit signs),
directional signs (e.g., highway directional signs pointing to
different routes or places), and general signs (e.g., a rectangular
business sign that is associated with a unique signature, such as a
color pattern). The identified landmark may be compared with the
landmark stored in sparse map 800. When a match is found, the
location of the landmark stored in sparse map 800 may be used as
the location of the identified landmark. The location of the
identified landmark may be used for determining the location of the
vehicle 1205 along a target trajectory. In some embodiments,
processor 1715 may also determine the location of vehicle 1205
based on GPS signals output by GPS unit 1710.
[0512] Processor 1715 may determine the vehicle motion based on
output from the accelerometer 1725, the camera 122, and/or the
speed sensor 1720. For example, speed sensor 1720 may output a
current speed of vehicle 1205 to processor 1715. Accelerometer 1725
may output a signal indicating three dimensional translation and/or
rotation of vehicle 1205 to processor 1715. The camera 122 may
output a plurality of images of the surrounding of vehicle 1205 to
processor 1715. Based on the outputs from the plurality of sensors
and devices, processor 1715 may determine an actual trajectory of
vehicle 1205. The actual trajectory reflects the actual path
vehicle 1205 has taken or is taking, including, e.g., which lane
along road segment 1200 vehicle 1205 has travelled in or is
travelling in, and what different road segments vehicle 1205 have
travelled along.
[0513] Processor 1715 may receive, from camera 122, at least one
environmental image associated with vehicle 1205. For example,
camera 122 may be a front-facing camera, which may capture an image
of the environment in front of vehicle 1205. Camera 122 may be
facing other directions, such as the sides of vehicle 1205 or the
rear of vehicle 1205. Vehicle 1205 may include a plurality of
cameras facing different directions. Processor 1715 may analyze the
at least one environmental image to determine information
associated with at least one navigational constraint. The
navigational constraint may include at least one of a barrier
(e.g., a lane separating barrier), an object (e.g., a pedestrian, a
lamppost, a traffic light post), a lane marking (e.g., a solid
yellow lane marking), a sign (e.g., a traffic sign, a directional
sign, a general sign), or another vehicle (e.g., a leading vehicle,
a following vehicle, a vehicle that is traveling on the side of
vehicle 1205).
[0514] Processor 1715 may also determine a target trajectory for
transmitting to server 1230. The target trajectory may be the same
as the actual trajectory determined by processor 1715 based on the
sensor outputs. In some embodiments, the target trajectory may be
different from the actual trajectory determined based on the sensor
outputs. The target trajectory may include one or more
modifications to the actual trajectory based on the determined
information associated with the at least one navigational
constraint.
[0515] For example, the environmental image captured by camera 122
may include a barrier, such as a temporary lane shifting barrier
100 meters ahead of vehicle 1250 that changes the lanes (e.g., when
lanes are temporarily shifted due to an accident ahead). Processor
1715 may detect the temporary lane shifting barrier from the image,
and take a lane different from a lane corresponding to the target
trajectory stored in the road model or sparse map in compliance to
the temporary lane shift. The actual trajectory of vehicle may
reflect this change of lanes. However, the lane shifting is
temporary and may be cleared in the next 10, 15, or 30 minutes.
Vehicle 1205 may thus modify the actual trajectory (i.e., the shift
of lanes) vehicle 1205 has taken to reflect that a target
trajectory should be different from the actual trajectory vehicle
1205 has taken. For example, the system may recognize that the path
traveled differs from a preferred trajectory for the road segment.
Thus, the system may adjust a reconstructed trajectory prior to
uploading the trajectory information to the servers. In other
embodiments, the actual reconstructed trajectory information may be
uploaded, by one or more recommended trajectory refinements (e.g.,
a size and direction of a translation to be made to at least a
portion of the reconstructed trajectory) may also be uploaded. In
some embodiments, processor 1715 may transmit a modified actual
trajectory to server 1230. Server 1230 may generate or update a
target trajectory based on the received information and may
transmit the target trajectory to other autonomous vehicles that
later travel on the same road segment.
[0516] As another example, the environmental image may include an
object, such as a pedestrian suddenly appearing in road segment
1200. Processor 1715 may detect the pedestrian, and vehicle 1205
may change lanes to avoid a collision with the pedestrian. The
actual trajectory vehicle 1205 reconstructed based on sensed data
may include the change of lanes. However, the pedestrian may soon
leave the roadway. So, vehicle 1205 may modify the actual
trajectory (or determine a recommended modification) to reflect
that the target trajectory should be different from the actual
trajectory taken (as the appearance of the pedestrian is a
temporary condition that should not be accounted for in the target
trajectory determination. In some embodiments, the vehicle may
transmit to the server data indicating a temporary deviation from
the predetermined trajectory, when the actual trajectory is
modified. The data may indicate a cause of the deviation, or the
server may analyze the data to determine a cause of the deviation.
Knowing the cause of the deviation may be useful. For example, when
the deviation is due to the driver noticing an accident that has
recently occurred and, in response steering the wheel to avoid
collision, the server may plan a more moderate adjustment to the
model or a specific trajectory associated with the road segment
based on the cause of deviation. As another example, when the cause
of deviation is a pedestrian crossing the road, the server may
determine that there is no need to change the trajectory in the
future.
[0517] As another example, the environmental image may include a
lane marking indicating that vehicle 1205 is driving slightly
outside of a lane, perhaps under the control of a human driver.
Processor 1715 may detect the lane marking from the captured images
and may modify the actual trajectory of vehicle 1205 to account for
the departure from the lane. For example, a translation may be
applied to the reconstructed trajectory so that it falls within the
center of an observed lane.
[0518] FIG. 20 shows an example memory 2000. Memory 2000 may
include various modules, which when executed by a processor, may
cause the processor to perform the disclosed methods. For example,
memory 2000 may include an actual trajectory determination module
2005. Actual trajectory determination module 2005, when executed by
a processor (e.g., processor 1715 or other processors), may cause
the processor to determine an actual trajectory of a vehicle based
on data output or received from one or more sensors included in the
vehicle. For example, the processor may reconstruct the actual
trajectory based on signals received from one or more of
accelerometer 1725, camera 122, and/or speed sensor 1720. In some
embodiments, the processor may determine the actual trajectory
based on the outputs received from the sensors indicative of a
motion of the vehicle.
[0519] Memory 2000 may also include a target trajectory
determination module 2010. Target trajectory determination module
2010, when executed by the processor, may cause the processor to
determine a target trajectory based on the actual trajectory. For
example, based on data received from the sensor, the processor may
determine that one or more modifications need to be made to the
actual trajectory. The modified actual trajectory may be used as
the target trajectory for transmitting to a server (e.g., server
1230). The target trajectory may represent a better trajectory than
the actual trajectory for other autonomous vehicles to follow when
the other autonomous vehicles travel on the same road segment at a
later time. In some embodiments, the processor may determine a
target trajectory that includes the actual trajectory and one or
more modifications based on information associated with
navigational constraints.
[0520] Memory 2000 may also include an image analysis module 2015.
Image analysis module, when executed by the processor, may cause
the processor to analyze one or more images captured by a camera
(e.g., camera 122) using various image analysis algorithms. For
example, the processor may analyze an image of the environment to
identify a landmark, at least one navigational constraint, or to
calculate a distance from the vehicle to the landmark, etc.
[0521] FIG. 21 is a flowchart illustrating an example process for
uploading recommended trajectory to a server. Process 2100 may be
performed by a processor included in a navigation system of a
vehicle, such as processor 1715 included in navigation system 1700
of autonomous vehicle 1205. Process 2100 may include receiving,
from one or more sensors, outputs indicative of a motion of a
vehicle (step 2105). For example, processor 1715 may receive
outputs from inertial sensors, such as accelerometer 1725
indicating the three dimensional translation and/or three
dimensional rotational motions of vehicle 1205. Process 2100 may
include determining an actual trajectory of the vehicle based on
the outputs from the one or more sensors (step 2110). For example,
processor 1715 may analyze images from camera 122, speed from speed
sensor 1720, position information from GPS unit 1710, motion data
from accelerometer 1725, to determine an actual trajectory. Process
2100 may include receiving, from the camera, at least one
environmental image associated with the vehicle (step 2115). For
example, processor 1715 may receive at least one environmental
image associated with vehicle 1205 from camera 122. Camera 122 may
be a front-facing camera, which may capture an image of an
environment in front of vehicle 1205. Process 2100 may include
analyzing the at least one environmental image to determine
information associated with at least one navigation constraint
(step 2120). For example, processor 1715 may analyze the
environmental images from camera 122 to detect at least one of a
barrier, an object, a lane marking, a sign, or another vehicle in
the images. Process 2100 may also include determining a target
trajectory, including the actual trajectory and one or more
modifications to the actual trajectory based on the determined
information associated with the navigational constraint (step
2125). For example, based on at least one of the barrier, object,
lane marking, sign, or another vehicle detected from the
environmental images, processor 1715 may modify the actual
trajectory, e.g., to include a lane or a road other than the lane
or road vehicle 1205 is travelling in. The modified actual
trajectory may be used as the target trajectory. The target
trajectory may reflect a safer or better trajectory than the actual
trajectory vehicle 1205 is taking. Process 2100 may further include
transmitting the target trajectory to a server (step 2130). For
example, processor 1715 may transmit the target trajectory from
vehicle 1205 to server 1230. Server 1230 may transmit the target
trajectory received from vehicle 1205 to other vehicles (which may
be autonomous vehicles or traditional, human-operated vehicles).
Other vehicles may change their lanes or paths based on the target
trajectory. In some embodiments, process 2100 may include
overriding a change in trajectory that is suggested by the server.
For example, when the vehicle is approaching a lane split, and the
server determines to change the current lane to a lane that has
been temporarily closed or marked for other traffic, processor 1715
may override the determination by the server based on the detection
(e.g., from images captured by the camera onboard the vehicle) of
the temporary closure.
[0522] Process 2100 may include other operations or steps. For
example, processor 1715 may receive target trajectories from server
1230. The target trajectories may be transmitted to server 1230
from other vehicles travelling ahead of vehicle 1205 on the same
road segment 1200. Processor 1715 may update an autonomous vehicle
road navigation model provided in navigation system 1700 with the
target trajectories received from server 1230, and cause vehicle
1205 to make a navigational maneuver, such as changing a lane.
[0523] Landmark Identification
[0524] Consistent with disclosed embodiments, the system may
identify landmarks for use in an autonomous vehicle road navigation
model. This identification may include a determination of a
landmark type, physical size, and location of the identified
landmark, among other characteristics.
[0525] FIG. 22 illustrates an example environment including a
system for identifying a landmark for use in autonomous vehicle
navigation. In this example, FIG. 22 shows a road segment 2200.
Vehicles 2201 and 2202 may be traveling along road segment 2200.
Along the road segment 2200, there may be one or more signs or
objects (e.g., 2205 and 2206), which may be identified as
landmarks. Landmarks may be stored in an autonomous vehicle road
navigation model or a sparse map (e.g., sparse map 800). Actual
images of the landmarks need not be saved in the model or sparse
map. Rather, as previously discussed, a small amount of data that
characterizes the landmark type, location, physical size, and, in
certain cases, a condensed image signature may be stored in the
model or sparse map, thereby reducing the storage space required
for storing the model or sparse map and/or transmitting some or all
of the sparse map to autonomous vehicles. In addition, not every
landmark appearing along a road segment is stored. The model or
sparse map may have sparse recording of recognized landmarks, which
may be spaced apart from each other along a road segment by at
least 50 meters, 100 meters, 500 meters, 1 kilometer, 2 kilometers,
etc. Sparse recording of the landmarks also reduce the storage
space required for storing data relating to the landmarks.
Landmarks stored in the model and/or sparse map may be used for
autonomous vehicle navigation along road segment 2200. For example,
recognized landmarks included in sparse data map 800 may be used
for locating vehicles 2201 and 2202 (e.g., determining locations of
vehicles 2201 and 2202 along a target trajectory stored in the
model or sparse map).
[0526] Vehicles 2201 and 2202 may be autonomous vehicles, and may
be similar to vehicles disclosed in other embodiments. For example,
vehicles 2201 and 2202 may include components and devices included
in vehicle 200, such as at least one image capture device (e.g.,
image capture device or camera 122). Vehicles 2201 and 2202 may
each include at least one processor 2210, which may be similar to
processor 180, 190, or processing unit 110. Each of vehicles 2201
and 2202 may include a communication unit 2215, which may
communicate with a server 2230 via one or more networks (e.g., over
a cellular network and/or the Internet, etc.).
[0527] Server 2230 may include both hardware components (e.g.,
circuits, switches, network cards) and software components (e.g.,
communication protocols, computer-readable instructions or codes).
For example, server 2230 may include a communication unit 2231
configured to communicate with communication units 2215 of vehicles
2201 and 2202. Server 2230 may include at least one processor 2232
configured to process data, such as the autonomous vehicle road
navigation model, the sparse map (e.g., sparse map 800), and/or
navigation information received from vehicles 2201 and 2202. The
navigation information may include any information received from
vehicles 2201 and 2202, such as images of landmarks, landmark
identifiers, Global Positioning System signals, ego motion data,
speed, acceleration, road geometry (e.g., road profile, lane
structure, elevation of road segment 2200), etc. Server 2230 may
include a storage device 2233, which may be a hard drive, a compact
disc, a memory, or other non-transitory computer readable
media.
[0528] Vehicles 2201 and 2202 may capture at least one image, via
camera 122, of an environment of vehicles as the vehicles travel
along road segment 2200. The image of the environment may include
an image of signs or landmarks 2205 and 2206. In some embodiments,
at least one identifier associated with landmarks 2205 and 2206 may
be determined by vehicles 2201 and 2202, and the identifier may be
transmitted to server 2230 from the vehicles. In some embodiments,
at least one identifier associated with landmarks 2205 and 2206 may
be determined by server 2230 based on images of the landmarks 2205
and 2206 captured by cameras 122 and transmitted to server
2230.
[0529] For example, camera 122 installed on a host vehicle (e.g.,
vehicle 2201 hosting camera 122) may acquire at least one image
representative of an environment of vehicle 2201 (e.g., in front of
vehicle 2201). Processor 2215 included in vehicle 2201 may analyze
the at least one image to identify a landmark (e.g., landmark 2206)
in the environment of the host vehicle. Processor 2215 may also
analyze the at least one image to determine the at least one
identifier associated with the landmark.
[0530] In some embodiments, processor 2215 may then transmit the at
least one identifier to server 2230. Server 2230 (e.g., through
processor 2232 and communication unit 2231) may receive the at
least one identifier associated with the landmark. Processor 2232
may associate the landmark with the corresponding road segment
2200. Processor 2232 may update the autonomous vehicle road
navigation model relative to the corresponding road segment 2200 to
include the at least one identifier associated with the landmark.
Processor 2232 may distribute the updated autonomous vehicle road
navigation model to a plurality of autonomous vehicles, such as
vehicles 2201 and 2202, and other vehicles that travel along road
segment 2200 at later times.
[0531] The at least one identifier associated with the landmark
(e.g., landmark 2205 or 2206) may include a position of the
landmark. The position may be determined based on the signals
provided by various sensors or devices installed on vehicles 2201
and 2202 (e.g., GPS signals, vehicle motion signals). The
identifier may include a shape of the landmark. For example, the
identifier may include data indicating a rectangular shape of
landmark 2205 or a triangular shape of landmark 2206. The
identifier may include a size of the landmark. For example, the
identifier may include data indicating a width and/or height of the
rectangular sign 2205 and/or the triangular sign 2206. The
identifier may include a distance of the landmark relative to
another landmark. For example, the identifier associated with
landmark 2206 may include a distance d from landmark 2206 to
landmark 2205. The distance d is shown as a distance between
landmarks 2205 and 2206 along road segment 2200. Other distances
may also be used, such as the direct distance between landmarks
2205 and 2206 crossing the road segment 2200. In some embodiments,
the distance may refer to a distance from the recognized landmark
(e.g., 2206) to a previously recognized landmark (e.g., a landmark
that is recognized at least 50 meters, 100 meters, 500 meters, 1
kilometer, 2 kilometers away back along road segment 2200).
[0532] In some embodiments, the identifier may be determined based
on the landmark being identified as one of a plurality of landmark
types. In other words, the identifier may be the type of the
landmark. The landmark types include a traffic sign (e.g., a speed
limit sign), a post (e.g., a lamppost), a directional indicator
(e.g., a high way exit sign with an arrow indicating a direction),
a business sign (e.g., a rectangular sign such as sign 2205), a
reflector (e.g., a reflective mirror at a curve for safety
purposes), a distance marker, etc. Each type may be associated with
a unique tag (e.g., a numerical value, a text value, etc.), which
requires little data storage (e.g., 4 bytes, 8 bytes, etc.). When a
landmark is recognized as a specific, stored type, the tag
corresponding to the type of the landmark may be stored, along with
other features of the landmark (e.g., size, shape, location,
etc.).
[0533] Landmarks may be classified into two categories: landmarks
that are directly relevant to driving, and landmarks that are not
directly relevant to driving. Landmarks directly relevant to
driving may include traffic signs, arrows on the road, lane
markings, traffic lights, stop lines, etc. These landmarks may
include a standard form. Landmarks directly relevant to driving may
be readily recognizable by the autonomous vehicle as a certain
type. Thus, a tag corresponding to the type of landmarks may be
stored with small data storage space (e.g., 1 byte, 2 bytes, 4
bytes, 8 bytes, etc.). For example, a tag having a numerical value
of "55" may be associated with a stop sign, "100" associated with a
speed limit, "108" associated with a traffic light, etc.
[0534] Landmarks not directly relevant to driving may include, for
example, lampposts, directional signs, businesses signs or
billboards (e.g., for advertisements). These landmarks may not have
a standard form. Landmarks that are not directly relevant to
driving, such as billboards for advertisement and lamppost, may not
be readily recognizable by the autonomous vehicle. Signs like
billboards may be referred to as general signs. General signs may
be identified using a condensed signature representation (or a
condensed signature). For example, the identifier associated with a
general sign landmark may include the condensed signature
representation derived from an image of the landmark. The general
sign landmark may be stored using data representing the condensed
signature, rather than an actual image of the landmark. The
condensed signature may require small data storage space. In some
embodiments, the condensed signature may be represented by one or
more integer numbers, which may require only a few bytes of data
storage. The condensed signature representation may include unique
features, patterns, or characteristics extracted or derived from an
image of the landmarks. The condensed signature representation of
landmarks may indicate an appearance of the landmarks.
[0535] The identifier of the landmarks may be stored within the
autonomous vehicle road navigation model or sparse map 800, which
may be used for providing navigation guidance to autonomous
vehicles. For example, when another vehicle later travels along
road segment 2200 a previously determined position for the
recognized landmark may be used in a determination of the location
of that vehicle relative to a target trajectory for a road
segment.
[0536] FIG. 23 illustrates an example environment including a
system for identifying a landmark for use in autonomous vehicle
navigation. Vehicles 2301 and 2302 (which may be autonomous
vehicles) may travel on road segment 2200. Vehicles 2301 and 2302
may be similar to other vehicles (e.g., vehicles 200, 2201, and
2202) disclosed in other embodiments. Vehicle 2301 may include a
camera 122, a processor 2310, and a communication unit 2315.
Vehicle 2302 may include a camera 122, a processor 2311, and a
communication unit 2315. In this embodiment, one of the vehicles
2301 and 2302 may function as a hub vehicle (e.g., vehicle 2301),
which may perform functions performed by server 2230 in the
embodiments shown in FIG. 22. For example, a server similar to
server 2230 may be installed on hub vehicle 2301 to perform
functions similar to those performed by server 2230. As another
example, the processor 2310 provided on vehicle 2301 may perform
some or all of the functions of server 2230.
[0537] As shown in FIG. 23, vehicles 2301 and 2302 may communicate
with each other through communication units 2315, 2316, and a
communication path 2340. Other autonomous vehicles on road segment
2200, although not shown in FIG. 23, may also communicate with hub
vehicle 2301. Vehicle 2302 (and other vehicles) may transmit
landmark data (e.g., images of a landmark 2206) captured or
processed by processor 2311 on vehicle 2302 to processor 2310 on
hub vehicle 2301. Vehicle 2302 may also transmit other navigation
information (e.g., road geometry) to hub vehicle 2301. In some
embodiments, processor 2310 on hub vehicle 2301 may process the
landmark data received from vehicle 2302 to determine an identifier
associated with a landmark detected by vehicle 2302. In some
embodiments, processor 2311 on vehicle 2302 may process images to
determine an identifier associated with a landmark, and transmit
the identifier to vehicle 2301. Processor 2310 on hub vehicle 2301
may associate the landmark with road segment 2200, and update an
autonomous vehicle road navigation model and/or sparse map 800 to
include the identifier associated with the landmark 2206. Processor
2310 on hub vehicle 2301 may distribute the updated autonomous
vehicle road navigation model and/or sparse map 800 to a plurality
of autonomous vehicles, such as vehicle 2302 and other autonomous
vehicles travelling on road segment 2200. It should be understood
that any functions referenced or described relative to the hub
vehicle may be performed by one or more servers located remotely
with respect to the vehicles traveling on a system of roads. For
example, such servers may be located in one or more central
facilities and may be in communication with deployed vehicles via
wireless communication interfaces.
[0538] FIG. 24 illustrates a method of determining a condensed
signature representation of a landmark. The condensed signature
representation (or condense signature, or signature) may be
determined for a landmark that is not directly relevant to driving,
such as a general sign. For example, condensed signature
representation may be determined for a rectangular business sign
(advertisement), such as sign or landmark 2205. The condensed
signature, rather than an actual image of the general sign may be
stored within the model or sparse map, which may be used for later
comparison with a condensed signature derived by other vehicles. In
the embodiment shown in FIG. 24, an image of the landmark 2205 may
be mapped to a sequence of numbers of a predetermined data size,
such as 32 bytes (or any other size, such as 16 bytes, 64 bytes,
etc.). The mapping may be performed through a mapping function
indicated by arrow 2405. Any suitable mapping function may be used.
In some embodiments, a neural network may be used to learn the
mapping function based on a plurality of training images. FIG. 24
shows an example array 2410 including 32 numbers within a range of
-128 to 127. The array 2410 of numbers may be an example condensed
signature representation or identifier of landmark 2205.
[0539] FIG. 25 illustrates another method of determining a
condensed signature representation of a landmark. For example, a
color pattern may be extracted or derived from an image of a
general sign, such as rectangular business sign 2205. As another
example, a brightness pattern may be extracted or derived from the
image of the general sign. The condensed signature representation
may include at least one of the color pattern or the brightness
pattern. In some embodiments, an image of the landmark 2205 may be
divided into a plurality of pixel sections, as shown by the grids
in FIG. 25. For each pixel section, a color value or a brightness
value may be calculated and associated with the pixel section, as
represented by one of the circle, star, or triangle. A pattern 2500
may represent a color pattern (in which case each of the circle,
start, and triangle represents a color value), or a brightness
pattern (in which case each of the circle, start, and triangle
represents a brightness value). Pattern 2500 may be used as the
condensed signature representation of landmark 2205.
[0540] FIG. 26 illustrates an example block diagram of a memory,
which may store computer code or instructions for performing one or
more operations for identifying a landmark for use in autonomous
vehicle navigation. As shown in FIG. 26, memory 2600 may store one
or more modules for performing the operations for identifying a
landmark for use in autonomous vehicle navigation.
[0541] For example, memory 2600 may include a model updating and
distribution module 2605 and a landmark identifier determination
module 2610. In some embodiments, the model updating and
distribution module 2605 and the landmark identifier determination
module 2610 may be stored in the same memory 2600, or in different
memories. A processor may execute the modules to perform various
functions defined by the instructions or codes included within the
modules. For example, when executed by a processor, the model
updating and distribution module 2605 may cause the processor to
update an autonomous vehicle road navigation model relative to a
corresponding road segment to include at least one identifier
associated with a landmark. The model updating and distribution
module 2605 may also cause the processor to distribute the updated
autonomous vehicle road navigation model to a plurality of
autonomous vehicles for providing autonomous navigation. When
executed by a processor, the landmark identifier determination
module 2610 may cause the processor to analyze at least one image
representative of an environment of a vehicle to identify a
landmark in the image. The landmark identifier determination module
2610 may also cause the processor to analyze the image to determine
at least one identifier associated with the landmark. The
identifier may be used for updating the model in the model updating
and distribution module 2605.
[0542] In some embodiments, the landmark identifier determination
module 2610 may be configured with a certain predefined detection
priority. For example, the landmark identifier determination module
2610 may cause the processor to first search for road signs, and if
no road sign is found within a certain distance from a previous
landmark, then landmark identifier determination module 2610 may
use other landmarks.
[0543] In addition the landmark identifier determination module
2610 may include a minimum landmark density/frequency and a maximum
landmark density/frequency, to limit the landmark frequency (e.g.,
detected or stored landmarks over a predetermined distance). In
some embodiments, these limits may ensure that there are enough
landmarks but not too many that are recognized or detected and
stored.
[0544] In some embodiments, the landmark density/frequency may be
associated with a storage size or a bandwidth size. When more road
signs are available, more storage space or bandwidth may be used.
Alternatively or additionally, different settings may be associated
with different types of landmarks. For example, traffic signs may
be associated with a higher landmark density/frequency, whereas
general signs may be associated with a lower landmark
density/frequency, such that within a predetermined distance, more
traffic signs may be detected and stored than general signs.
[0545] In some embodiments, memory 2600 may be included in server
2230, for example, as part of storage device 2233. Processor 2232
included in server 2230 may execute the model updating and
distribution module 2605 to update the autonomous vehicle road
navigation model to include at least one identifier associated with
a landmark, and distribute the updated model to a plurality of
autonomous vehicles. In some embodiments, processor 2232 included
in server 2230 may receive data (images of landmarks, navigation
information, road information, etc.) from vehicles (e.g., 2201,
2202), and may execute the landmark identifier determination module
2610 to determine an identifier associated with the landmark based
on the received data.
[0546] In some embodiments, memory 2600 may be a memory provided on
a hub autonomous vehicle that performs functions of server 2230.
For example, when the hub vehicle is vehicle 2201, processor 2210
may execute the model updating and distribution module 2605 to
update the autonomous vehicle road navigation model to include an
identifier associated with a landmark. Processor 2210 may also
distribute the updated model to a plurality of other autonomous
vehicles travelling on road segment 2200. In some embodiments,
processor 2210 of hub vehicle 2201 may receive data (e.g., images
of landmarks, navigation information, road information, etc.) from
other autonomous vehicles (e.g., vehicle 2202). Processor 2210 of
hub vehicle 2201 may execute the landmark identifier determination
module 2610 to determine an identifier of a landmark based on the
data received from other autonomous vehicles. For example,
processor 2210 of hub vehicle 2201 may analyze an image of an
environment of another vehicle to identify a landmark, and to
determine at least one identifier associated with the landmark. The
identifier may be used by the model updating and distribution
module 2605 in updating the model by hub vehicle 2201.
[0547] In some embodiments, the model updating and distribution
module 2605 and the landmark identifier determination module 2610
may be stored in separate memories. For example, the model updating
and distribution module 2605 may be stored in a memory included in
server 2230, and the landmark identifier determination module 2610
may be stored in a memory provided on an autonomous vehicle (e.g.,
a memory of a navigation system provided on vehicles 2201, 2202,
2301, and 2302). A processor provided in server 2230 (e.g.,
processor 2232) may execute the model updating and distribution
module 2605 to update the model and distribute the updated model to
autonomous vehicles. A processor provided in the autonomous
vehicles (e.g., processor 2210, 2310, or 2311) may execute the
landmark identifier determination module 2610 to determine an
identifier associated with a landmark.
[0548] FIG. 27 is a flowchart showing an exemplary process 2700 for
determining an identifier of a landmark. Process 2700 may be
performed when the landmark identifier determination module 2610 is
executed by a processor, e.g., processor 2232 included in server
2230, or processor 2210, 2310, and 2311 provided on autonomous
vehicles. Process 2700 may include acquiring at least one image of
an environment of a host vehicle (step 2710). For example, camera
122 provided on host vehicle 2202 (on which camera 122 is hosted)
may capture at least one image of the environment surrounding
vehicle 2202. Processor 2210 provided on vehicle 2202 may receive
the image from camera 122. Process 2700 may also include analyzing
the image to identify a landmark (step 2720). For example,
processor 2210 provided on vehicle 2202 may analyze the image
received from camera 122 to identify a landmark in the environment
surrounding vehicle 2202. Process 2700 may also include analyzing
the image to determine at least one identifier associated with the
landmark (step 2730). For example, processor 2210 provided on
vehicle 2202 may analyze the image received from camera 122 to
determine at least one identifier associated with the landmark. The
identifier may include any observable characteristic associated
with the candidate landmark, including any of those discussed
above, among others. For example, observation of such landmarks may
be made through visual recognition based on analysis of captured
images and/or may involve sensing by one of more sensors (e.g., a
suspension sensor), or any other means of observation.
[0549] Process 2700 may include other operations or steps. For
example, in identifying a landmark from the image of the
environment, processor 2210 may identify the landmark based on a
predetermined type. In determining the identifier associated with
the landmark, processor 2210 may determine a position of the
landmark based on GPS signals received by vehicle 2202, or other
sensor signals that may be used to determine the position.
Processor 2210 may determine at least one of a shape or size of the
landmark from the image. Processor 2210 may also determine a
distance of the landmark to another landmark as appearing in the
image, or in the real world. Processor 2210 may extract or derive a
condensed signature representation as part of the identifier of the
landmark. Processor 2210 may determine the condensed signature
representation based on mapping the image of the landmark to a
sequence of numbers of a predetermined data size (e.g., 32 bytes,
64 byte, etc.). Processor 2210 may determine at least one of a
color pattern or a brightness pattern as the condensed signature
representation of the landmark.
[0550] FIG. 28 is a flowchart showing an exemplary process for
updating and distributing a vehicle road navigation model based on
an identifier. Process 2800 may be performed when the model
updating and distribution module 2602 is executed by a processor,
such as processor 2232 included in server 2230, or processors 2210,
2310, and 2311 included in autonomous vehicles. Process 2800 may
include receiving an identifier associated with a landmark (step
2810). For example, processor 2232 may receive at least one
identifier associated with a landmark from autonomous vehicle 2201
or 2202. Process 2800 may include associating the landmark with a
corresponding road segment (step 2820). For example, processor 2232
may associate landmark 2206 with road segment 2200. Process 2800
may include updating an autonomous vehicle road navigation model to
include the identifier associated with the landmark (step 2830).
For example, processor 2232 may update the autonomous vehicle road
navigation model to include an identifier (including, e.g.,
position information, size, shape, pattern) associated with
landmark 2205 in the model. In some embodiments, processor 2232 may
also update sparse map 800 to include the identifier associated
with landmark 2205. Process 2800 may include distributing the
updated model to a plurality of autonomous vehicles (step 2840).
For example, processor 2232 may distribute the updated model to
autonomous vehicles 2201, 2202, and other vehicles that travel on
road segment 2200 at later times. The update model may provide
updated navigation guidance to autonomous vehicles.
[0551] Process 2800 may include other operations or steps. For
example, processor 2232 included in server 2230 may perform some or
all of the process 2700 for determining an identifier associated
with a landmark. Processor 2232 may receive data (including an
image of the environment) related to landmarks from vehicles 2201
and 2202. Processor 2232 may analyze the data (e.g., the image) to
identify a landmark in the image and to determine an identifier
associated with the image.
[0552] The disclosed systems and methods may include other
features. For example, in some embodiments, the vehicle location
along a road segment may be determined by a processor on the
vehicle or a remote server by integrating the velocity of the
vehicle between two landmarks. Thus, the landmarks may serve as one
dimensional (1D) localization anchors. In the model, the position
of a landmark may be computed based on positions identified by
multiple vehicles in multiple drives by, e.g., averaging these
positions.
[0553] For certain landmarks, such as the general signs, the
disclosed systems store an image signature (e.g., a condensed
signature) rather than an actual image of the landmarks Some types
of landmarks may be detected with a relatively high precision, and
may be readily used for localization (e.g., determining the
position of the vehicle). For example, a sign that is directly
relevant to traffic, such as a circular speed limit sign with the
digits "80" may be readily classified as a certain type and easily
detected. On the other hand, a beacon sign (e.g., a rectangular
advertisement sign) that invites the driver to a nearby restaurant
may be harder to find without any false detections. The reason is
that it is difficult to learn a model for a very diverse class of
objects (e.g., the advertisement sign may not fall into a known
class or type). When other easy-to-detect signs are not available,
general signs may also be used as landmarks, although they pose
some risk of false detections.
[0554] For a landmark that is hard to interpret, the disclosed
systems associate an appearance signature (or condensed signature,
or signature) with it. The signature may be stored in the model
(e.g., the road model or the autonomous vehicle road navigation
model), together with the positional information of the landmark.
When the vehicle detects such an object and matches it to the
stored mode, the disclosed systems may match the signatures of the
landmarks. The signature may not encode class information (e.g.,
class information indicating whether the identified object is a
sign), but rather a "same-not-same" information (e.g., information
indicating whether the identified object is the same as one that
has been before, or one that has been stored in the model.
[0555] The systems (e.g., the remove server or the processor
provided on the vehicle) may learn the image signatures from prior
examples. A pair of images may be tagged the "same" if and only if
they belong to the same specific object (a particular landmark at a
particular position). In some embodiments, the disclosed systems
may learn the signatures using a neural network, such as a Siamese
neural network. The signature of the landmark may require small
storage space, such as 10 bytes, 20 bytes, 32 bytes, 50 bytes, 100
bytes, etc.
[0556] The landmarks may be used for longitudinal localization of a
vehicle along a road. Once the relative distances between landmarks
(e.g., a first landmark and a second landmark spaced apart from the
first landmark by a certain distance along the road) are estimated
with a sufficient accuracy, then once when the vehicle passes a
landmark, the vehicle may "reset" the identifier position
estimation and cancel errors that emerge from integration of ego
speed.
[0557] The system may use the ego speed from either the wheels
sensor (e.g., a speed sensor), or from the Global Navigation
Satellite System (GNSS) system. In the first option, the system may
learn a calibration factor per vehicle, to cancel inter-vehicles
variability.
[0558] To localize the position of a camera, the disclosed systems
may identify visual landmarks. Any object with prominent features
that may be repeatedly identified may serve as a visual landmark.
On road scenarios, road side signs and traffic signs in particular,
frequently serve as landmarks. Traffic signs usually are associated
with a type. Traffic sign of "yield" type, for example, may appear
exactly or substantially the same all over a particular country.
When the disclosed systems identify a traffic sign with a type,
also known as typed-traffic-sign, the systems may look for this
type in the map and establish the camera localization when a match
is found.
[0559] Some traffic signs, however, do not look the same. A common
example is the "directional" traffic signs, which tell the driver
which lane goes where. Other, more generic, signs may also be used,
such as signs of a particular restaurant or advertisements. The
standard traffic signs may be detected using traffic sign
recognition algorithms designed to recognize tens or a few hundreds
of signs. These standard signs may be stored in the map using one
identification byte and a few bytes for localization (e.g.,
position of the signs).
[0560] One way to store generic signs is to store the image of the
signs in the map database, and look for that image. This solution,
however, may require a large memory footprint. Whereas a typed
traffic sign may require a single integer (e.g., four bytes), an
image patch with an untyped-traffic sign may require 256 bytes or
more to store even a low resolution of 32.times.32 pixel image. The
solution provided by the disclosed systems and methods uses a
signature function that maps any given image patch showing the sign
to a unique sequence of 32 bytes (any other bytes may also be used,
e.g., 50 bytes). Conceptually, the output of the function is the
signature of the image patch showing the sign. Using this signature
function, the systems may transform any sign to a "signature,"
which may be a sequence of 32 bytes (or 8 integers). Using the
signature, the system may then look in the map for the location of
a sign with a similar signature or conversely, look in the image
for a signature, which according to the map, should be visible in
that area of the image.
[0561] The signature function may be designed to give similar
signatures to similar image patches, and different signatures to
different image patches. The systems may use a deep neural network
to learn both the signature function and a distance function
between two signatures. In the neural network, the actual size of
the sign in the image is not known. Rectangles of various sizes
that may be candidates for signs are detected in the image. Each
rectangle may then be scaled to a uniform size of, for example,
32.times.32 pixels, although other sizes may also be used. For
training the neural network, similar images of the same sign are
tagged as the "same," whereas images for different signs captured
in the same geographic location are tagged as "different." The
image patches were all scaled to a uniform size. The systems may
use a Siamese network that receives two image patches of
32.times.32 pixels each and outputs a binary bit: 0 means image
patches are not the same and 1 means image patches are the same.
For example, in the example shown in FIG. 24, the signature 2410 of
landmark 2205 may be stored in the map. The signature includes a
sequence of integer numbers (first sequence), as shown in FIG. 24.
When a vehicle passes a sign at the same location as sign 2205, the
vehicle may capture an image of the sign, and derive a second
sequence of numbers using the mapping function. For example, the
second sequence of number may include [44, 4, -2, -34, -58, 5, -17,
-106, 26, -23, -8, -4, 7, 57, -16, -9, -11, 1, -26, 34, 18, -21,
-19, -52, 17, -6, 4, 33, -6, 9, -7, -6]. The system may compare the
second sequence with the first second sequence using the neural
network, which may output a score for the comparison. In some
embodiments, a negative score may indicate that the two signatures
are not the same, and a positive score may indicate that the two
signatures are the same. It is noted that the system may not
require two signatures to be exactly the same in order for them to
be regarded as the same. The neural network may be capable of
processing low resolution input images, which leads to a low
computational cost while achieving high performance.
[0562] After the training is completed, the Siamese network may be
separated into two networks: Signature network, which may be the
part of the network that receives a single patch, and outputs the
"signature" of the landmark image; and the SNS (same-not-same)
network, which may be the part that receives the two different
signatures and outputs a scalar (e.g., the score).
[0563] The signature of a landmark may be attached to its location
on the map. When a rectangle candidate for landmark is observed,
its signature may be computed using the Signature-network. Then the
two signatures, the one from the map and the one from the current
landmark are fed into the SNS network. If the output score of the
SNS network is negative, it may indicate that the landmark in the
captured image is not the same as the one stored in the map. If the
output score of the SNS network is positive, it may indicate that
the landmark in the captured image is the same as the one stored in
the map.
[0564] The signatures may require small storage space. For example,
the signatures may use 32 bytes (although other size, such as 10
bytes, 20 bytes, 50 bytes, etc., may also be used). Such
small-sized signatures may also enable transmission on low
bandwidth communication channels.
[0565] Signatures may be associated with other sizes. There may be
a tradeoff between the length (hence the size) of the signature and
the discrimination ability of the algorithm. Smaller size may give
a higher error rate, whereas larger signature may give less error.
Since the disclosed systems may limit the discrimination
requirements to landmark signatures from the same geographic
location, the signature size may be more compact.
[0566] An example use of landmarks in an autonomous navigation
system included in a vehicle is provided below. A camera provided
on the vehicle may detect a landmark candidate, e.g., a rectangular
sign. A processor (provided on the vehicle or on a remote server)
may scale the rectangular sign to a standard size (for example
32.times.32 pixels). The processor may compute a signature (for
example using a system, such as a neural network trained on example
data). The processor may compare the computed signature to a
signature stored in the map. If signatures match then the processor
may obtain the size of the landmark size from the map. The
processor may also estimate a distance from the vehicle to the
landmark based on the landmark size and/or vehicle motion data
(e.g., speed, translation and/or rotation data). The processor may
use the distance from the landmark to localize the position of the
vehicle along the road or path (e.g., along a target trajectory
stored in the map).
[0567] The disclosed systems and methods may detect typical street
structures such as lampposts. The system may take into account both
the local shape of the lamppost and the arrangement of the lamppost
in the scene: lampposts are typically at the side of the road (or
on the divider), lampposts often appear more than once in a single
image and at different sizes, Lampposts on highways may have fixed
spacing based on country standards (e.g., around 25 m to 50 m
spacing). The disclosed systems may use a convolutional neural
network algorithm to classify a constant strip from the image
(e.g., 136.times.72 pixels) that may be sufficient to catch almost
all the street poles. The network may not contain any affine
layers, and may only be composed of convolution layers, Max Pooling
vertical layers and ReLu layers. The network's output dimension may
be 3 times of the strip width, these three channels may have 3
degrees of freedom for each column in the strip. The first degree
of freedom may indicate whether there is a street pole in this
column, the second degree of freedom may indicate this pole's top,
and the third degree of freedom may indicate its bottom. With the
network's output results, the system may take all the local
maximums that are above a threshold, and built rectangles bounding
the poles.
[0568] After the system obtains the initial rectangles, the system
may use two alignment neural networks and one filter neural
network, and algorithms to track these poles including optical flow
and Kalman Filter. Poles that were detected multiple times and
tracked well were given a higher confidence.
[0569] This disclosure introduces an idea related to landmark
definition within the context of camera (or vehicle) localization
in urban scenarios. Some landmarks such as traffic signs tend to be
quite common and one single landmark of this sort may not be
uniquely identified unless the GPS localization is good. However a
sequence of common landmarks may be quite unique and may give
localization even when GPS signal is poor as in "urban canyons." An
urban area with high buildings may cause satellites signal to
reflect, hence causing poor GPS signals. On the other hand, an
urban area is crowded with landmarks of all kinds and sorts. Hence
a camera may be used to self-localize, using visual landmarks.
Certain landmarks, however, may be seen repeatedly along the path
or trajectory, making it hard to match the landmark to a concrete
location on the map. A "yield" sign may be common in urban
scenarios. When observing just a "yield" traffic sign, the system
may not be able to use it for localization, since there are many
"yield" signs in the vicinity of the vehicle, and the system may
not be able to know which one of them is the one captured by the
camera.
[0570] The disclosed systems and methods may use any of the
following solutions. In solution one, while virtually all
localization algorithms use landmarks, the disclosed system may use
the positional arrangement of the landmarks to create a
positional-landmark. For example, the positional relation between a
plurality of landmarks appearing in the same image may be measured
by a vehicle. The configuration of the landmarks' positional
relation may be taken as a positional-landmark. Instead of just
noting the landmarks, the system may compute also the distances
among the different landmarks appearing in the same image. These
set of distances may establish a signature of the landmark
positioning with respect to each other. For example, a sequence of
landmarks detected by the vehicle may be spaced apart by 11 mm
between the first and the second landmarks, 16 mm between the
second and third landmarks, and 28 mm between the third and fourth
landmarks. In some embodiments, the specific positional arrangement
of the currently visible landmarks may be unique in the map, and
therefore, may be used as a positional-landmark for localization
purposes. Since the positional-landmarks may be unique, it may be
easy to associate them with a location on the map.
[0571] Solution two uses another way to create a unique landmark
based on landmarks is to use the sequence of the landmarks, rather
than a single landmark. For example, a sequence of landmarks may
include a stop sign, a speed limit sign, and a yield sign. While
the yield sign landmark may be abundant, and hence have little
localization value, the sequence of several landmarks may be more
unique and may lead to unambiguous localization.
[0572] In some embodiments, the above solutions may be used
together. For example, the route may be tagged with a sequence of
landmarks and the distance between them. When the GPS signal is
weak, the location and distance between landmarks may be based
primarily on odometry (e.g., based on images and/or inertial
sensors and speedometer). Multiple vehicles drive along the route
and capture landmarks and their positions along the route. The
collected information regarding the landmarks may be sent from the
vehicles to the server. The server may collate the landmark
information into landmarks sequences. Each data collecting vehicles
may give slightly different distances. The average distance or a
robust statistic such as median may be used. The variance among the
distances may also be stored in the map. The sequences of the
landmarks may be aligned taking into account possibly missing
landmarks in the recordings from some of the vehicles. The number
of times a landmark is missing gives an indication as to the
landmark visibility. The visibility of the landmark at that
position in the sequence may also be stored in the map.
[0573] When a client vehicle drives along the route, it may compare
the landmarks and distances detected by the client vehicle with the
sequences stored in the map (or alternatively received from the
server). The vehicle may match landmarks types in both sequences,
and may penalize the detected sequence for missing landmarks and
for distance errors. Landmarks that have low visibility or that
have large distance variances may be penalized less.
[0574] The camera for detecting landmarks may be augmented any
distance measurement apparatus, such as laser or radar.
[0575] It is possible that vehicles traveling along a route may
record two different sequences. For example, of 50 vehicles
traveling along a route, 20 of them may report a sequence of "star,
star, square, circle, star" (where "star," "square," "circle" may
each represent a certain type of sign) with consistent distances,
whereas the other 30 of them may report: "star, star, square,
square, triangle" with consistent distances and where the first
three "star, star, square" have consistent distance with the other
20 vehicles. This may indicate that there is some interesting road
feature such as an intersection or road split.
[0576] Refining Landmark Positions
[0577] While the models used for steering in the system need not be
globally accurate, consistent with disclosed embodiments, global
localization may be useful for navigation systems. For example,
global coordinates may be useful as an index to determine which
local map may be relevant for navigation along a particular road
segment or to differentiate one similar landmark from another
(e.g., a speed limit sign located near milepost 45 versus a similar
speed limit sign located at milepost 68). Global coordinates may be
assigned to landmarks in the model by first determining, based on
image analysis, a location of a particular landmark relative to a
host vehicle. Adding these relative coordinates to the host
vehicle's global position may define the global position of the
landmark. This measurement, however, may be no more accurate than
the measured position of the host vehicle based on the standard
automotive Global Navigation Satellite System (GNSS) receiver.
Thus, while such position determination may be sufficient for
indexing purposes, the disclosed navigational techniques described
in detail in later sections rely upon landmarks to determine a
current position of a vehicle relative to a target trajectory for a
road segment. Usage of landmarks for this purpose may require more
accurate position information for the landmarks than a GPS based
measurement can provide. For example, if a GPS measurement is
accurate only to +-5 meters, then the position determination
relative to a target trajectory could be incorrect by 5 or meters,
which may be unsuitable for enabling the vehicle to follow a target
trajectory.
[0578] One solution would be to survey the landmarks associated
with a road segment and define highly accurate positions for those
landmarks in global coordinates. Such a method, however, would be
prohibitively costly in time and money. As another approach to
refining the accuracy of a determined landmark position (to a level
sufficient to serve as a global localization reference for the
disclosed methods of autonomous vehicle navigation), multiple
measurements of the landmark position may be made, and the multiple
measurements may be used to refine the determined position of the
landmark. The multiple measurements may be obtained by passing
vehicles equipped to determine a position of the landmark relative
to GPS positions for the vehicles obtained as the vehicles pass by
the landmark.
[0579] FIGS. 22 and 23 each show an example system for identifying
a landmark for use in autonomous vehicle navigation. The systems
may also determine a location or position of the landmark. In the
embodiment shown in FIG. 22, the system may include server 2230,
which may be configured to communicate with a plurality of vehicles
(e.g., vehicles 2201 and 2202) travelling on road segment 2200.
Along the road segment 2200, there may be one or more landmarks.
The landmarks may include at least one of a traffic sign, an arrow,
a lane marking, a dashed lane marking, a traffic light, a stop
line, a directional sign, a landmark beacon, or a lamppost. For
illustration, FIG. 22 shows two landmarks 2205 and 2206. Server
2230 may receive data collected by vehicles 2201 and 2202,
including landmarks (e.g., 2205 and 2206) recognized by vehicles
2201 and 2202. Data collected by vehicles 2201 and 2202 regarding
landmarks may include position data (e.g., location of the
landmarks), physical size of the landmarks, distances between two
sequentially recognized landmarks along road segment 2200, the
distance from vehicle 2201 or 2202 to a landmark (e.g., 2205 or
2206). Vehicles 2201 and 2202 may both pass landmark 2206, and may
measure positions of landmark 2206 and transmit the measured
positions to server 2230. Server 2230 may determine a refined
position of a landmark based on the measured position data of the
landmarks received from vehicles 2201 and 2202. For example, the
refined position may be an average of the measured position data
received from vehicles 2201 and 2202, which both pass and recognize
landmark 2206. The refined position of landmark 2206 may be stored
in an autonomous vehicle road navigation model or sparse map 800,
along with an identifier (e.g., a type, size, condensed signature)
of landmark 2206. The target position of landmark 2206 may be used
by other vehicles later traveling along road segment 2200 to
determine their location along a target trajectory associated with
road segment 2200, which may be stored in the model or sparse map.
A refined position of a recognized landmark (e.g., one that has
been included in sparse map 800) may be updated or further refined
when server 2230 receives new measured position data from other
vehicles relative to the recognized landmark.
[0580] In the embodiment shown in FIG. 23, the system may utilize
one of the autonomous vehicles as a hub vehicle to perform some or
all of the functions performed by the remote server 2230 shown in
FIG. 22, and therefore, may not include a server. The hub vehicle
may communicate with other autonomous vehicles and may receive data
from other vehicles. The hub vehicle may perform functions related
to generating a road model, an update to the model, a sparse map,
an update to the sparse map, a target trajectory, etc. In some
embodiments, the hub vehicle may also determine a refined position
of a landmark stored in a model or sparse map based on multiple
positions measured by multiple vehicles traversing road segment
2200.
[0581] For example, in the embodiment shown in FIG. 23, vehicle
2201 may be the hub vehicle, which includes at least one processor
(e.g., processor 2310) configured to receive various data,
including measured position of landmark 2206, from vehicle 2202.
Vehicle 2201 may determine a refined position of landmark 2206
based on the measured position data received from vehicle 2202, and
other previously received measured position data from vehicles
previously passed and recognized landmark 2206. The refined
position of landmark 2206 may be stored within the road model or
sparse map 800. The refined position may be updated or refined when
vehicle 2201 receives new measured position data from other
vehicles regarding the same landmark.
[0582] FIG. 29 shows an example block diagram of a system 2900 for
determining/processing/storing a location of a landmark. System
2900 may be implemented in server 2230 or in a hub vehicle (e.g.,
2201). System 2900 may include a memory 2910. Memory 2910 may be
similar to other memories disclosed in other embodiments. For
example, memory 2910 may be a non-transitory flash memory. Memory
2910 may store data such as computer codes or instructions, which
may be executed by a processor. System 2900 may include a storage
device 2920. Storage device 2920 may include one or more of a hard
drive, a compact disc, a magnetic tape, etc. Storage device 2920
may be configured to store data, such as sparse map 800, an
autonomous vehicle road navigation model, road profile data,
landmarks information, etc. System 2900 may include at least one
processor 2930 configured to execute various codes or instructions
to perform one or more disclosed methods or processes. Processor
2930 may be similar to any other processors disclosed in other
embodiments. Processor 2930 may include both hardware components
(e.g., computing circuits) and software components (e.g., software
codes). System 2900 may also include a communication unit 2940
configured to communicate with autonomous vehicles via wireless
communications, such as wireless internet, cellular communications
network, etc.
[0583] FIG. 30 shows an example block diagram of memory 2910
included in system 2900. Memory 2910 may store computer code or
instructions for performing one or more operations for determining
a location or position of a landmark for use in autonomous vehicle
navigation. As shown in FIG. 30, memory 2910 may store one or more
modules for performing the operations for determining the location
of a landmark.
[0584] For example, memory 2910 may include a landmark
identification module 3010 and a landmark position determination
module 3020. Memory 2910 may also include a landmark position
refining module 3030. A processor (e.g., processor 2930) may
execute the modules to perform various functions defined by the
instructions or codes included within the modules.
[0585] For example, when executed by a processor, the landmark
identification module 3010 may cause the processor to identify a
landmark from an image captured by a camera provided on a vehicle.
In some embodiments, the processor may acquire at least one
environmental image associated with a host vehicle from a camera
installed on the host vehicle. The processor may analyze the at
least one environmental image to identify the landmark in the
environment of the host vehicle. The processor may identify a type
of the landmark, a physical size of the landmark, and/or a
condensed signature of the landmark.
[0586] When executed by a processor, the landmark position
determination module 3020 may cause the processor to determine a
position of a landmark. In some embodiments, the processor may
receive global positioning system (GPS) data representing a
location of the host vehicle, analyze the environmental image to
determine a relative position of the identified landmark with
respect to the host vehicle (e.g., a distance from the vehicle to
the landmark). The processor may further determine a globally
localized position of the landmark based on at least the GPS data
and the determined relative position. This globally localized
position may be used as the location of the landmark and stored in
the model or map.
[0587] When executed by a processor, the landmark position refining
module 3030 may refine a position determined by module 3020. In
some embodiments, the processor may receive multiple positions
relating to the same landmark from multiple vehicles in multiple
drive, or may measure the positions of the same landmark by the
same vehicle in multiple drives. The multiple positions may be used
to refine a position of the landmark already stored in the map. For
example, the processor may calculate an average of the multiple
positions, or a median value of the multiple positions and use that
(average or median) to update the position of the landmark stored
in the map. As another example, whenever the processor receives a
new position measured by a new vehicle identifying the same
landmark, the new position may be used to update the position of
the landmark already stored in the map.
[0588] Various methods may be used to determine the relative
position of the identified landmark with respect to the vehicle
based on the analysis of one or more images captured by a camera
provided on the vehicle. For example, FIG. 31 shows a method for
determining a relative position of the landmark to the host vehicle
(or a distance from the host vehicle to the landmark) based on a
scale associated with one or more images of the landmark. In this
example, camera 122 provided on vehicle 2201 may capture an image
3100 of the environment in front of vehicle 2201. The environment
may include landmark 3130, which is a speed limit sign, as
represented by the circle with number "70." The focus of expansion
is indicated by number 3120. Camera 122 may capture a plurality of
images of the environment, such as a sequence of images. The speed
limit sign 3130 may appear in a first image at the location
indicated by time t1. The speed limit sign 3130 may appear in a
second image captured after the first image at the location
indicated by time t2. The distance between the first location (at
time t1) to the focus of expansion 3120 is indicated by r, and the
distance between the first and second locations of the speed limit
sign 3130 is indicated by d. The distance from vehicle 2201 to
landmark 3130 may be calculated by Z=V*(t2-t1)*r/d, where V is the
speed of vehicle 2201, and Z is the distance from vehicle 2201 to
landmark 3130 (or the relative position from the landmark 3130 to
vehicle 2201).
[0589] FIG. 32 illustrates a method for determining the relative
position of the landmark with respect to the host vehicle (or a
distance from the vehicle to the landmark) based on an optical flow
analysis associated with a plurality of images of the environment
within a field of view 3200. For example, camera 122 may capture a
plurality of images of the environment in front of vehicle 2201.
The environment may include a landmark. A first image of the
landmark (represented by the smaller bold rectangle) is referenced
as 3210, and a second image of the landmark (represented by the
larger bold rectangle) is referenced as 3220. An optical flow
analysis may analyze two or more images of the same object, and may
derive an optical flow field 3230, as indicated by the field of
arrows. The first focus of expansion is referenced by number 3240,
and the second focus of expansion is referenced by number 3250. In
some embodiments, the optical flow analysis may determine a time to
collision (TTC) based on a rate of expansion derived from the
optical flow of the images. The distance from the vehicle to the
landmark may be estimated based on the time to collision and the
speed of the vehicle.
[0590] FIG. 33A is a flowchart showing an example process 3300 for
determining a location of a landmark for use in navigation of an
autonomous vehicle. Process 3300 may be performed by processor
2930, which may be included in a remote server (e.g., server 2230),
or on an autonomous vehicle (e.g., vehicle 2301). Process 3300 may
include receiving a measured position of a landmark (step 3310).
For example, processor 2930 may receive a measured position of
landmark 2206 from vehicle 2202. Vehicle 2202 may measure the
position of the landmark 2206 based on the GPS data indicating the
location of the vehicle, a relative position of the landmark with
respect to vehicle 2202 determined from analysis of one or more
images of an environment of vehicle 2202 including the landmark.
Process 3000 may include determining a refined position of the
landmark based on the measured position and at least one previously
acquired position of the landmark (step 3320). For example,
processor 2930 may average the measured position with the at least
one previously acquired position of the landmark, such as one or
more previously acquired positions received from other vehicles
that identified the landmark. In some embodiments, processor 2930
may average the measured position with a position stored in a map
(e.g., sparse map 800) that is determined based on at least one
previously acquired position of the landmark. Processor 2930 may
use the averaged position as the refined position. In some
embodiments, processor 2930 may calculate a median value of the
measured position and the at least one previously acquired position
(e.g., a plurality of previously acquired positions), and use the
median value as the refined position. Other statistical parameters
that may be obtained from the measured position and the plurality
of previously acquired positions may be used as the target
position. Process 3000 may update the location of the landmark
stored in a map with the refined position (step 3330). For example,
processor 2930 may replace the position stored in the map with the
refined position. When new position data is received, processor
2930 may repeat steps 3320 and 3330 to refine the position of the
landmark stored in the map, thereby increasing the accuracy of the
position of the landmark.
[0591] FIG. 33B is a flowchart showing an example process 3350 for
measuring the position of a landmark. Process 3350 may be performed
by processor 2930, which may be provided in server 2230 or the
autonomous vehicles (e.g., vehicles 2201, 2202, 2301, and 2302). A
previously acquired position stored in a map may also be obtained
using process 3350. Process 3350 may include acquiring an
environmental image from a camera (step 3351). For example, camera
122 provided on vehicle 2202 may capture one or more images of the
environment of vehicle 2202, which may include landmark 2206.
Processor 2930 may acquire images from camera 122. Process 3350 may
include analyzing the environmental image to identify a landmark
(step 3351). For example, processor 2930 (or processor 2210)
provided on vehicle 2202 may analyze the images to identify
landmark 2206. Process 3350 may also include receiving GPS data
from a GPS unit provided on the vehicle (step 3353). For example,
processor 2930 may receive GPS data from the GPS unit provided on
vehicle 2202. The GPS data may represent the location of vehicle
2202 (host vehicle). Process 3350 may include analyzing the
environmental image to determine a relative position of the
identified landmark with respect to the vehicle (step 3354). For
example, processor 2930 may analyze the images of the environment
to determine a relative position of the identified landmark 2206
with respect to vehicle 2202 using a suitable method. In some
embodiments, processor 2930 may analyze the images to determine the
relative position based on a scale discussed above in connection
with FIG. 31. In some embodiments, processor 2930 may analyze the
images to determine the relative position based on an optical flow
analysis of the images, as discussed above in connection with FIG.
32. Process 3350 may further include determining a globally
localized position of the landmark based on the GPS data and the
determined relative position of the landmark with respect to the
vehicle (step 3355). For example, processor 2930 may calculate the
globally localized position of the landmark by combining the
position of vehicle 2202 as indicated by the GPS data and the
relative position (or distance from vehicle 2202 to landmark 2206).
The globally localized position of the landmark may be used as the
measured position of the landmark.
[0592] The disclosed systems and methods may include other features
discussed below. The disclosed system may be capable of steering an
autonomous vehicle along a target trajectory without knowing the
precise location of the vehicle relative to a global coordinate
frame. The GPS information may have an error of greater than 10 m,
so the GPS information is primarily used to index the memory in
order to retrieve a landmark candidate or a relevant road tile. The
global localization may be determined using the visual ego motion.
In order to avoid drifts, the system may estimate the GPS location
of the landmarks by combining the GPS position of the host vehicle
and the relative position of the landmark to the host vehicle. The
global landmark location may be refined (e.g., averaged) with
location data obtained from multiple vehicles and multiple drives.
The measured position or location of the landmark may behave like a
random variable, and hence may be averaged to improve accuracy. The
GPS signals are used primarily as a key or index to a database
storing the landmarks, and do not have to have high precision for
determining the position of the vehicle. Low precision GPS data may
be used to determine the location of the vehicle, which is used to
determine the position of the landmark. Errors introduced by the
low precision GPS data may accumulate. Such errors may be fixed by
averaging the position data of the landmark from multiple
drives.
[0593] In some embodiments, for steering purpose, the GPS
coordinates may only be used to index the database. The GPS data
may not be taken into account in the computation of the steering
angle. The model including the location of the landmarks may be
transitioned to a global coordinate system. The transition may
include determining the GPS coordinates of landmarks by averaging,
concluding the GPS position of the vehicle near (globally
localized) landmarks, and extending the global localization away
from landmarks, by using the curve geometry, the location along
path, the lane assignment and the in-lane position.
[0594] Autonomous Navigation Using a Sparse Road Model
[0595] In some embodiments, the disclosed systems and methods may
use a sparse road model for autonomous vehicle navigation. For
example, the disclosed systems and methods may provide navigation
based on recognized landmarks, align a vehicle's tail for
navigation, allow a vehicle to navigate road junctions, allow a
vehicle to navigate using local overlapping maps, allow a vehicle
to navigate using a sparse map, navigate a vehicle based on an
expected landmark location, autonomously navigate a vehicle based
on road signatures, navigate a vehicle forward based on a rearward
facing camera, navigate a vehicle based on a free space
determination, and navigate a vehicle in snow. Additionally, the
disclosed embodiments provide systems and methods for autonomous
vehicle speed calibration, determining a lane assignment o of a
vehicle based on a recognized landmark location, and using super
landmarks as navigation aids when navigating a vehicle. These
systems and methods are detailed below.
[0596] Navigation Based on Recognized Landmarks
[0597] Consistent with disclosed embodiments, the system may use
landmarks, for example, to determine the position of a host vehicle
along a path representative of a target road model trajectory
(e.g., by identifying an intersection point of a relative direction
vector to the landmark with the target road model trajectory). Once
this position is determined, a steering direction can be determined
by comparing a heading direction to the target road model
trajectory at the determined position. Landmarks may include, for
example, any identifiable, fixed object in an environment of at
least one road segment or any observable characteristic associated
with a particular section of the road segment. In some cases,
landmarks may include traffic signs (e.g., speed limit signs,
hazard signs, etc.). In other cases, landmarks may include road
characteristic profiles associated with a particular section of a
road segment. In yet other cases, landmarks may include road
profiles as sensed, for example, by a suspension sensor of the
vehicle. Further examples of various types of landmarks are
discussed in previous sections, and some landmark examples are
shown in FIG. 10.
[0598] FIG. 34 illustrates vehicle 200 (which may be an autonomous
vehicle) travelling on road segment 3400 in which the disclosed
systems and methods for navigating vehicle 200 using one or more
recognized landmarks 3402, 3404 may be used. Although, FIG. 34
depicts vehicle 200 as equipped with image capture devices 122,
124, 126, more or fewer image capture devices may be employed on
any particular vehicle 200. As illustrated in FIG. 34, road segment
3400 may be delimited by left side 3406 and right side 3408. A
predetermined road model trajectory 3410 may define a preferred
path (e.g., a target road model trajectory) within road segment
3400 that vehicle 200 may follow as vehicle 200 travels along road
segment 3400. In some exemplary embodiments, predetermined road
model trajectory 3410 may be located equidistant from left side
3406 and right side 3408. It is contemplated however that
predetermined road model trajectory 3410 may be located nearer to
one or the other of left side 3406 and right side 3408 of road
segment 3400. Further, although FIG. 34 illustrates one lane in
road segment 3400, it is contemplated that road segment 3400 may
have any number of lanes. It is also contemplated that vehicle 200
travelling along any lane of road segment 3400 may be navigated
using one or more landmarks 3402, 3404 according to the disclosed
methods and systems.
[0599] Image acquisition unit 120 may be configured to acquire an
image representative of an environment of vehicle 200. For example,
image acquisition unit 120 may obtain an image showing a view in
front of vehicle 200 using one or more of image capture devices
122, 124, 126. Processing unit 110 of vehicle 200 may be configured
to detect one or more landmarks 3402, 3404 in the one or more
images acquired by image acquisition unit 120. Processing unit 110
may detect the one or more landmarks 3402, 3404 using one or more
processes of landmark identification discussed above with reference
to FIGS. 22-28. Although FIG. 34 illustrates only two landmarks
3402, 3404, it is contemplated that vehicle 200 may detect fewer
than or more than landmarks 3402, 3404 based on the images acquired
by image acquisition unit 120.
[0600] Processing unit 110 may be configured to determine positions
3432, 3434 of the one or more landmarks 3402, 3404, respectively,
relative to a current position 3412 of vehicle 200. Processing unit
110 may also be configured to determine a distance between current
position 3412 of vehicle 200 and the one or more landmarks 3402,
3404. Further, processing unit 110 may be configured to determine
one or more directional indicators 3414, 3416 of the one or more
landmarks 3402, 3404 relative to current position 3412 of vehicle
200. Processing unit 110 may be configured to determine directional
indicators 3414, 3416 as vectors originating from current position
3412 of vehicle 200 and extending towards, for example, positions
3432, 3434 of landmarks 3402, 3404, respectively.
[0601] Processing unit 110 may also be configured to determine an
intersection point 3418 of the one or more directional indicators
3414, 3416 with predetermined road model trajectory 3410. In one
exemplary embodiment as illustrated in FIG. 34, intersection point
3418 may coincide with current position 3412 of vehicle 200. This
may occur, for example, when vehicle 200 is located on
predetermined road model trajectory 3410. Although generally
vehicle 200 may be expected to be located on or very near
predetermined road model trajectory 3410, it is contemplated that
vehicle 200 may not be located on predetermined road model
trajectory 3410 as will be discussed below with respect to FIG.
35.
[0602] Processing unit 110 may be configured to determine a
direction 3420 of predetermined road model trajectory 3410 at
intersection point 3418. Processing unit 110 may determine
direction 3420 as a direction tangential to predetermined road
model trajectory 3410. In one exemplary embodiment, processing unit
110 may be configured to determine direction 3420 based on a
gradient or slope of a three-dimensional polynomial representing
predetermined road model trajectory 3410.
[0603] Processing unit 110 may also be configured to determine
heading direction 3430 of vehicle 200. As illustrated in FIG. 34,
heading direction 3430 of vehicle 200 may be a direction along
which image capture device 122 may be oriented relative to a local
coordinate system associated with vehicle 200. Processing unit 110
may be configured to determine whether heading direction 3430 of
vehicle 200 is aligned with (i.e., generally parallel to) direction
3420 of predetermined road model trajectory 3410. When heading
direction 3430 is not aligned with direction 3420 of predetermined
road model trajectory 3410 at intersection point 3418, processing
unit 110 may determine an autonomous steering action such that
heading direction 3430 of vehicle 200 may be aligned with direction
3420 of predetermined road model trajectory 3410. In one exemplary
embodiment, an autonomous steering action may include, for example,
a determination of an angle by which the steering wheel or front
wheels of vehicle 200 may be turned to help ensure that heading
direction 3430 of vehicle 200 may be aligned with direction 3420 of
predetermined road model trajectory 3410. In another exemplary
embodiment, an autonomous steering action may also include a
reduction or acceleration in a current velocity of vehicle 200 to
help ensure that heading direction 3430 of vehicle 200 may be
aligned with direction 3420 of predetermined road model trajectory
3410 in a predetermined amount of time. Processing unit 110 may be
configured to execute instructions stored in navigational response
module 408 to trigger a desired navigational response by, for
example, turning the steering wheel of vehicle 200 to achieve a
rotation of a predetermined angle. Rotation by the predetermined
angle may help align heading direction 3430 of vehicle 200 with
direction 3420.
[0604] Processing unit 110 may include additional considerations
when determining the autonomous steering action. For example, in
some exemplary embodiments, processing unit 110 may determine the
autonomous steering action based on a kinematic and physical model
of the vehicle, which may include the effects of a variety of
possible autonomous steering actions on the vehicle or on a user of
vehicle 200. Processing unit 110 may implement a selection criteria
for selecting at least one autonomous steering action from the
plurality of autonomous steering actions. In other exemplary
embodiments, processing unit 110 may determine an autonomous
steering action based on a "look ahead" operation, which may
evaluate portions of road segment 3400 located in front of current
location 3418 of vehicle 200. Processing unit 110 may determine an
effect of one or more autonomous steering actions on the behavior
of vehicle 200 or on a user of vehicle 200 at a location in front
of current location 3418, which may be caused by the one or more
autonomous steering actions. In yet other exemplary embodiments,
processing unit 110 may further account for the presence and
behavior of one or more other vehicles in the vicinity of vehicle
200 and a possible (estimated) effect of one or more autonomous
steering actions on such one or more other vehicles. Processing
unit 110 may implement the additional considerations as overrides.
Thus, for example, processing unit 110 may initially determine an
autonomous steering action that may help ensure that heading
direction 3430 of vehicle 200 may be aligned with direction 3420 of
predetermined road model trajectory 3410 at current location 3418.
When processing unit 110 determines that the determined autonomous
steering does not comply with one or more constraints imposed by
the additional considerations, processing unit 110 may modify the
autonomous steering action to help ensure that all the constraints
may be satisfied.
[0605] Image acquisition unit 120 may repeatedly acquire an image
of the environment in front of vehicle 200, for example, after a
predetermined amount of time. Processing unit 110 may also be
configured to repeatedly detect the one or more landmarks 3402,
3404 in the image acquired by image acquisition unit 120 and
determine the autonomous steering action as discussed above. Thus,
image acquisition unit 120 and processing unit 110 may cooperate to
navigate vehicle 200 along road segment 3400 using one or more
landmarks 3402, 3404.
[0606] FIG. 35 illustrates another vehicle 200 travelling on road
segment 3400 in which the disclosed systems and methods for
navigating vehicle 200 using one or more recognized landmarks 3402,
3404 may be used. Unlike FIG. 34, vehicle 200 of FIG. 35 is not
located on predetermined road model trajectory 3410. As a result,
as illustrated in FIG. 35, intersection point 3418 of directional
indicator 3416 may not coincide with current position 3412 of
vehicle 200.
[0607] As discussed above with respect to FIG. 34, processing unit
110 may be configured to determine a direction 3420 of
predetermined road model trajectory 3410 at intersection point
3418. Processing unit 110 may also be configured to determine
whether heading direction 3430 of vehicle 200 is aligned with (i.e.
generally parallel to) direction 3420. When heading direction 3430
is not aligned with direction 3420 of predetermined road model
trajectory 3410 at intersection point 3418, processing unit 110 may
determine a first autonomous steering action such that heading
direction 3430 of vehicle 200 may be aligned with direction 3420 of
predetermined road model trajectory 3410. For example, as
illustrated in FIG. 35, processing unit 110 may determine the first
autonomous steering action to require a rotation by an angle to
help ensure that heading direction 3430 of vehicle 200 may be
aligned with direction 3420.
[0608] In addition, when current position 3412 of vehicle 200 is
not located on predetermined road model trajectory 3410, processing
unit 120 may determine a second autonomous steering action to help
ensure that vehicle 200 may move from current position 3412 to
intersection point 3418 on predetermined road model trajectory
3410. For example, as illustrated in FIG. 35, processing unit 110
may determine a distance "d" by which vehicle 200 must be
translated to move current position 3412 to coincide with
intersection point 3418 on predetermined road model trajectory
3410. Although not illustrated in FIG. 35, processing unit 110 may
also be configured to determine a rotation that may be required to
help ensure that vehicle 200 may move from current position 3412 to
intersection point 3418 on predetermined road model trajectory
3410. Processing unit 110 may be configured to execute instructions
stored in navigational response module 408 to trigger a desired
navigational response corresponding to the first autonomous
steering action, the second autonomous steering action, or some
combination of the first and the second autonomous steering
actions. In some embodiment, processing unit 110 may execute
instructions to trigger a desired navigational response
corresponding to the first autonomous steering action and the
second autonomous steering action sequentially in any order.
[0609] FIG. 36 is a flowchart showing an exemplary process 3600,
for navigating vehicle 200 along road segment 3400, using one or
more landmarks 3402, 3404, consistent with disclosed embodiments.
Steps of process 3600 may be performed by one or more of processing
unit 110 and image acquisition unit 120, with or without the need
to access memory 140 or 150. The order and arrangement of steps in
process 3600 is provided for purposes of illustration. As will be
appreciated from this disclosure, modifications may be made to
process 3600 by, for example, adding, combining, removing, and/or
rearranging the steps for the process.
[0610] As illustrated in FIG. 36, process 3600 may include a step
3602 of acquiring an image representative of an environment of the
vehicle. In one exemplary embodiment, image acquisition unit 120
may acquire one or more images of an area forward of vehicle 200
(or to the sides or rear of a vehicle, for example). For example,
image acquisition unit 120 may obtain an image using image capture
device 122 having a field of view 202. In other exemplary
embodiments, image acquisition unit 120 may acquire images from one
or more of image capture devices 122, 124, 126, having fields of
view 202, 204, 206. Image acquisition unit 120 may transmit the one
or more images to processing unit 110 over a data connection (e.g.,
digital, wired, USB, wireless, Bluetooth, etc.).
[0611] Process 3600 may also include a step 3604 of identifying one
or more landmarks 3402, 3404 in the one or more images. Processing
unit 110 may receive the one or more images from image acquisition
unit 120. Processing unit 110 may execute monocular image analysis
module 402 to analyze the plurality of images at step 3604, as
described in further detail in connection with FIGS. 5B-5D. By
performing the analysis, processing unit 110 may detect a set of
features within the set of images, for example, one or more
landmarks 3402, 3404. Landmarks 3402, 3404 may include one or more
traffic signs, arrow markings, lane markings, dashed lane markings,
traffic lights, stop lines, directional signs, reflectors, landmark
beacons, lampposts, a change is spacing of lines on the road, signs
for businesses, and the like.
[0612] In some embodiments, processing unit 110 may execute
monocular image analysis module 402 to perform multi-frame analysis
on the plurality of images to detect landmarks 3402, 3404. For
example, processing unit 110 may estimate camera motion between
consecutive image frames and calculate the disparities in pixels
between the frames to construct a 3D-map of the road. Processing
unit 110 may then use the 3D-map to detect the road surface, as
well as landmarks 3402, 3404. In another exemplary embodiment,
image processor 190 of processing unit 110 may combine a plurality
of images received from image acquisition unit 120 into one or more
composite images. Processing unit 110 may use the composite images
to detect the one or more landmarks 3402, 3404.
[0613] In some embodiments, processing unit 110 may be able to
recognize various attributes of objects that may qualify as
potential landmarks. This information may be uploaded to a server,
for example, remote from the vehicle. The server may process the
received information and may establish a new, recognized landmark
within sparse data map 800, for example. It may also be possible
for the server to update one or more characteristics (e.g., size,
position, etc.) of a recognized landmark already included in sparse
data map 800.
[0614] In some cases, processing unit 110 may receive information
from a remote server that may aid in locating recognized landmarks
(e.g., those landmarks that have already been identified and
represented in sparse data map 800). For example, as a vehicle
travels along a particular road segment, processor 110 may access
one or more local maps corresponding to the road segment being
traversed. The local maps may be part of sparse data map 800 stored
on a server located remotely with respect to the vehicle, and the
one or more local maps may be wirelessly downloaded as needed. In
some cases, the sparse map 800 may be stored locally with respect
to the navigating vehicle. The local maps may include various
features associated with a road segment. For example, the local
maps may include a polynomial spline representative of a target
trajectory that the vehicle should follow along the road segment.
The local maps may also include representations of recognized
landmarks. In some cases, as previously described, the recognized
landmarks may include information such as a landmark type,
position, size, distance to another landmark, or other
characteristics. In the case of non-semantic signs (e.g., general
signs not necessarily associated with road navigation), for
example, the information stored in sparse data map 800 may include
a condensed image signature associated with the non-semantic road
sign.
[0615] Such information received from sparse data map 800 may aid
processor unit 110 in identifying recognized landmarks along a road
segment. For example, processor unit 110 may determine based on its
current position (determined, for example, based on GPS data, dead
reckoning relative to a last determined position, or any other
suitable method) and information included in a local map (e.g., a
localized position of the next landmark to be encountered and/or
information indicating a distance from the last encountered
landmark to the next landmark) that a recognized landmark should be
located at a position approximately 95 meters ahead of the vehicle
and 10 degrees to the right of a current heading direction.
Processor unit 110 may also determine from the information in the
local map that the recognized landmark is of a type corresponding
to a speed limit sign and that the sign has a rectangular shape of
about 2 feet wide by 3 feet tall.
[0616] Thus, when processor unit 110 receives images captured by
the onboard camera, those images may be analyzed by searching for
an object at the expected location of a recognized landmark from
sparse map 800. In the speed limit sign example, processor unit 110
may review captured images and look for a rectangular shape at a
position in the image 10 degrees to the right of a heading
direction of the vehicle. Further, the processor may look for a
rectangular shape occupying a number of pixels of the image that a
2 foot by 3 foot rectangular sign would be expected to occupy at a
relative distance of 95 meters. Upon identifying such an object in
the image, where expected, the processor may develop a certain
confidence level that the expected recognized landmark has been
identified. Further confirmation may be obtained, for example, by
analyzing the image to determine what text or graphics appear on
the sign in the captured images. Through textual or graphics
recognition processes, the processor unit may determine that the
rectangular shape in the captured image includes the text "Speed
Limit 55." By comparing the captured text to a type code associated
with the recognized landmark stored in sparse data map 800 (e.g., a
type indicating that the next landmark to be encountered is a speed
limit sign), this information can further verify that the observed
object in the captured images is, in fact, the expected recognized
landmark.
[0617] Process 3600 may include a step 3606 of determining a
current position 3412 of vehicle 200 relative to a target
trajectory. Processing unit 110 may determine current position 3412
of vehicle 200 in many different ways. For example, processing unit
110 may determine current position 3412 based on signals from
position sensor 130, for example, a GPS sensor. In another
exemplary embodiment, processing unit 110 may determine current
position 3412 of vehicle 200 by integrating a velocity of vehicle
200 as vehicle 200 travels along predetermined road model
trajectory 3410. For example, processing unit 110 may determine a
time "t" required for vehicle 200 to travel between two locations
on predetermined road model trajectory 3410. Processing unit 110
may integrate the velocity of vehicle 200 over time t to determine
current position 3412 of vehicle 200 relative to the two locations
on predetermined road model trajectory 3410.
[0618] Once a recognized landmark is identified in a captured
image, predetermined characteristics of the recognized landmark may
be used to assist a host vehicle in navigation. For example, in
some embodiments, the recognized landmark may be used to determine
a current position of the host vehicle. In some cases, the current
position of the host vehicle may be determined relative to a target
trajectory from sparse data model 800. Knowing the current position
of the vehicle relative to a target trajectory may aid in
determining a steering angle needed to cause the vehicle to follow
the target trajectory (for example, by comparing a heading
direction to a direction of the target trajectory at the determined
current position of the vehicle relative to the target
trajectory).
[0619] A position of the vehicle relative to a target trajectory
from sparse data map 800 may be determined in a variety of ways.
For example, in some embodiments, a 6D Kalman filtering technique
may be employed. In other embodiments, a directional indicator may
be used relative to the vehicle and the recognized landmark. For
example, process 3600 may also include a step 3608 of determining
one or more directional indicators 3414, 3416 associated with the
one or more landmarks 3402, 3404, respectively. Processing unit 110
may determine directional indicators 3414, 3416 based on the
relative positions 3432, 3434 of the one or more landmarks 3402,
3404, respectively, relative to current position 3412 of vehicle
200. For example, processing unit 110 may receive landmark
positions 3432, 3434 for landmarks 3402, 3404, respectively, from
information, which may be stored in one or more databases in memory
140 or 150. Processing unit 110 may also determine distances
between current position 3412 of vehicle 200 and landmark positions
3432, 3434 for landmarks 3402, 3404, respectively. In addition,
processing unit 110 may determine directional indicator 3414 as a
vector extending from current position 3412 of vehicle 200 and
extending along a straight line passing through current position
3412 and landmark position 3432. Likewise, processing unit 110 may
determine directional indicator 3416 as a vector extending from
current position 3412 of vehicle 200 and extending along a straight
line passing through current position 3412 and landmark position
3434. Although two landmarks 3402, 3404 are referenced in the above
discussion, it is contemplated that processing unit 110 may
determine landmark positions 3432, 3434, distances between current
position 3412 and landmark positions 3402, 34014, and directional
indicators 3414, 3416 for fewer than or more than landmarks 3402,
3404.
[0620] Process 3600 may include a step 3610 of determining an
intersection point 3418 of directional indicator 3416 with
predetermined road model trajectory 3410. Processing unit 110 may
determine a location of intersection point 3418 at which
predetermined road model trajectory 3410 intersects with a straight
line extending between current position 3412 of vehicle 200 and
landmark position 3434. Processing unit 110 may obtain a
mathematical representation of predetermined road model trajectory
3410 from information stored in memories 140, 150. Processing unit
110 may also generate a mathematical representation of a straight
line passing through both current position 3412 of vehicle 200 and
landmark position 3434 of landmark 3404. Processing unit 110 may
use the mathematical representation of predetermined road model
trajectory 3410 and the mathematical representation of a straight
line extending between current position 3412 and landmark position
3434 to determine a location of intersection point 3418.
[0621] In one exemplary embodiment as illustrated in FIG. 34,
intersection point 3418 may coincide with current position 3412 of
vehicle 200 (e.g., a position of a point of reference, which may be
arbitrarily assigned, associated with the vehicle). This may
happen, for example, when vehicle 200 is located on predetermined
road model trajectory 3410. In another exemplary embodiment as
illustrated in FIG. 35, intersection point 3418 may be separated
from current position 3412. Processing unit 110 may detect that
vehicle 200 is not located on predetermined road model trajectory
3410 by comparing a first distance "D.sub.1" (see, e.g., FIG. 35)
between current position 3412 and landmark position 3434 with a
second distance "D.sub.2" between intersection point 3418 and
landmark position 3434.
[0622] When intersection point 3418 is separated from current
position 3412 of vehicle 200, processing unit 110 may determine an
amount of translation and/or rotation that may be required to help
move vehicle 200 from current position 3412 to intersection point
3418 on predetermined road model trajectory 3410. In some exemplary
embodiments, processing unit 110 may execute navigation module 408
to cause one or more navigational responses in vehicle 200 based on
the analysis performed at step 520 and the techniques as described
above in connection with FIG. 4. For example, processing unit 110
may issue commands to steering system 240 to move vehicle 200 so
that a current position 3412 of vehicle 200 may coincide with
intersection point 3418.
[0623] Process 3600 may include a step 3612 of determining
direction 3420 of predetermined road model trajectory 3410 at
intersection point 3418. In one exemplary embodiment, processing
unit 110 may obtain a mathematical representation (e.g.
three-dimensional polynomial) of predetermined road model
trajectory 3410. Processing unit 110 may determine direction 3420
as a vector oriented tangentially to predetermined road model
trajectory 3410 at intersection point 3418. For example, processing
unit 110 may determine direction 3420 as a vector pointing along a
gradient of the mathematical representation of predetermined road
model trajectory 3410 at intersection point 3418.
[0624] Process 3600 may also include a step 3614 of determining an
autonomous steering action for vehicle 200. In one exemplary
embodiment, processing unit 110 may determine a heading direction
3430 of vehicle 200. For example, as illustrated in FIGS. 34 and
35, processing unit 110 may determine heading direction 3430 of
vehicle 200 as the direction in which image capture device 122 may
be oriented relative to a local coordinate system associated with
vehicle 200. In another exemplary embodiment, processing unit 200
may determine heading direction 3430 as the direction of motion of
vehicle 200 at current position 3412. Processing unit 110 may also
determine a rotational angle between heading direction 3430 and
direction 3420 of predetermined road model trajectory 3410.
Processing unit 110 may execute the instructions in navigational
module 408 to determine an autonomous steering action for vehicle
200 that may help ensure that heading direction 3430 of vehicle 200
is aligned (i.e., parallel) with direction 3420 of predetermined
road model trajectory 3410 at intersection point 3418. Processing
unit 110 may also send control signals to steering system 240 to
adjust rotation of the wheels of vehicle 200 to turn vehicle 200 so
that heading direction 3430 may be aligned with direction 3420 of
predetermined road model trajectory 3410 at intersection point
3418. In one exemplary embodiment, processing unit 110 may send
signals to steering system 240 to adjust rotation of the wheels of
vehicle 200 to turn vehicle 200 until a difference between heading
direction 3430 and direction 3420 of predetermined road model
trajectory 3410 at intersection point 3418 may be less than a
predetermined threshold value.
[0625] Processing unit 110 and/or image acquisition unit 120 may
repeat steps 3602 through 3614 after a predetermined amount of
time. In one exemplary embodiment, the predetermined amount of time
may range between about 0.5 seconds to 1.5 seconds. By repeatedly
determining intersection point 3418, heading direction 3430,
direction 3420 of predetermined road model trajectory 3410 at
intersection point 3418, and the autonomous steering action
required to align heading direction 3430 with direction 3420,
processing unit 110 and/or image acquisition unit 120 may help to
navigate vehicle 200, using the one or more landmarks 3402, 3404,
so that vehicle 200 may travel along road segment 3400.
[0626] Tail Alignment Navigation
[0627] Consistent with disclosed embodiments, the system can
determine a steering direction for a host vehicle by comparing and
aligning a traveled trajectory of the host vehicle (the tail) with
a predetermined road model trajectory at a known location along the
road model trajectory. The traveled trajectory provides a vehicle
heading direction at the host vehicle location, and the steering
direction can be obtained, relative to the heading direction, by
determining a transformation (e.g., rotation and potentially
translation) that minimizes or reduces an error between the
traveled trajectory and the road model trajectory at the known
location of the vehicle along the road model trajectory.
[0628] Tail alignment is a method of aligning an autonomous
vehicle's heading with a pre-existing model of the path based on
information regarding the path over which the vehicle has already
travelled. Tail alignment uses a tracked path of the autonomous
vehicle over a certain distance (hence the "tail"). The tracked
path is a representation of the path over which the autonomous
vehicle has already travelled in order to reach a current location
of the autonomous vehicle. For example, the tracked path may
include a predetermined distance (e.g. 60 m or other desired
length) of the path behind the autonomous vehicle over which the
autonomous vehicle travelled to reach its current location. The
tracked path may be compared with the model to determine, for
example, a heading angle of the autonomous vehicle.
[0629] In some embodiments, a rear looking camera may be used to
determine or aid in determination of the travelled path. A rear
looking camera may be useful both for modeling, heading estimation,
and lateral offset estimation. By adding a rear looking camera it
may be possible to boost the reliability of the system, since a bad
illumination situation (e.g., low sun on the horizon) rarely would
affect both front looking and rear looking cameras.
[0630] The tracked path can also optionally be combined with a
predicted path of the autonomous vehicle. The predicted path may be
generated by processing images of the environment ahead of the
autonomous vehicle and detecting lane, or other road layout,
markings. In this regard it is worth noting, that in a potential
implementation of the present disclosure, a road model may diverge
due to accumulated errors (integration of ego motion). Thus, for
example, a predicted path over a predetermined distance (e.g. 40 m)
ahead of the current location of the autonomous vehicle may be
compared with the tracked path to determine the heading angle for
the autonomous vehicle.
[0631] FIG. 37 illustrates vehicle 200 (which may be an autonomous
vehicle) travelling on road segment 3700 in which the disclosed
systems and methods for navigating vehicle 200 using tail alignment
may be used. As used here and throughout this disclosure, the term
"autonomous vehicle" refers to vehicles capable of implementing at
least one navigational change in course without driver input. To be
autonomous, a vehicle need not be fully automatic (e.g., fully
operational without a driver or without driver input). Rather, an
autonomous vehicle includes those that can operate under driver
control during certain time periods and without driver control
during other time periods. Autonomous vehicles may also include
vehicles that control only some aspects of vehicle navigation, such
as steering (e.g., to maintain a vehicle course between vehicle
lane constraints), but may leave other aspects to the driver (e.g.,
braking). In some cases, autonomous vehicles may handle some or all
aspects of braking, speed control, and/or steering of the
vehicle.
[0632] Although, FIG. 37 depicts vehicle 200 as equipped with image
capture devices 122, 124, 126, more or fewer image capture devices
may be employed on any particular vehicle 200. As illustrated in
FIG. 37, road segment 3700 may be delimited by left side 3706 and
right side 3708. A predetermined road model trajectory 3710 may
define a preferred path (i.e. a target road model trajectory)
within road segment 3700 that vehicle 200 may follow as vehicle 200
travels along road segment 3700. In some exemplary embodiments,
predetermined road model trajectory 3710 may be located equidistant
from left side 3706 and right side 3708. It is contemplated however
that predetermined road model trajectory 3710 may be located nearer
to one or the other of left side 3706 and right side 3708 of road
segment 3700. Further, although FIG. 37 illustrates one lane in
road segment 3700, it is contemplated that road segment 3700 may
have any number of lanes. It is also contemplated that vehicle 200
travelling along any lane of road segment 3400 may be navigated
using tail alignment according to the disclosed methods and
systems.
[0633] Image acquisition unit 120 may be configured to acquire a
plurality of images representative of an environment of vehicle
200, as vehicle 200 travels along road segment 3700. For example,
image acquisition unit 120 may obtain the plurality of images
showing views in front of vehicle 200 using one or more of image
capture devices 122, 124, 126. Processing unit 110 of vehicle 200
may be configured to detect a location of vehicle 200 in each of
the plurality of images. Processing unit 110 of vehicle 200 may
also be configured to determine a traveled trajectory 3720 based on
the detected locations. As used in this disclosure, the travelled
trajectory 3720 may represent an actual path taken by vehicle 200
as vehicle 200 travels along road segment 3700.
[0634] Processing unit 110 may be configured to determine a current
location 3712 of vehicle 200 based on analysis of the plurality of
images. In one exemplary embodiment as illustrated in FIG. 37, the
current location 3712 of vehicle 200 may coincide with a target
location 3714 on predetermined road model trajectory 3710. This may
occur, for example, when vehicle 200 is located on predetermined
road model trajectory 3710. Although generally vehicle 200 may be
expected to be located on or very near predetermined road model
trajectory 3710, it is contemplated that vehicle 200 may not be
located on predetermined road model trajectory 3710 as will be
discussed below with respect to FIG. 38.
[0635] Processing unit 110 may be configured to determine an
autonomous steering action for vehicle 200 by comparing the
travelled trajectory 3720 with the predetermined road model
trajectory 3710 at current location 3712 of vehicle 200. For
example, processing unit 110 may be configured to determine a
transformation (i.e., rotation and potentially translation) such
that an error between the travelled trajectory 3720 and the
predetermined road model trajectory 3710 may be reduced.
[0636] Processing unit 110 may be configured to determine a heading
direction 3730 of vehicle 200 at current location 3712. Processing
unit 110 may determine heading direction 3730 based on the
travelled trajectory 3720. For example, processing unit 110 may
determine heading direction 3730 as a gradient of travelled
trajectory 3720 at current location 3712 of vehicle 200. Processing
unit 110 may also be configured to determine steering direction
3740 as a direction tangential to predetermined road model
trajectory 3710. In one exemplary embodiment, processing unit 110
may be configured to determine steering direction 3740 based on a
gradient of a three-dimensional polynomial representing
predetermined road model trajectory 3710.
[0637] Processing unit 110 may be configured to determine whether
heading direction 3730 of vehicle 200 is aligned with (i.e.,
generally parallel to) steering direction 3740 of predetermined
road model trajectory 3710. When heading direction 3730 is not
aligned with steering direction 3740 of predetermined road model
trajectory 3710 at current location 3712 of vehicle 200, processing
unit 110 may determine an autonomous steering action such that
heading direction 3730 of vehicle 200 may be aligned with steering
direction 3740 of predetermined road model trajectory 3710.
Processing unit 110 may be configured to execute instructions
stored in navigational response module 408 to trigger a desired
navigational response by, for example, turning the steering wheel
of vehicle 200 to achieve a rotation of angle .quadrature..
Rotation by the angle .quadrature. may help align heading direction
3730 of vehicle 200 with steering direction 3740. Thus, for
example, processing unit 110 may perform tail alignment of vehicle
200 by determining the angle .quadrature. by which vehicle 200 may
turn so that heading direction 3730 of autonomous vehicle may be
aligned with steering direction 3740.
[0638] Image acquisition unit 120 may repeatedly acquire the
plurality of images of the environment in front of vehicle 200, for
example, after a predetermined amount of time. Processing unit 110
may also be configured to repeatedly determine the transformation
as discussed above. Thus, image acquisition unit 120 and processing
unit 110 may cooperate to navigate vehicle 200 along road segment
3400 using the travelled trajectory 3720 (i.e. the "tail") of
vehicle 200.
[0639] FIG. 38 illustrates another vehicle 200 travelling on road
segment 3700 in which disclosed systems and methods for navigating
vehicle 200 using tail alignment. Unlike FIG. 38, vehicle 200 of
FIG. 38 is not located on predetermined road model trajectory 3710.
As a result, as illustrated in FIG. 38, target location 3714 of
vehicle 200 may not coincide with current location 3712 of vehicle
200.
[0640] As discussed above with respect to FIG. 37, processing unit
110 may be configured to determine a steering direction 3740 of
predetermined road model trajectory 3710 at current location 3712
of vehicle 200. Processing unit 110 may determine steering
direction 3740 as the direction of the gradient of predetermined
road model trajectory 3710 at target location 3714. Processing unit
110 may also be configured to determine whether heading direction
3730 of vehicle 200 is aligned with (i.e., generally parallel to)
steering direction 3740. When heading direction 3730 is not aligned
with steering direction 3740, processing unit 110 may determine a
transformation that may include, for example, a rotation angle that
may be required to align heading direction 3730 with steering
direction 3740. In addition, the transformation may include a
translation "d" that may be required to ensure that vehicle 200 may
move from current location 3712 to target location 3714 on
predetermined road model trajectory 3710.
[0641] Processing unit 110 may be configured to determine the
transformation by comparing predetermined road model trajectory
3710 with the travelled trajectory 3720 of vehicle 200. In one
exemplary embodiment, processing unit 110 may determine the
transformation by reducing an error between predetermined road
model trajectory 3710 and travelled trajectory 3720. Processing
unit 110 may be configured to execute instructions stored in
navigational response module 408 to trigger a desired navigational
response based on the determined transformation.
[0642] FIG. 39 is a flowchart showing an exemplary process 3900,
for navigating vehicle 200 along road segment 3700, using tail
alignment, consistent with disclosed embodiments. Steps of process
3900 may be performed by one or more of processing unit 110 and
image acquisition unit 120, with or without the need to access
memory 140 or 150. The order and arrangement of steps in process
3900 is provided for purposes of illustration. As will be
appreciated from this disclosure, modifications may be made to
process 3900 by, for example, adding, combining, removing, and/or
rearranging the steps for the process.
[0643] As illustrated in FIG. 39, process 3900 may include a step
3902 of acquiring a plurality of images representative of an
environment of the vehicle. In one exemplary embodiment, image
acquisition unit 120 may acquire the plurality of images of an area
forward of vehicle 200 (or to the sides or rear of a vehicle, for
example) at multiple locations as vehicle travels along road
segment 3700. For example, image acquisition unit 120 may obtain
images using image capture device 122 having a field of view 202 at
each of locations 3752-3768 and 3712 (see FIGS. 37, 38). In other
exemplary embodiments, image acquisition unit 120 may acquire
images from one or more of image capture devices 122, 124, 126,
having fields of view 202, 204, 206 at each of locations 3752-3768
and 3712. Image acquisition unit 120 may transmit the one or more
images to processing unit 110 over a data connection (e.g.,
digital, wired, USB, wireless, Bluetooth, etc.). Images obtained by
the one or more image capture devices 122, 124, 126 may be stored
in one or more of memories 140, 150, and/or database 160.
[0644] Process 3900 may also include a step 3904 of determining
travelled trajectory 3720. Processing unit 110 may receive the one
or more images from image acquisition unit 120. Processing unit 110
may execute processes similar to those discussed with respect to
FIGS. 34-36 to identify locations 3752-3768 of vehicle 200 in the
plurality of images. For example, processing unit 110 may identify
one or more landmarks and use directional vectors of the landmarks
to determine locations 3752-3768 and current location 3712 using
the systems and methods disclosed with respect to FIGS. 34-36.
Processing unit 110 may determine travelled trajectory 3720 based
on the determined locations 3752-3768 and current location 3712 of
vehicle 200. In one exemplary embodiment, processing unit 110 may
determine travelled trajectory 3720 by curve-fitting a
three-dimensional polynomial to the determined locations 3752-3768
and current location 3712 of vehicle 200.
[0645] In some embodiments, processing unit 110 may execute
monocular image analysis module 402 to perform multi-frame analysis
on the plurality of images. For example, processing unit 110 may
estimate camera motion between consecutive image frames and
calculate the disparities in pixels between the frames to construct
a 3D-map of the road. Processing unit 110 may then use the 3D-map
to detect the road surface as well as to generate travelled
trajectory 3720 of vehicle 200.
[0646] Process 3900 may include a step 3906 of determining a
current location 3712 of vehicle 200. Processing unit 110 may
determine current location 3712 of vehicle 200 by performing
processes similar to those discussed, for example, with respect to
FIGS. 34-36 regarding navigation based on recognized landmarks. In
some exemplary embodiments, processing unit 110 may determine
current location 3712 based on signals from position sensor 130,
for example, a GPS sensor. In another exemplary embodiment,
processing unit 110 may determine current location 3712 of vehicle
200 by integrating a velocity of vehicle 200 as vehicle 200 travels
along travelled trajectory 3720. For example, processing unit 110
may determine a time "t" required for vehicle 200 to travel between
two locations 3751 and 3712 on travelled trajectory 3720.
Processing unit 110 may integrate the velocity of vehicle 200 over
time t to determine current location 3712 of vehicle 200 relative
to location 3751.
[0647] Process 3900 may also include a step 3908 of determining
whether current location 3712 of vehicle 200 is located on
predetermined road model trajectory 3710. In some exemplary
embodiments, predetermined road model trajectory 3710 may be
represented by a three-dimensional polynomial of a target
trajectory along road segment 3700. Processing unit 110 may
retrieve predetermined road model trajectory 3710 from database 160
stored in one or memories 140 and 150 included in vehicle 200. In
some embodiments, processing unit 110 may retrieve predetermined
road model trajectory 3710 from database 160 stored at a remote
location via a wireless communications interface.
[0648] Processing unit 110 may determine whether current location
3712 of vehicle 200 is located on predetermined road model
trajectory 3710, using processes similar to those discussed with
respect to FIGS. 34-37, by for example, determining a distance
between vehicle 200 and a recognized landmark. When processing unit
110 determines that current location of vehicle 200 is on
predetermined road model trajectory 3710 (see FIG. 37), processing
unit 110 may proceed to step 3912. When processing unit 110
determines, however, that current location of vehicle 200 is not on
predetermined road model trajectory 3710 (see FIG. 38), processing
unit 110 may proceed to step 3910.
[0649] In step 3910, processing unit 110 may determine a lateral
offset "d" that may help ensure that vehicle 200 may move from
current location 3712 to target location 3714 on predetermined road
model trajectory 3710. Processing unit 110 may determine lateral
offset d. In one embodiment, processing unit 110 may determine
lateral offset d by determining the left and right sides 3706,
3708. In one other exemplary embodiments, processing unit 110 may
determine a translation function needed to convert current location
3712 to target location 3714. In another embodiment, processing
unit 110 may determine the translation function by reducing the
error between current location 3712 and target location 3714. In
additional exemplary embodiments, processing unit 110 may determine
the lateral offset d by observing (using one or more onboard
cameras and one or more images captured by those cameras) left side
3706 and right side 3708 of road segment 3700. After determining
the lateral offset d, processing unit may proceed to step 3912.
[0650] Process 3900 may include a step 3912 of determining heading
direction 3730 of vehicle 200, and possibly a correction to the
current location 3712 computed in step 3906. In one exemplary
embodiment, processing unit 110 may determine heading direction
3730 and a correction to location 3712 by aligning the travelled
trajectory 3720 at current location 3712 with the model trajectory
3710. The alignment procedure may provide a rigid transformation
that reduces or minimizes the distance between 3720 and 3712. In
one exemplary embodiment, processing unit 110 may compute a rigid
transformation with four degrees of freedom, accounting for 3D
rotation (heading) and 1D longitudinal translation. In another
exemplary embodiment, processing unit 110 may compute a rigid
transformation with any number of parameters (degrees of freedom)
between 1 and 6. After alignment, processing unit 110 may determine
the predicted location 3774 (see FIGS. 37, 38) of vehicle 200 after
time "t" based on a current velocity of vehicle 200 and the
geometry of the model trajectory 3710.
[0651] In other exemplary embodiments, in step 3912, processing
unit 110 may determine heading direction 3730 of vehicle 200, and
possibly a correction to the current location 3712 computed in step
3906. For example, processing unit 110 may determine heading
direction 3730 and improved location 3712 by aligning the travelled
trajectory 3720 at current location 3712 with the model trajectory
3710. The alignment procedure may find a rigid transformation that
minimizes the distance between 3720 and 3712. In one exemplary
embodiment, processing unit 110 may compute a rigid transformation
with four degrees of freedom, accounting for 3D rotation (heading)
and 1D longitudinal translation. In another exemplary embodiment,
processing unit 110 may compute a rigid transformation with any
number of parameters (degreed of freedom) between 1 and 6. After
alignment, processing unit 110 may determine the predicted location
3774 (see FIGS. 37, 38) of vehicle 200 after time "t" based on a
current velocity of vehicle 200 and the geometry of the model
trajectory 3710.
[0652] In yet other exemplary embodiments, processing unit 110 may
determine heading direction 3730 and a location 3712 as a gradient
of travelled trajectory 3720 at current location 3712 of vehicle
200. For example, processing unit 110 may obtain a slope of a
three-dimensional polynomial representing travelled trajectory 3720
to determine heading direction 3730 of vehicle 200. In another
exemplary embodiment, process 110 may project travelled trajectory
3720 forward from current location 3712. In projecting travelled
trajectory 3720, processing unit 110 may determine a predicted
location 3774 (see FIGS. 37, 38) of vehicle 200 after time "t"
based on a current velocity of vehicle 200.
[0653] Processing unit 110 may also determine predicted location
3774 of vehicle 200 after time "t" based on one of many cues. For
example, processing unit 110 may determine predicted location 3774
of vehicle 200 after time "t" based on a left lane mark polynomial,
which may be a polynomial representing left side 3706 of road
segment 3700. Thus, for example, processing unit 110 may determine
left position 3770 (see FIGS. 37, 38) on the left lane mark
polynomial corresponding to current location 3712 of vehicle 200.
Processing unit 110 may determine location 3770 by determining the
distance "D" between current location 3712 and left side 3706 based
on the left lane mark polynomial. It is contemplated that when
vehicle 200 is not located on predetermined road model trajectory
3710 (as in FIG. 38), processing unit 110 may determine distance D
as the distance between target location 3714 and left side 3706.
Processing unit 110 may also determine a location 3772 on left side
3706 after time "t" using the mathematical representation of the
left lane mark polynomial and current velocity of vehicle 200.
Processing unit 110 may determine predicted location 3774 of
vehicle 200 by laterally offsetting the determined location 3773 on
left side 3706 by distance D. In another exemplary embodiment,
processing unit 110 may determine the location of vehicle 200 after
time "t" based on a right lane mark polynomial, which may be a
polynomial representing right side 3708 of road segment 3700.
Processing unit 110 may perform processes similar to those
discussed above with respect to left lane mark polynomial to
determine predicted position 3774 of vehicle 200 based on right
lane mark polynomial.
[0654] In some exemplary embodiments, processor 110 may determine
the location of vehicle 200 after time "t" based on the trajectory
followed by a forward vehicle, which may be travelling in front of
vehicle 200. In other exemplary embodiments, processing unit 200
may determine the location of vehicle 200 after time "t" by
determining an amount of free space ahead of vehicle 200 and a
current velocity of vehicle 200. In some embodiments, processing
unit 200 may determine the location of vehicle 200 after time "t"
based on virtual lanes or virtual lane constraints. For example,
when processing unit 110 detects two vehicles travelling in front
of vehicle 200, one in each adjacent lane, processing unit 110 may
use the average lateral distance between the two vehicles in front
as a trajectory (virtual lane marker), which may be used to
determine a position of vehicle 200 after time "t." In other
embodiments, processing unit 110 may use mathematical
representations of left side 3706 (i.e. left lane mark polynomial)
and right side 3708 (i.e. right lane mark polynomial) as defining
virtual lane constraints. Processing unit 110 may determine
predicted position 3774 of vehicle 200 based on the virtual lane
constraints (i.e. based on both the left and the right lane mark
polynomials) and an estimated location of vehicle 200 from the left
and right sides 3706, 3708.
[0655] In other embodiments, processing unit 110 may determine
predicted location 3774 of vehicle 200 after time "t" based on
following a trajectory predicted using holistic path prediction
methods. In some exemplary embodiments, processing unit 110 may
determine predicted location 3774 of vehicle 200 after time "t" by
applying weights to some or all of the above-described cues. For
example, processing unit 110 may determine the location of vehicle
200 after time "t" as a weighted combination of the locations
predicted based on one or more of a left lane mark polynomial
model, a right lane mark polynomial model, holistic path
prediction, motion of a forward vehicle, determined free space
ahead of the autonomous vehicle, and virtual lanes. Processing unit
110 may use current location 3712 of vehicle 200 and predicted
location 3774 after time "t" to determine heading direction 3730
for vehicle 200.
[0656] In some embodiments, in step 3912 of process 3900,
processing unit 110 may also estimate a longitudinal offset. For
example, processing unit 110 may solve for the heading and the
offset by an alignment procedure, between the model trajectory and
the tail of vehicle 200.
[0657] Process 3900 may also include a step 3914 of determining
steering direction 3740. In one exemplary embodiment, processing
unit 110 may obtain a mathematical representation (e.g.
three-dimensional polynomial) of predetermined road model
trajectory 3710. Processing unit 110 may determine steering
direction 3740 as a vector oriented tangentially to predetermined
road model trajectory 3710 at target location 3714. For example,
processing unit 110 may determine direction 3740 as a vector
pointing along a gradient of the mathematical representation of
predetermined road model trajectory 3710 at target location
3714.
[0658] Process 3900 may also include a step 3916 of adjusting
steering system 240 of vehicle 200 based on the transformation
determined, for example, in steps 3910-3914. The required
transformation may include lateral offset d. The transformation may
further include rotation by an angle to help ensure that heading
direction 3730 of vehicle 200 may be aligned with steering
direction 3740. Although, FIGS. 37, 38 illustrate determination of
one angle between heading direction 3730 and steering direction
3740, it is contemplated that in three-dimensional space, rotation
along three angles in three generally orthogonal planes may be
required to ensure that heading direction 3730 may be aligned with
steering direction 3730. One of ordinary skill in the art would,
therefore, recognize that the transformation determined in steps
3910-3914 may include at least three rotational angles and at least
one translation (i.e. lateral offset).
[0659] Processing unit 110 may send control signals to steering
system 240 to adjust rotation of the wheels of vehicle 200 so that
heading direction 3730 may be aligned with steering direction 3740
and vehicle 200 may move from current location 3712 to target
location 3714 when vehicle 200 is located off predetermined road
model trajectory 3710. Processing unit 110 and/or image acquisition
unit 120 may repeat steps 3902 through 3916 after a predetermined
amount of time. In one exemplary embodiment, the predetermined
amount of time may range between about 0.5 seconds to 1.5 seconds.
By repeatedly determining lateral offset d and rotation angles,
processing unit 110 and/or image acquisition unit 120 may help to
navigate vehicle 200, using tail alignment, along road segment
3700.
[0660] As discussed in other sections, navigation of an autonomous
vehicle along a road segment may include the use of one or more
recognized landmarks Among other things, such recognized landmarks
may enable the autonomous vehicle to determine its current location
with respect to a target trajectory from sparse data model 800. The
current location determination using one or more recognized
landmarks may be more precise than determining a position using GPS
sensing, for example.
[0661] Between recognized landmarks, the autonomous vehicle may
navigate using a dead-reckoning technique. This technique may
involve periodically estimating a current location of the vehicle
with respect to the target trajectory based on sensed ego-motion of
the vehicle. Such sensed ego motion may enable the vehicle (e.g.,
using processing unit 110) to not only estimate the current
location of the vehicle relative to the target trajectory, but it
may also enable the processing unit 110 to reconstruct the
vehicle's travelled trajectory. Sensors that may be used to
determine the ego motion of the vehicle may include various sensors
such as, for example, onboard cameras, speedometers, and/or
accelerometers. Using such sensors, processing unit 110 may sense
where the vehicle has been and reconstruct the travelled
trajectory. This reconstructed travelled trajectory may then be
compared to the target trajectory using the tail alignment
technique described above to determine what navigational changes,
if any, are required to align the traveled trajectory at a current
location with the target trajectory at the current location.
[0662] Navigating Road Junctions
[0663] Consistent with disclosed embodiments, the system may
navigate through road junctions, which may constitute areas with
few or no lane markings. Junction navigation may include 3D
localization based on two or more landmarks. Thus, for example, the
system may rely on two or more landmarks to determine a current
location and a heading of an autonomous vehicle. Further, the
system may determine a steering action based on the determined
heading and a direction of a predetermine road model trajectory
representing a preferred path for the vehicle.
[0664] FIG. 40 illustrates vehicle 200 (which may be an autonomous
vehicle) travelling through road junction 4000 in which the
disclosed systems and methods for navigating road junctions may be
used. As illustrated in FIG. 40, vehicle 200 may be travelling
along road segment 4002, which may intersect with road segment
4004. Although road segments 4002 and 4004 appear to intersect at
right angles in FIG. 40, it is contemplated that road segments 4002
and 4004 may intersect at any angle. Further, although road
segments 4002 and 4004 each have two lanes in FIG. 40, it is
contemplated that road segments 4002 and 4004 may have any number
of lanes. It is also contemplated that road segments 4002 and 4004
may have the same number or different number of lanes.
[0665] Vehicle 200 may travel along lane 4006 of road segment 4002.
Vehicle 200 may be equipped with three image capture devices 122,
124, 126. Although, FIG. 40 depicts vehicle 200 as equipped with
image capture devices 122, 124, 126, more or fewer image capture
devices may be employed on any particular vehicle 200. As
illustrated in FIG. 40, lane 4006 of road segment 4002 may be
delimited by left side 4008 and right side 4010. A predetermined
road model trajectory 4012 may define a preferred path (i.e., a
target road model trajectory) within lane 4006 of road segments
4002, 4004 that vehicle 200 may follow as vehicle 200 travels along
road segments 4002, 4004 through junction 4000. In some exemplary
embodiments, predetermined road model trajectory 4012 may be
located equidistant from left side 4008 and right side 4010. It is
contemplated however that predetermined road model trajectory 4012
may be located nearer to one or the other of left side 4008 and
right side 4010 of road segment 4002.
[0666] In one exemplary embodiment, predetermined road model
trajectory 4012 may be mathematically defined using a
three-dimensional polynomial function. In some exemplary
embodiments, processing unit 110 of vehicle 200 may be configured
to retrieve predetermined road model trajectory 4012 from a
database (e.g. 160) stored in one or more of memories 140, 150
included in vehicle 200. In other exemplary embodiments, processing
unit 110 of vehicle 200 may be configured to retrieve predetermined
road model trajectory 4012 from a database (e.g. 160), which may be
stored remotely from vehicle 200, over a wireless communications
interface. As illustrated in the exemplary embodiment of FIG. 40,
predetermined road model trajectory 4012 may allow vehicle 200 to
turn left from lane 4006 of road segment 4002 into lane 4014 of
road segment 4004.
[0667] Image acquisition unit 120 may be configured to acquire an
image representative of an environment of vehicle 200. For example,
image acquisition unit 120 may obtain an image showing a view in
front of vehicle 200 using one or more of image capture devices
122, 124, 126. Processing unit 110 of vehicle 200 may be configured
to detect two or more landmarks 4016, 4018 in the one or more
images acquired by image acquisition unit 120. Such detection may
occur using the landmark detection techniques previously discussed,
for example. Processing unit 110 may detect the two or more
landmarks 4016, 4018 using one or more processes of landmark
identification discussed above with reference to FIGS. 22-28.
Although FIG. 40 illustrates two landmarks 4016, 4018, it is
contemplated that vehicle 200 may detect more than two landmarks
4016, 4018 (i.e., three or more landmarks) based on the images
acquired by image acquisition unit 120. For example, FIG. 40
illustrates additional landmarks 4020 and 4022, which may be
detected and used by processing unit 110.
[0668] Processing unit 110 may be configured to determine positions
4024, 4026 of landmarks 4016, 4018, respectively, relative to
vehicle 200. Processing unit 110 may also be configured to
determine one or more directional indicators 4030, 4032 of
landmarks 4016, 4018 relative to vehicle 200. Further, processing
unit 110 may be configured to determine current location 4028 of
vehicle 200 based on an intersection of directional indicators
4030, 4032. In one exemplary embodiment as illustrated in FIG. 40,
processing unit 110 may be configured to determine current location
4028 as the intersection point of directional indicators 4030,
4032.
[0669] Processing unit 110 may be configured to determine previous
location 4034 of vehicle 200. In one exemplary embodiment,
processing unit 110 may repeatedly determine a location of vehicle
200 as vehicle 200 travels on road segments 4002 and 4004. Thus,
for example, before vehicle 200 reaches its current location 4028,
vehicle may be located at previous location 4034 and may travel
from previous location 4034 to current location 4028. Before
reaching current location 4028, processing unit 110 of vehicle 200
may be configured to determine positions 4024, 4026 of landmarks
4016, 4018, respectively, relative to vehicle 200. Processing unit
110 may also be configured to determine directional indicators
4036, 4038 of landmarks 4016, 4018 relative to vehicle 200.
Processing unit 110 may also be configured to determine previous
location 4034 of vehicle 200 based on an intersection of
directional indicators 4036, 4038. In one exemplary embodiment as
illustrated in FIG. 40, processing unit 110 may be configured to
determine previous location 4034 as the intersection point of
directional indicators 4036, 4038.
[0670] Processing unit 110 may be configured to determine a
direction 4040 of predetermined road model trajectory 4012 at
current location 4028 of vehicle 200. Processing unit 110 may
determine direction 4040 as a direction tangential to predetermined
road model trajectory 4012. In one exemplary embodiment, processing
unit 110 may be configured to determine direction 4040 based on a
gradient or slope of a three-dimensional polynomial representing
predetermined road model trajectory 4012.
[0671] Processing unit 110 may also be configured to determine
heading direction 4050 of vehicle 200. Processing unit 110 may
determine heading direction 4050 based on landmarks 4016 and 4018.
Processing unit 110 may determine heading direction 4050 based on
current location 4028 and previous location 4034 of vehicle 200.
For example, processing unit 110 may determine heading direction
4050 as a vector extending from previous location 4034 towards
current location 4028. In some exemplary embodiments, processing
unit 110 may determine heading direction 4050 as a direction along
which image capture device 122 may be oriented relative to a local
coordinate system associated with vehicle 200.
[0672] Processing unit 110 may be configured to determine whether
heading direction 4050 of vehicle 200 is aligned with (i.e.,
generally parallel to) direction 4040 of predetermined road model
trajectory 4012. When heading direction 4050 is not aligned with
direction 4040 of predetermined road model trajectory 4012 at
current location 4028 of vehicle 200, processing unit 110 may
determine a steering angle between heading direction 4050 of
vehicle 200 and direction 4040 of predetermined road model
trajectory 4012. In one exemplary embodiment, processing unit 110
may also determine, for example, a reduction or acceleration in a
current velocity of vehicle 200 required to help ensure that
heading direction 4050 of vehicle 200 may be aligned with direction
4040 of predetermined road model trajectory 4012 in a predetermined
amount of time. Processing unit 110 may be configured to execute
instructions stored in navigational response module 408, for
example, to transmit a control signal specifying the steering angle
to steering system 240 of the vehicle. Steering system 200, in
turn, may be configured to rotate wheels of vehicle 200 to help
ensure that heading direction 4050 of vehicle 200 may be aligned
with direction 4040 of predetermined road model trajectory
4012.
[0673] Image acquisition unit 120 may repeatedly acquire an image
of the environment in front of vehicle 200, for example, after a
predetermined amount of time. Processing unit 110 may also be
configured to repeatedly detect landmarks 4016, 4018, 4020, 4022,
etc., in the image acquired by image acquisition unit 120 and
determine the steering angle as discussed above. Thus, image
acquisition unit 120 and processing unit 110 may cooperate to
navigate vehicle 200 through junction 400 using two or more of
landmarks 4016, 4018, 4020, 4022.
[0674] FIG. 41 is a flowchart showing an exemplary process 4100,
for navigating vehicle 200 through junction 4000, using two or more
landmarks 4016, 4018, 4020, 4022, consistent with disclosed
embodiments. Steps of process 4100 may be performed by one or more
of processing unit 110 and image acquisition unit 120, with or
without the need to access memory 140 or 150. The order and
arrangement of steps in process 4100 is provided for purposes of
illustration. As will be appreciated from this disclosure,
modifications may be made to process 4100 by, for example, adding,
combining, removing, and/or rearranging the steps for the
process.
[0675] As illustrated in FIG. 41, process 4100 may include a step
4102 of acquiring an image representative of an environment of the
vehicle. In one exemplary embodiment, image acquisition unit 120
may acquire one or more images of an area forward of vehicle 200
(or to the sides or rear of a vehicle, for example). For example,
image acquisition unit 120 may obtain an image using image capture
device 122 having a field of view 202. In other exemplary
embodiments, image acquisition unit 120 may acquire images from one
or more of image capture devices 122, 124, 126, having fields of
view 202, 204, 206. Image acquisition unit 120 may transmit the one
or more images to processing unit 110 over a data connection (e.g.,
digital, wired, USB, wireless, Bluetooth, etc.).
[0676] Process 4100 may also include a step 4104 of identifying two
or more landmarks 4016, 4018, 4020, 4022 in the one or more images
Processing unit 110 may receive the one or more images from image
acquisition unit 120. Processing unit 110 may execute monocular
image analysis module 402 to analyze the plurality of images at
step 4104, as described in further detail in connection with FIGS.
5B-5D. By performing the analysis, processing unit 110 may detect a
set of features within the set of images, for example, two or more
landmarks 4016, 4018, 4020, 4022. Landmarks 4016, 4018, 4020, 4022
may include one or more traffic signs, arrow markings, lane
markings, dashed lane markings, traffic lights, stop lines,
directional signs, reflectors, landmark beacons, lampposts, a
change in spacing of lines on the road, signs for businesses, and
the like.
[0677] In some embodiments, processing unit 110 may execute
monocular image analysis module 402 to perform multi-frame analysis
on the plurality of images to detect two or more landmarks 4016,
4018, 4020, 4022. For example, processing unit 110 may estimate
camera motion between consecutive image frames and calculate the
disparities in pixels between the frames to construct a 3D-map of
the road. Processing unit 110 may then use the 3D-map to detect the
road surface, as well as landmarks 4016, 4018, 4020, 4022. In
another exemplary embodiment, image processor 190 of processing
unit 110 may combine a plurality of images received from image
acquisition unit 120 into one or more composite images. Processing
unit 110 may use the composite images to detect the two or more
landmarks 4016, 4018, 4020, 4022. For example, in some embodiments,
processing unit 110 may perform stereo processing of images from
two or more image capture devices.
[0678] Process 4100 may also include a step 4106 of determining
directional indicators 4030, 4032 associated with at least two
landmarks 4016, 4018, respectively. Processing unit 110 may
determine directional indicators 4030, 4032 based on the positions
4024, 4026 of the at least two landmarks 4016, 4018, respectively,
relative to vehicle 200. For example, processing unit 110 may
receive landmark positions 4024, 4026 for landmarks 4016, 4018,
respectively, from information, which may be stored in one or more
databases in memory 140 or 150. Processing unit 110 may determine
directional indicator 4030 as a vector extending from vehicle 200
towards landmark position 4024. Likewise, processing unit 110 may
determine directional indicator 4032 as a vector extending from
vehicle 200 towards landmark position 4026. Although two landmarks
4016, 4018 are referenced in the above discussion, it is
contemplated that processing unit 110 may determine landmark
positions 4024, 4026, and directional indicators 4030, 4032 for
more than two landmarks 4016, 4018 (e.g., for landmarks 4020,
4022).
[0679] Process 4100 may include a step 4108 of determining current
location 4028 of vehicle 200. Processing unit 110 may determine
current location 4028 based on an intersection of directional
indicators 4030 and 4032 of landmarks 4016, 4018, respectively
(e.g., at an intersection point of directional indicators 4030 and
4032). Process 4100 may include a step 4110 of determining previous
location 4034 of vehicle 200. As discussed above, processing unit
110 may be configured to determine previous location 4034 of
vehicle 200 based on two or more landmarks 4016, 4018, 4020, 4022.
In one exemplary embodiment, processing unit 110 may repeatedly
determine a location of vehicle 200 using two or more landmarks
4016, 4018, 4020, 4022 as vehicle 200 moves on road segments 4002
and 4004. Thus, for example, before vehicle 200 reaches its current
location 4028, vehicle may be located at previous location 4034 and
may travel from previous location 4034 to current location 4028.
Before reaching current location 4028, processing unit 110 of
vehicle 200 may be configured to determine positions 4024, 4026 of
landmarks 4016, 4018, respectively, relative to vehicle 200.
Processing unit 110 may perform processes similar to those
discussed above with respect to step 4108 to determine previous
location 4034 of vehicle 200. For example, processing unit 110 may
be configured to determine directional indicators 4036, 4038 of
landmarks 4016, 4018 relative to vehicle 200. Processing unit 110
may also be configured to determine previous location 4034 of
vehicle 200 based on an intersection of directional indicators
4036, 4038 (e.g. at an intersection point of directional indicators
4036 and 4038).
[0680] Process 4100 may include a step 4112 of determining heading
direction 4050 of vehicle 200. As discussed above, processing unit
110 may determine heading direction 4050 based on current location
4028 and previous location 4034 of vehicle 200, both of which may
be determined using two or more of landmarks 4016, 4018, 4020,
4022. In one exemplary embodiment, processing unit 110 may
determine heading direction 4050 as a vector extending from
previous location 4034 towards current location 4028. In another
exemplary embodiment, processing unit 110 may determine heading
direction 4050 as a direction along which image capture device 122
may be oriented relative to a local coordinate system associated
with vehicle 200. Although only two landmarks 4016, 4018 have been
described with respect to determining current location 4028 and
previous location 4024 of vehicle 200, it is contemplated that
processing unit may use more than two landmarks 4016, 4018 to
determine current location 4028 and previous location 4024 of
vehicle 200 and heading direction 4050.
[0681] Process 4100 may include a step 4114 of determining
direction 4040 of predetermined road model trajectory 4012 at
current location 4028 of vehicle 200. In one exemplary embodiment,
processing unit 110 may obtain a mathematical representation (e.g.
three-dimensional polynomial) of predetermined road model
trajectory 4012. Processing unit 110 may determine direction 4040
as a vector oriented tangentially to predetermined road model
trajectory 4012 at current location 4028 of vehicle 200. For
example, processing unit 110 may determine direction 4040 as a
vector pointing along a gradient of the mathematical representation
of predetermined road model trajectory 4012 at current location
4028 of vehicle 200. Although the above description assumes that
current location 4028 and previous location 4034 of vehicle 200 are
located on predetermined road model trajectory 4012, processing
unit 110 may perform processes similar to those discussed above
with respect to FIGS. 34-39 when vehicle 200 is not located on
predetermined road model trajectory 4012. For example, processing
unit 110 may determine a transform required to move vehicle 200 to
predetermined road model trajectory 4012 before determining
direction 4040 as discussed above.
[0682] Process 4100 may also include a step 4116 of determining
steering angle .quadrature. for vehicle 200. Processing unit 110
may also determine steering angle .quadrature. as an angle between
heading direction 4050 and direction 4040 of predetermined road
model trajectory 4012 at current location 4028 of vehicle 200.
Processing unit 110 may execute instructions in navigational module
408, for example, to transmit a control signal specifying steering
angle .quadrature. to steering system 240. Steering system 240 may
help adjust, for example, a steering wheel of vehicle 200 to turn
the wheels of vehicle 200 to help ensure that heading direction
4050 of vehicle 200 may be aligned (i.e., parallel) with direction
4040 of predetermined road model trajectory 4012.
[0683] Processing unit 110 and/or image acquisition unit 120 may
repeat steps 4102 through 4116 after a predetermined amount of
time. In one exemplary embodiment, the predetermined amount of time
may range between about 0.5 seconds to 1.5 seconds. By repeatedly
determining current location 4028, heading direction 4050,
direction 4040 of predetermined road model trajectory 4012 at
current location 4028, and steering angle .quadrature. required to
align heading direction 4050 with direction 4040, processing unit
110 may transmit one or more control signals to one or more of
throttling system 220, steering system 240, and braking system 230
to navigate vehicle 200 through road junction 4000, using two or
more landmarks 4016, 4018, 4020, 4022.
[0684] Navigation Using Local Overlapping Maps
[0685] Consistent with disclosed embodiments, the system may use a
plurality of local maps for navigation. Each map may have its own
arbitrary coordinate frame. To ease the transition in navigating
from one local map to another, the maps may include an overlap
segment, and navigation in the overlap segment may be based on both
of the overlapping maps.
[0686] FIG. 43 illustrates first and second local maps 4200 and
4202 associated with first and second road segments 4204 and 4206,
respectively. First road segment 4204 may be different from second
road segment 4206. Maps 4200 and 4202 may each have their own
arbitrary coordinate frame. Maps 4200 and 4202 may also each
constitute a sparse map having the same or different data
densities. In one exemplary embodiment, maps 4200 and 4202 may each
have a data density of no more than 10 kilobytes per kilometer. Of
course, local maps 4200 and 4202 may include other data density
values, such as any of the data densities previously discussed
relative to sparse map 800, for example. Vehicle 200 (which may be
an autonomous vehicle) travelling on a first road segment 4204
and/or on second road segment 4206 may use the disclosed systems
and methods for navigation. Vehicle 200 may include at least one
image capture device 122, which may be configured to obtain one or
more images representative of an environment of the autonomous
vehicle. Although FIG. 42 depicts vehicle 200 as equipped with
image capture devices 122, 124, 126, more or fewer image capture
devices may be employed on any particular vehicle 200. As
illustrated in FIG. 42, map 4200 may include road segment 4204,
which may be delimited by left side 4208 and right side 4210. A
predetermined road model trajectory 4212 may define a preferred
path (i.e., a target road model trajectory) within road segment
4204. Predetermined road model trajectory 4212 may be
mathematically represented by a three-dimensional polynomial.
Vehicle 200 may follow predetermined road model trajectory 4212 as
vehicle 200 travels along road segment 4204. In some exemplary
embodiments, predetermined road model trajectory 4212 may be
located equidistant from left side 4208 and right side 4210. It is
contemplated however that predetermined road model trajectory 4212
may be located nearer to one or the other of left side 4208 and
right side 4210 of road segment 4204. As also illustrated in FIG.
42, a portion of road segment 4204 between delimiting points A and
B may represent an overlap segment 4220. As will be described
later, overlap segment 4220 between positions A and B of road
segment 4204 may overlap with a portion of road segment 4206.
Further, although FIG. 42 illustrates one lane in road segment
4204, it is contemplated that road segment 4204 may have any number
of lanes. It is also contemplated that vehicle 200 travelling along
any lane of road segment 4204 may be navigated according to the
disclosed methods and systems. Further, in some embodiments, a road
segment may extend between two known locations such as, for
example, two intersections.
[0687] As also illustrated in FIG. 42, map 4202 may include road
segment 4206, which may be delimited by left side 4222 and right
side 4224. A predetermined road model trajectory 4226 may define a
preferred path (i.e., a target road model trajectory) within road
segment 4206. Predetermined road model trajectory 4226 may be
mathematically represented by a three-dimensional polynomial.
Vehicle 200 may follow predetermined road model trajectory 4226 as
vehicle 200 travels along road segment 4206. In some exemplary
embodiments, predetermined road model trajectory 4226 may be
located equidistant from left side 4222 and right side 4224. It is
contemplated however that predetermined road model trajectory 4226
may be located nearer to one or the other of left side 4222 and
right side 4224 of road segment 4206. As also illustrated in FIG.
42, a portion of road segment 4206 between delimiting points A' and
B' may represent overlap segment 4220, which may overlap with
overlap segment 4220 between delimiting points A and B of road
segment 4204. Although FIG. 42 illustrates one lane in road segment
4206, it is contemplated that road segment 4206 may have any number
of lanes. It is also contemplated that vehicle 200 travelling along
any lane of road segment 4206 may be navigated according to the
disclosed methods and systems.
[0688] As used in this disclosure, the term overlap indicates that
overlap segment 4220 represents the same portion of the road that
may be travelled on by vehicle 200. In some embodiments, an overlap
segment 4220 may include a segment of map 4200 that represents a
road segment and associated road features (such as landmarks, etc.)
that are also represented by a corresponding segment (i.e., the
overlap segment) of map 4222. As a result, overlap segment 4220 may
include portions of road segments 4204, 4206 having the same size
(length, width, height, etc.), shapes (orientation and inclination,
etc.), etc. Moreover, the shapes and lengths of predetermined road
model trajectories 4212 and 4226 in the overlap segment 4220 may be
similar. However, because maps 4200 and 4202 may have different
local coordinate systems, the mathematical representations (e.g.,
three-dimensional polynomials) of predetermined road model
trajectories 4212 and 4226 may differ in the overlap segment 4220.
In one exemplary embodiment, overlap segment 4220 may have a length
ranging between 50 m and 150 m.
[0689] Image acquisition unit 120 may be configured to acquire an
image representative of an environment of vehicle 200. For example,
image acquisition unit 120 may obtain an image showing a view in
front of vehicle 200 using one or more of image capture devices
122, 124, 126. Processing unit 110 of vehicle 200 may be configured
to detect a current location 4214 of vehicle 200 using one or more
navigational processes discussed above with reference to FIGS.
34-36. Processing unit 110 may also be configured to determine
whether current position 4214 of vehicle 200 lies on road segment
4204 or 4206 using one or more processes of determining
intersection points of directional vectors for recognized landmarks
with one or more of predetermined road model trajectories 4212 and
4226 as discussed above with reference to FIGS. 34-36. Furthermore,
processing unit 110 may be configured to determine whether current
location 4214 of vehicle 200 lies on road segment 4204, road
segment 4206, or in the overlap segment 4220, using similar
processes discussed above with reference to FIGS. 34-36.
[0690] When vehicle 200 is located on road segment 4204, processing
unit 110 may be configured to align a local coordinate system of
vehicle 200 with a local coordinate system associated with road
segment 4204. After aligning the two coordinate systems, processing
unit 110 may be configured to determine a direction 4230 of
predetermined road model trajectory 4212 at current location 4214
of vehicle 200. Processing unit 110 may determine direction 4230 as
a direction tangential to predetermined road model trajectory 4212.
In one exemplary embodiment, processing unit 110 may be configured
to determine direction 4230 based on a gradient or slope of a
three-dimensional polynomial representing predetermined road model
trajectory 4212.
[0691] Processing unit 110 may also be configured to determine
heading direction 4240 of vehicle 200. As illustrated in FIG. 42,
heading direction 4240 of vehicle 200 may be a direction along
which image capture device 122 may be oriented relative to the
local coordinate system associated with vehicle 200. Processing
unit 110 may be configured to determine whether heading direction
4240 of vehicle 200 is aligned with (i.e., generally parallel to)
direction 4230 of predetermined road model trajectory 4212. When
heading direction 4240 is not aligned with direction 4230 of
predetermined road model trajectory 4212 at current location 4214
of vehicle 200, processing unit 110 may determine a first
autonomous navigational response (ANR) that may help ensure that
heading direction 4240 of vehicle 200 may be aligned with direction
4230 of predetermined road model trajectory 4212.
[0692] In one exemplary embodiment, first ANR may include, for
example, a determination of an angle by which the steering wheel or
front wheels of vehicle 200 may be turned to help ensure that
heading direction 4240 of vehicle 200 may be aligned with direction
4230 of predetermined road model trajectory 4212. In another
exemplary embodiment, first autonomous navigational response may
also include a reduction or acceleration in a current velocity of
vehicle 200 to help ensure that heading direction 4240 of vehicle
200 may be aligned with direction 4230 of predetermined road model
trajectory 4212 in a predetermined amount of time. Processing unit
110 may be configured to execute instructions stored in
navigational response module 408 to trigger first ANR by, for
example, turning the steering wheel of vehicle 200 to achieve a
rotation of an angle 1. Rotation by angle 1 may help align heading
direction 4240 of vehicle 200 with direction 4230.
[0693] When vehicle 200 is located on road segment 4206, processing
unit 110 may be configured to align a local coordinate system of
vehicle 200 with a local coordinate system associated with road
segment 4206. After aligning the two coordinate systems, processing
unit 110 may be configured to determine a direction 4250 of
predetermined road model trajectory 4226 at current location 4214
of vehicle 200. Processing unit 110 may determine direction 4250 as
a direction tangential to predetermined road model trajectory 4226.
In one exemplary embodiment, processing unit 110 may be configured
to determine direction 4250 based on a gradient or slope of a
three-dimensional polynomial representing predetermined road model
trajectory 4226.
[0694] Processing unit 110 may also be configured to determine
heading direction 4260 of vehicle 200. As illustrated in FIG. 42,
heading direction 4260 of vehicle 200 may be a direction along
which image capture device 122 may be oriented relative to the
local coordinate system associated with vehicle 200. Processing
unit 110 may be configured to determine whether heading direction
4260 of vehicle 200 is aligned with (i.e., generally parallel to)
direction 4250 of predetermined road model trajectory 4226. When
heading direction 4260 is not aligned with direction 4250 of
predetermined road model trajectory 4226 at current location 4214
of vehicle 200, processing unit 110 may determine a second ANR that
may help ensure that heading direction 4260 of vehicle 200 may be
aligned with direction 4250 of predetermined road model trajectory
4226.
[0695] In one exemplary embodiment, the second ANR may include, for
example, a determination of an angle 2 by which the steering wheel
or front wheels of vehicle 200 may be turned to help ensure that
heading direction 4260 of vehicle 200 may be aligned with direction
4250 of predetermined road model trajectory 4226. In another
exemplary embodiment, the second ANR may also include a reduction
or acceleration in a current velocity of vehicle 200 to help ensure
that heading direction 4260 of vehicle 200 may be aligned with
direction 4250 of predetermined road model trajectory 4226 in a
predetermined amount of time. Processing unit 110 may be configured
to execute instructions stored in navigational response module 408
to trigger second ANR by, for example, turning the steering wheel
of vehicle 200 to achieve a rotation of angle 2. Rotation by angle
2 may help align heading direction 4260 of vehicle 200 with
direction 4250.
[0696] When vehicle 200 is located on overlap segment 4220 of road
segments 4204, 4206, processing unit 110 may be configured to align
the local coordinate system of vehicle 200 with both the local
coordinate system associated with road segment 4204 as well as the
local coordinate system associated with road segment 4206. Thus,
processing unit 110 may be configured to determine a third ANR
based on both maps 4200 and 4202. In one exemplary embodiment,
processing unit 110 may determine the third ANR as an angle 3 by
which the steering wheel or front wheels of vehicle 200 may be
turned to help ensure that heading direction 4240 of vehicle 200
may be aligned with heading direction 4230 of predetermined road
model trajectory, and heading direction 4260 of vehicle 200 may be
aligned with direction 4250 of predetermined road model trajectory
4226. Thus, for example, processing unit 110 may determine angle
.quadrature..sub.3 as a combination of angles 1 and 2.
[0697] Image acquisition unit 120 may repeatedly acquire an image
of the environment in front of vehicle 200, for example, after a
predetermined amount of time. Processing unit 110 may also be
configured to repeatedly detect whether current location 4214 of
vehicle 200 lies on road segment 4204, road segment 4206, or in
overlap segment 4220. Processing unit 110 may determine first,
second, or third ANR (e.g., angles 1, 2, or 3) based on where
vehicle 200 is located on road segments 4204, 4206. Thus, image
acquisition unit 120 and processing unit 110 may cooperate to
navigate vehicle 200 along road segments 4204 and 4206 using
overlap segment 4220.
[0698] FIGS. 43A-C include flowcharts showing an exemplary process
4300, for navigating vehicle 200 along road segments 4204, 4206,
using overlapping maps 4200, 4202, consistent with disclosed
embodiments. Steps of process 4300 may be performed by one or more
of processing unit 110 and image acquisition unit 120, with or
without the need to access memory 140 or 150. The order and
arrangement of steps in process 4300 is provided for purposes of
illustration. As will be appreciated from this disclosure,
modifications may be made to process 4300 by, for example, adding,
combining, removing, and/or rearranging the steps of process
4300.
[0699] As illustrated in FIG. 43A, process 4300 may include a step
4302 of acquiring an image representative of an environment of the
vehicle. In one exemplary embodiment, image acquisition unit 120
may acquire one or more images of an area forward of vehicle 200
(or to the sides or rear of a vehicle, for example). For example,
image acquisition unit 120 may obtain an image using image capture
device 122 having a field of view 202. In other exemplary
embodiments, image acquisition unit 120 may acquire images from one
or more of image capture devices 122, 124, 126, having fields of
view 202, 204, 206. Image acquisition unit 120 may transmit the one
or more images to processing unit 110 over a data connection (e.g.,
digital, wired, USB, wireless, Bluetooth, etc.).
[0700] Process 4300 may also include a step 4302 of determining
current location 4214 of vehicle 200. Processing unit 110 may
receive the one or more images from image acquisition unit 120.
Processing unit 110 may execute monocular image analysis module 402
to analyze the plurality of images at step 4302, as described in
further detail in connection with FIGS. 5B-5D. By performing the
analysis, processing unit 110 may detect a set of features within
the set of images, for example, one or more landmarks. Processing
unit 110 may use the landmarks and perform processes similar to
those discussed above, for example, in FIGS. 34-36 to determine
current location 4214 of vehicle 200.
[0701] Process 4300 may include a step 4306 of determining whether
vehicle 200 is located on first road segment 4304. Processing unit
110 may determine whether vehicle 200 is located on first road
segment 4304 in many ways. For example, processing unit may compare
its current location 4214 determined in, for example, step 4304
with predetermined road model trajectory 4212 to determine whether
current position 4214 is located on predetermined road model
trajectory 4212. Processing unit may determine that vehicle 200 is
located on first road segment 4304 when current position 4214 is
located on predetermined road model trajectory 4212. In another
exemplary embodiment, processing unit 110 may use landmarks and
directional indicators for the landmarks to determine whether a
current position 4214 of vehicle 200 is located on road segment
4204. For example, as discussed above with respect to FIGS. 34-36,
if a directional indicator of a recognized landmark intersects with
predetermined road model trajectory 4212 (discussed above, e.g., in
relation to FIGS. 34-36), processing unit 110 may determine that
current location 4214 of vehicle 200 lies in road segment 4204.
When processing unit 110 determines that vehicle 200 is located in
road segment 4204 (Step 4306: Yes), processing unit 110 may proceed
to step 4308. When processing unit 110 determines, however, that
vehicle 200 is not located on road segment 4204 (Step 4306: No),
processing unit 110 may proceed to step 4314 via process segment
C.
[0702] In step 4308, processing unit 110 may determine whether
vehicle 200 is located in overlap segment 4220. Processing unit 110
may use processes similar to those discussed above with respect to
step 4306 to determine whether vehicle 200 is located in overlap
segment 4220. For example, processing unit 110 may determine
whether a direction indicator corresponding to a recognized
landmark intersects predetermined road model trajectory 4212 in the
portion of predetermined road model trajectory 4212 located between
A and B in overlap segment 4220. In another exemplary embodiment,
processing unit 110 may compare current location 4214 of vehicle
200 with the mathematical representation of predetermined road
model trajectory 4212 to determine whether vehicle 200 is located
in overlap segment 4220. In yet another exemplary embodiment,
processing unit 110 may determine a distance travelled by vehicle
200 along predetermined road model trajectory 4212 in first road
segment 4204. Processing unit may determine the distance travelled
using processes similar to those discussed above with respect to
FIGS. 37-39 regarding navigation using tail alignment. Processing
unit 110 may determine whether current location 4214 of vehicle 200
lies in overlap segment 4220 based on the distance travelled by
vehicle 200. When processing unit 110 determines that vehicle 200
is located within overlap segment 4220 (Step 4308: Yes), processing
unit 110 may proceed to step 4320 via process segment D. When
processing unit 110 determines, however, that vehicle 200 is not
located within overlap segment 4220 (Step 4308: No), processing
unit 110 may proceed to step 4310.
[0703] Process 4300 may include a step 4310 of determining first
ANR. Processing unit 110 may determine first ANR based on its
determination that vehicle 200 is located in first road segment
4204 but not in overlap segment 4220. In one exemplary embodiment,
processing unit 110 may obtain a mathematical representation (e.g.
three-dimensional polynomial) of predetermined road model
trajectory 4212. Processing unit 110 may determine direction 4230
of predetermined road model trajectory 4212 as a vector oriented
tangentially to predetermined road model trajectory 4212 at current
location 4214 of vehicle 200. For example, processing unit 110 may
determine direction 4230 as a vector pointing along a gradient of
the mathematical representation of predetermined road model
trajectory 4212 at current location 4214. Although the above
description assumes that current location 4214 of vehicle 200 is
located on predetermined road model trajectory 4212, processing
unit 110 may perform processes similar to those discussed above
with respect to FIGS. 34-39 when vehicle 200 is not located on
predetermined road model trajectory 4212. For example, processing
unit may determine a transform required to move vehicle 200 to
predetermined road model trajectory 4212 before determining
direction 4230 as discussed above.
[0704] Processing unit 110 may also determine a heading direction
4240 of vehicle 200. For example, as illustrated in FIG. 42,
processing unit 110 may determine heading direction 4240 of vehicle
200 as the direction in which image capture device 122 may be
oriented relative to a local coordinate system associated with
vehicle 200. In another exemplary embodiment, processing unit 200
may determine heading direction 4240 as the direction of motion of
vehicle 200 at current location 4214. In yet another exemplary
embodiment, processing unit may determine heading direction 4240
based on a travelled trajectory as discussed above with respect to
FIGS. 37-39. Processing unit 110 may determine a rotational angle
.quadrature..sub.1 between heading direction 4240 and direction
4230 of predetermined road model trajectory 4212. In one exemplary
embodiment, first ANR may include rotation angle 1 that may help
ensure that heading direction 4240 of vehicle 200 may be aligned
with direction 4230 of predetermined road model trajectory 4212. In
another exemplary embodiment, first ANR may also include
accelerations or decelerations of vehicle 200 that may be required
to help ensure that heading direction 4240 of vehicle 200 may be
aligned with direction 4230 of predetermined road model trajectory
4212 in a predetermined amount of time.
[0705] Process 4300 may also include a step 4312 of adjusting
steering system 240 based on first ANR. Processing unit 110 may be
configured to execute instructions stored in navigational response
module 408 to trigger first ANR by, for example, turning the
steering wheel of vehicle 200 to achieve a rotation of angle 1.
Processing unit 110 may also execute instructions stored in
navigational response module 408 to control throttling system 220
and/or braking system 230 to appropriately control a speed of
vehicle 200 to help ensure that heading direction 4240 of vehicle
200 may be aligned with direction 4230 of predetermined road model
trajectory 4212 in a predetermined amount of time.
[0706] Returning to step 4306, when processing unit 110 determines
that vehicle 200 is not located on road segment 4204 (Step 4306:
No), processing unit 110 may proceed to step 4314 via process
segment C. In step 4314, processing unit 110 may determine whether
vehicle 200 is located in road segment 4206. Processing unit 110
may perform operations similar to those discussed above in step
4306 to determine whether vehicle 200 is located in road segment
4206. When processing unit 110 determines that vehicle 200 is not
located in road segment 4206, process 4300 may end. When processing
unit 110 determines, however, that vehicle 200 is located in road
segment 4206, processing unit 100 may proceed to step 4316 of
determining second ANR.
[0707] Processing unit 110 may determine second ANR using processes
similar to those discussed above with respect to step 4310. For
example, processing unit 110 may determine a direction 4250 of
predetermined road model trajectory 4226 at current location 4214
of vehicle 200, a heading direction 4260, and an angle of rotation
2, which may help ensure that heading direction 4260 of vehicle 200
may be aligned with direction 4250. Further, like first ANR, second
ANR may also include accelerations or decelerations of vehicle 200
that may be required to help ensure that heading direction 4260 of
vehicle 200 may be aligned with direction 4250 of predetermined
road model trajectory 4226 in a predetermined amount of time.
[0708] Process 4300 may also include a step 4318 of adjusting
steering system 240 based on second ANR. Processing unit 110 may be
configured to execute instructions stored in navigational response
module 408 to trigger second ANR by, for example, turning the
steering wheel of vehicle 200 to achieve a rotation of angle 2.
Processing unit 110 may also execute instructions stored in
navigational response module 408 to control throttling system 220
and/or braking system 230 to appropriately control a speed of
vehicle 200 to help ensure that heading direction 4260 of vehicle
200 may be aligned with direction 4250 of predetermined road model
trajectory 4226 in a predetermined amount of time.
[0709] Returning to step 4308, when processing unit 110 determines
that vehicle 200 is located on overlap segment 4220 (Step 4308:
Yes), processing unit 110 may proceed to step 4320 via process
segment D. In step 4320, processing unit 110 may determine first
ANR. Processing unit 110 may determine first ANR using operations
similar to those discussed above with respect to step 4310. Thus,
for example, processing unit may determine a direction 4240 of
predetermined road model trajectory 4212 at current location 4214
of vehicle 200, a heading direction 4230, and an angle of rotation
1, which may help ensure that heading direction 4240 of vehicle 200
may be aligned with direction 4230. Further, first ANR may also
include accelerations or decelerations of vehicle 200 that may be
required to help ensure that heading direction 4240 of vehicle 200
may be aligned with direction 4230 of predetermined road model
trajectory 4212 in a predetermined amount of time.
[0710] Process 4300 may also include a step 4322 of determining a
second ANR. Processing unit 110 may determine second ANR using
operations similar to those discussed above with respect to step
4316. Thus, for example, processing unit may determine a direction
4260 of predetermined road model trajectory 4226 at current
location 4214 of vehicle 200, a heading direction 4250, and an
angle of rotation 2, which may help ensure that heading direction
4260 of vehicle 200 may be aligned with direction 4250. Further,
second ANR may also include accelerations or decelerations of
vehicle 200 that may be required to help ensure that heading
direction 4260 of vehicle 200 may be aligned with direction 4250 of
predetermined road model trajectory 4226 in a predetermined amount
of time.
[0711] Process 4300 may also include a step 4324 of determining an
error between first ANR and second ANR. In one exemplary
embodiment, processing unit 110 may determine the error as an error
between angles of rotation 1 and 2 determined, for example, in
steps 4320 and 4322. In another exemplary embodiment, processing
unit 110 may determine the error as an error between direction 4230
of predetermined road model trajectory 4212 and direction 4250 of
predetermined road model trajectory 4226. In another exemplary
embodiment, processing unit 110 may determine the error as a cosine
distance between directions 4230 and 4250. One of ordinary skill in
the art would recognize that processing unit 110 may use other
mathematical functions to determine the error between directions
4230 and 4250.
[0712] Process 4300 may also include a step 4326 of determining
whether the error is less than a threshold error. Because
processing unit 110 may perform step 4324 only when vehicle 200 is
located in overlap segment 4220, the error may indicate whether the
co-ordinate frame of vehicle 200 is aligned with both road segments
4204 and 4206. It is contemplated that in some embodiments when
vehicle 200 first enters overlap segment 4220, the error may exceed
the threshold error and navigating vehicle 200 based on both
navigational maps 4200 and 4202 may improve accuracy. As vehicle
200 travels further within overlap segment 4220, the error may
decrease and may eventually become less than the threshold error.
When the co-ordinate frame of vehicle 200 is aligned with both road
segments 4204 and 4206, the error may be smaller than the threshold
error and it may be sufficient to start navigating vehicle 200
based only on navigational map 4202.
[0713] When processing unit 110 determines that the error is
greater than the threshold error (Step 4326: Yes), processing unit
110 may proceed to step 4328. In step 4328, processing unit 110 may
determine third ANR based on both the first ANR and the second ANR
so that vehicle 200 may be navigated based on both maps 4200 and
4202. Thus, for example, processing unit 110 may determine a third
angle of rotation 3 as a combination of angles of rotation 1 and 2
determined, for example, in steps 4320 and 4322. In some exemplary
embodiments, the combination may be an average, a weighted average,
or some other mathematical combination of angles of rotation 1 and
2. Likewise, processing unit 110 may determine accelerations or
decelerations for vehicle 200 based on a combination of the
accelerations and/or decelerations determined, for example, in
steps 4320 and 4322.
[0714] Process 4300 may also include a step 4330 of adjusting
steering system 240 based on third ANR. Processing unit 110 may be
configured to execute instructions stored in navigational response
module 408 to trigger third ANR by, for example, turning the
steering wheel of vehicle 200 to achieve a rotation of angle 3.
Processing unit 110 may also execute instructions stored in
navigational response module 408 to control throttling system 220
and/or braking system 230 based on the accelerations and/or
decelerations determined in steps 4330 or 4332.
[0715] Returning to step 4326, when processing unit 110 determines
that the error is less than the threshold error (Step 4326: No),
processing unit 110 may proceed to step 4332 of determining the
third ANR based only on the second ANR. As discussed above, when
the error is less than the threshold error, it may be sufficient to
navigate vehicle 200 based only on map 4202. Thus, in one exemplary
embodiment, processing unit 110 may set third ANR equal to second
ANR. In another exemplary embodiment, processing unit 110 may set
third ANR by scaling (i.e. magnifying or attenuating) second ANR
using a scaling factor. After completing step 4332, processing unit
110 may proceed to step 4330 of adjusting steering system 240 based
on third ANR.
[0716] Processing unit 110 and/or image acquisition unit 120 may
repeat process 4300 after a predetermined amount of time. In one
exemplary embodiment, the predetermined amount of time may range
between about 0.5 seconds to 1.5 seconds. By repeatedly determining
a current location 4214 of vehicle 200, determining whether current
location 4214 lies in overlap segment 4220, and determining first
ANR, second ANR, and third ANR based on the location of vehicle
200, processing unit 110 and/or image acquisition unit 120 may help
to navigate vehicle 200, using overlapping road segment 4220 of
local maps 4200, 4202.
[0717] Sparse Map Autonomous Vehicle Navigation
[0718] In some embodiments, the disclosed systems and methods may
use a sparse map for autonomous vehicle navigation. As discussed
above regarding FIGS. 8-11D, the sparse map may provide sufficient
information for navigation without requiring excessive data storage
or data transfer rates. Further, a vehicle (which may be an
autonomous vehicle) may use the sparse map to navigate one or more
roads. For example, as discussed below in further detail, vehicle
200 may determine an autonomous navigational response based on
analysis of the sparse map and at least one image representative of
an environment of vehicle 200.
[0719] In some embodiments, vehicle 200 may access a sparse map
that may include data related to a road on which vehicle 200 is
traveling and potentially landmarks along the road that may be
sufficient for vehicle navigation. As described in sections above,
the sparse data maps accessed by vehicle 200 may require
significantly less storage space and data transfer bandwidth as
compared with digital maps including detailed map information, such
as image data collected along a road. For example, rather than
storing detailed representations of a road segment on which vehicle
200 is traveling, the sparse data map may store three dimensional
polynomial representations of preferred vehicle paths along the
road. A polynomial representation of a preferred vehicle path along
the road may be a polynomial representation of a target trajectory
along a road segment. These paths may require very little data
storage space.
[0720] Consistent with disclosed embodiments, an autonomous vehicle
system may use a sparse map for navigation. As discussed earlier,
at the core of the sparse maps, one or more three-dimensional
contours may represent predetermined trajectories that autonomous
vehicles may traverse as they move along associated road segments.
As also discussed earlier, the sparse maps may also include other
features, such as one or more recognized landmarks, road signature
profiles, and any other road-related features useful in navigating
a vehicle.
[0721] In some embodiments, an autonomous vehicle may include a
vehicle body and a processor configured to receive data included in
a sparse map and generate navigational instructions for navigating
the vehicle along a road segment based on the data in the sparse
map.
[0722] As discussed above in connection with FIG. 8, vehicle 200
(which may be an autonomous vehicle) may access sparse map 800 to
navigate. As shown in FIG. 8, in some embodiments, sparse map 800
may be stored in a memory, such as memory 140 or 150. For example,
sparse map 800 may be stored on a storage device or a
non-transitory computer-readable medium provided onboard vehicle
200 (e.g., a storage device included in a navigation system onboard
vehicle 200). A processor (e.g., processing unit 110) provided on
vehicle 200 may access sparse map 4400 stored in the storage device
or computer-readable medium provided onboard vehicle 200 in order
to generate navigational instructions for guiding the autonomous
vehicle 200 as it traverses a road segment.
[0723] In some embodiments, sparse map 800 may be stored remotely.
FIG. 44 shows an example of vehicle 200 receiving data from a
remote server 4400, consistent with disclosed embodiments. As shown
in FIG. 44, remote server 4400 may include a storage device 4405
(e.g., a computer-readable medium) provided on remote server 4400
that communicates with vehicle 200. For example, remote server 4400
may store a sparse map database 4410 in storage device 4405. Sparse
map database 4410 may include sparse map 800. In some embodiments,
sparse map database 4410 may include a plurality of sparse maps.
Sparse map database 4410 may be indexed based on certain regions
(e.g., based on geographical boundaries, country boundaries, state
boundaries, etc.) or based on any appropriate parameter (e.g., type
or size of vehicle, climate, etc.). Vehicle 200 may communicate
with remote server 440 via one or more networks (e.g., over a
cellular network and/or the Internet, etc.) through a wireless
communication path. In some embodiments, a processor (e.g.,
processing unit 110) provided on vehicle 200 may receive data
included in sparse map database 4410 over one or more networks from
remove server 4400. Furthermore, vehicle 200 may execute
instructions for navigating vehicle 200 using sparse map 800, as
discussed below in further detail.
[0724] As discussed above in reference to FIG. 8, sparse map 800
may include representations of a plurality of target trajectories
810 for guiding autonomous driving or navigation along a road
segment. Such target trajectories may be stored as
three-dimensional splines. The target trajectories stored in sparse
map 800 may be determined based on two or more reconstructed
trajectories of prior traversals of vehicles along a particular
road segment. A road segment may be associated with a single target
trajectory or multiple target trajectories. For example, on a two
lane road, a first target trajectory may be stored to represent an
intended path of travel along the road in a first direction, and a
second target trajectory may be stored to represent an intended
path of travel along the road in another direction (e.g., opposite
to the first direction). Additional target trajectories may be
stored with respect to a particular road segment.
[0725] Sparse map 800 may also include data relating to a plurality
of predetermined landmarks 820 associated with particular road
segments, local maps, etc. As discussed in detail in other
sections, these landmarks may be used in navigation of vehicle 200.
For example, in some embodiments, the landmarks may be used to
determine a current position of vehicle 200 relative to a stored
target trajectory. With this position information, vehicle 200 may
be able to adjust a heading direction to match a direction of the
target trajectory at the determined location.
[0726] Landmarks may include, for example, any identifiable, fixed
object in an environment of at least one road segment or any
observable characteristic associated with a particular section of a
particular road segment. In some cases, landmarks may include
traffic signs (e.g., speed limit signs, hazard signs, etc.). In
other cases, landmarks may include road characteristic profiles
associated with a particular section of a road segment. Further
examples of various types of landmarks are discussed in previous
sections, and some landmark examples are shown and discussed above
in connection with FIG. 10.
[0727] FIG. 45 shows vehicle 200 navigating along a multi-lane road
consistent with disclosed embodiments. Here, a vehicle 200 may
navigate road segments present within a geographic region 1111
shown previously in FIG. 11B. As previously discussed in relation
to FIG. 11B, road segment 1120 may include a multilane road with
lanes 1122 and 1124, double yellow line 1123, and branching road
segment 1130 that intersects with road segment 1120. Geographic
region 1111 may also include other road features, such as a stop
line 1132, a stop sign 1134, a speed limit sign 1136, and a hazard
sign 1138.
[0728] FIG. 46 shows vehicle 200 navigating using target
trajectories along a multi-lane road consistent with disclosed
embodiments. A vehicle 200 may navigate geographic region 1111
shown previously in FIG. 11B and FIG. 45, using target trajectory
4600. Target trajectory 4600 may be included in a local map (e.g.,
local map 1140 of FIG. 11C) of sparse map 800, and may provide a
target trajectory for one or more lanes associated with a road
segment. As previously discussed, sparse map 800 may include
representations of road-related features associated with geographic
region 1111, such as representations of one or more landmarks
identified in geographic region 1111. Such landmarks may include
speed limit sign 1136 and hazard sign 1138. Vehicle 200 may use
speed limit sign 1136 and hazard sign 1138 to assist in determining
its current location relative to target trajectory 4600. Based on
the determined current location of vehicle 200 relative to target
trajectory 4600, vehicle 200 may adjust its heading to match a
direction of the target trajectory at the determined location.
[0729] As discussed above, in some embodiments, sparse may 800 may
also include road signature profiles. Such road signature profiles
may be associated with any discernible/measurable variation in at
least one parameter associated with a road. For example, in some
cases, such profiles may be associated with variations in surface
roughness of a particular road segment, variations in road width
over a particular road segment, variations in distances between
dashed lines painted along a particular road segment, variations in
road curvature along a particular road segment, etc.
[0730] FIG. 47 shows an example of a road signature profile 1160
associated with vehicle 200 as it travels on the road shown in
FIGS. 45 and 46. While profile 1160 may represent any of the
parameters mentioned above, or others, in relation to vehicle 200,
in one example, profile 1160 may represent a measure of road
surface roughness obtained by monitoring one or more sensors
providing outputs indicative of an amount of suspension
displacement as a vehicle 200 travels a road segment in FIG. 46.
Alternatively, profile 1160 may represent variation in road width,
as determined based on image data obtained via a camera onboard
vehicle 200 traveling in a road segment in FIG. 46. Such profiles
may be useful, for example, in determining a particular location of
vehicle 200 relative to target trajectory 4600, and may aid in
navigation of vehicle 200. That is, as vehicle 200 traverses a road
segment of FIG. 46, vehicle 200 may measure a profile associated
with one or more parameters associated with that road segment. If
the measured profile can be correlated/matched with a predetermined
profile that plots the parameter variation with respect to position
along the road segment, then the measured and predetermined
profiles may be used by vehicle 200 (e.g., by overlaying
corresponding sections of the measured and predetermined profiles)
in order to determine a current position along the road segment
and, therefore, a current position relative to target trajectory
4600 for the road segment. Measurements of the profile by vehicle
200 may continue as vehicle 200 travels in lane 1124 of FIG. 46 in
order to continuously determine a current position along the road
segment and a current position of vehicle 200 relative to target
trajectory 4600. As such, navigation of vehicle 200 may be
provided.
[0731] FIG. 48 is an illustration of an example of a portion of a
road environment 4800, as shown in FIGS. 45 and 46. In this
example, FIG. 48 shows road segment 1120. Vehicle 200 may be
traveling along road segment 1120. Along the road segment 1120,
landmarks such as speed limit sign 1136 and hazard sign 1138 may be
present. Speed limit sign 1136 and hazard sign 1138 may be
recognized landmarks that are stored in sparse map 800, and may be
used for autonomous vehicle navigation along road segment 1120
(e.g., for locating vehicle 200, and/or for determining a target
trajectory of vehicle 200). Recognized landmarks 1136 and 1138 in
sparse map 800 may be spaced apart from each other at a certain
rate. For example, recognized landmarks may be spaced apart in the
sparse map at a rate of no more than 0.5 per kilometer, at a rate
of no more than 1 per kilometer, or at a rate of no more than 1 per
100 meters. Landmarks 1136 and 1138 may be used, for example, to
assist vehicle 200 in determining its current location relative to
target trajectory 4600, such that the vehicle may adjust its
heading to match a direction of the target trajectory at the
determined location.
[0732] FIG. 49 is a flow chart showing an exemplary process 4900
for sparse map autonomous navigation consistent with the disclosed
embodiments. Processing unit 110 may utilize one of or both of
application processor 180 and image processor 190 to implement
process 4900. As discussed below in further detail, vehicle 200 may
determine an autonomous navigational response based on analysis of
a sparse map and at least one image representative of an
environment of vehicle 200.
[0733] At step 4902, processing unit 110 may receive a sparse map
of a road segment, such as sparse map 800, from memory 140 or 150.
For example, the sparse map may be transmitted to processing unit
110 based on a calculation of the position of vehicle 200 by
position sensor 130. In other exemplary embodiments, vehicle 200
may receive the sparse map from remote server 4400. The sparse map
data may have a particular data density. The data density of the
sparse map may be expressed in terms of data unit per unit
distance. For example, the sparse map may have a data density of no
more than 1 megabyte per kilometer. In another example, the sparse
map may have a data density of no more than 100 kilobytes per
kilometer. In another example, the sparse map may have a data
density of no more than 10 kilobytes per kilometer. Data density
may be expressed in terms of any conceivable data unit and unit
distance. Further, the sparse map may include a polynomial
representation of a target trajectory along the road segment.
[0734] At step 4904, processing unit 110 may receive at least one
image representative of an environment of vehicle 200. For example,
processing unit 110 may receive at least one image from image
acquisition unit 120 using image capture device 122. In other
exemplary embodiments, image acquisition unit 120 may acquire one
or more images from one or more of image capture devices 122, 124,
and 126. Image acquisition unit 120 may transmit the one or more
images to processing unit 110 over a data connection (e.g.,
digital, wired, USB, wireless, Bluetooth, etc.).
[0735] At step 4906, processing unit 110 may analyze the received
sparse map and the at least one image of the environment of vehicle
200. For example, processing unit 110 may execute monocular image
analysis module 402 to analyze one or more images, as described in
further detail in connection with FIGS. 5B-5D. By performing the
analysis, processing unit 110 may detect a set of features within
the set of images, for example, one or more landmarks, such as
landmarks 1134, 1136, and 1138. As discussed earlier, landmarks may
include one or more traffic signs, arrow markings, lane markings,
dashed lane markings, traffic lights, stop lines, directional
signs, reflectors, landmark beacons, lampposts, a change is spacing
of lines on the road, signs for businesses, and the like.
Furthermore, processing unit 110 may analyze the sparse map to
determine that an object in one or more images is a recognized
landmark. For example, processing unit 110 may compare the image of
the object to data stored in the sparse map. Based on the
comparison, the image processor 190 may determine whether or not
the object is a recognized landmark. Processing unit 110 may use
recognized landmarks from captured image data of the environment
and/or GPS data to determine a position of vehicle 200. Processing
unit 110 may then determine a position of vehicle 200 relative to a
target trajectory of the sparse map.
[0736] At step 4908, processing unit 110 may cause one or more
navigational responses in vehicle 200 based solely on the analysis
of the sparse map and at least one image of the environment
performed at step 4906. For example, processing unit 110 may select
an appropriate navigational response based on the position of
vehicle 200 relative to the target trajectory of the sparse map.
Navigational responses may include, for example, a turn, a lane
shift, a change in acceleration, and the like. Processing unit 110
may cause system 100 to provide inputs (e.g., control signals) to
one or more of throttling system 220, braking system 230, and
steering system 240 as shown in FIG. 2F to navigate vehicle 200
(e.g., by causing an acceleration, a turn, a lane shift, etc.) to
provide a navigational response. System 100 may provide inputs to
one or more of throttling system 220, braking system 230, and
steering system 240 over one or more data links (e.g., any wired
and/or wireless link or links for transmitting data). Additionally,
multiple navigational responses may occur simultaneously, in
sequence, or any combination thereof. For instance, processing unit
110 may cause vehicle 200 to shift one lane over and then
accelerate by, for example, sequentially transmitting control
signals to steering system 240 and throttling system 220 of vehicle
200. Alternatively, processing unit 110 may cause vehicle 200 to
brake while at the same time shifting lanes by, for example,
simultaneously transmitting control signals to braking system 230
and steering system 240 of vehicle 200.
[0737] Navigation Based on Expected Landmark Location
[0738] Landmarks appearing in one or more images captured by a
camera onboard a vehicle may be used in the disclosed embodiments
to determine a location of a vehicle along a road model trajectory.
Such landmarks may include recognized landmarks represented, for
example, in sparse map 800. Processing unit 110 of vehicle 200 may
analyze images captured from one or more cameras onboard vehicle
200 to look for and verify the presence of a recognized landmark
(from sparse data map 800) in the captured images. According to
techniques described in detail in other sections of the disclosure,
the verified, recognized landmarks in the environment of the
vehicle can then be used to navigate the vehicle (e.g., by enabling
a determination of a position of vehicle 200 along a target
trajectory associated with a road segment).
[0739] In the disclosed embodiments, however, processor unit 110
may also generate navigational instructions on not only those
landmarks appearing in captured images, but also based on an
expected location of the recognized landmark as conveyed by sparse
data map 800. For example, braking of a vehicle may be initiated a
certain distance from recognized landmarks such as a stop line, a
traffic light, a stop sign, a sharp curve, etc., even before those
landmarks are detectable via an on-board camera. Landmarks may
include, for example, any identifiable, fixed object in an
environment of at least one road segment or any observable
characteristic associated with a particular section of the road
segment. In some cases, landmarks may include traffic signs (e.g.,
speed limit signs, hazard signs, etc.). In other cases, landmarks
may include road characteristic profiles associated with a
particular section of a road segment. Further examples of various
types of landmarks are discussed in previous sections, and some
landmark examples are shown in FIG. 10.
[0740] FIG. 50 illustrates an example environment consistent with
the disclosed embodiments. Vehicle 200 (which may be an autonomous
vehicle) may travel along a target road model trajectory 5002 in
road 5004. Vehicle 200 may be equipped with one or more image
capture devices (e.g., one or more of image capture device 122,
124, or 126) that capture an image of the environment of the
vehicle. The one or more image capture devices may have a sight
range 5006. Sight range 5006 may define a range at which an image
capture device of vehicle 200 can capture accurate images of the
environment around vehicle 200. For example, sight range 5006 may
define the range at which the field of view, focal length,
resolution focus, sharpness, image quality, and the like of the
image capture device of vehicle 200 is sufficient to provide images
for navigation of vehicle 200. Region 5008 may define a range
outside of the sight range 5006 of an image capture device of
vehicle 200. In region 5008, an image capture device of vehicle 200
may not be able to capture images of the environment around vehicle
200 that are sufficient to allow navigation of vehicle 200. In
other exemplary embodiments, each image capture device may have a
different sight range.
[0741] As shown in FIG. 50, recognized landmark 5010 is within
sight range 5006. Because recognized landmark 5010 is within sight
range 5006, it may be captured by an image capture device of
vehicle 200 and identified, and used to navigate vehicle 200.
Recognized landmark 5010 may be identified by vehicle 200 according
to techniques discussed above in connection with, for example,
FIGS. 34-36.
[0742] As previously discussed, recognized landmark 5012 is within
region 5008. However, region 5008 defines a range outside of the
sight range 5006 of an image capture device of vehicle 200.
Accordingly, vehicle 200 may not be able to identify recognized
landmark 5012 using an image capture device of vehicle 200 because
recognized landmark 5012 is out of the sight range of the image
capture device.
[0743] Consistent with disclosed embodiments, vehicle 200 may
identify recognized landmark 5012 using alternative techniques. For
example, an image capture device of vehicle 200 may capture an
image of the environment within sight range 5006. A processor of
vehicle 200 (e.g., processing unit 110) may receive the image. The
processor may then determine a position of vehicle 200 along
predetermined road model trajectory 5002 in road 5004 based on the
captured image. For example, as discussed in other sections, the
processor may compare data information representing recognized
landmark 5010 from the captured image of the environment to stored
data, such as data stored in sparse map 800, discussed above, to
determine a position of vehicle 200 along predetermined road model
trajectory 5002 in road 5004.
[0744] Based on the determined position of vehicle 200, the
processor may then identify a recognized landmark beyond sight
range 5006 (e.g., recognized landmark 5012) forward of the vehicle
200. For example, by accessing information stored in sparse data
map 800 or any portion of sparse data map 800 (e.g., any received
local map portions of sparse data map 800) processing unit 110 of
vehicle 200 may determine the next expected recognized landmark to
be encountered by vehicle 200 (or any other recognized landmark to
be encountered by vehicle 200). The processor may also determine a
predetermined position of recognized landmark 5012 based on the
information available in sparse data map 800. Then, processing unit
110 may determine a current distance 5014 between the vehicle 200
and expected, recognized landmark 5012. The current distance 5014
between the vehicle 200 and the recognized landmark 5012 may be
determined by comparing the determined position of vehicle 200 with
the predetermined position of recognized landmark 5012. Based on
the distance 5014, the processor of vehicle 200 may then determine
an autonomous navigational response for the vehicle. For example,
among other responses, processing unit 110 may initiate braking in
advance of landmark 5012 even prior to detection of landmark 5012
in any captured images from image capture devices onboard vehicle
200.
[0745] FIG. 51 illustrates a configuration 5100 for autonomous
navigation consistent with disclosed embodiments. As discussed
earlier, processing unit 110 may receive images from an image
acquisition unit 120. Image acquisition unit may include one or
more image capture devices (e.g., image capture device 122, 124, or
126). The images may depict an environment of vehicle 200 within
the field of view of an image capture device onboard vehicle
200.
[0746] While GPS data need not be relied upon to determine an
accurate position of vehicle 200 along a target trajectory, GPS
data (e.g., GPS data from GPS unit 5106) may be used as an index
for determining relevant local maps to access from within sparse
data map 800. Such GPS data may also be used as a general index to
aid in verifying an observed recognized landmark.
[0747] FIG. 52 shows an example of an environment 5200 consistent
with the present disclosure. As shown in FIG. 52, a vehicle 200 may
approach a junction 5202 with a stop sign 5204 and a stop line
5210. One or both of stop sign 5204 or stop line 5210 may
correspond to recognized landmarks represented in sparse data map
800. Either or both of stop sign 5204 or stop line 5210 may be
located in a region 5208 beyond a focal length of an image capture
device aboard vehicle 200 or otherwise outside of a usable sight
range of the image capture device. Based on information stored in
sparse data map 800 relative to stop sign 56204 and/or stop line
5210, processing unit 110 may initiate braking based on a
determined, expected distance to stop line 5204 or stop line 5210
even before stop line 5204 or 5210 have been identified in images
received from the image capture device onboard vehicle 200. Such a
navigation technique, for example, may aid in slowing vehicle 200
gradually or according to a predetermined braking profile even
without visual confirmation of a distance to a trigger for braking
(e.g., stop sign 5204, stop line 5210, an expected curve,
etc.).
[0748] FIG. 53 shows another example environment 5300 consistent
with the present disclosure. As shown in FIG. 53, a vehicle 200 may
approach a curve 5302 of road 5304. Vehicle 200 may include an
image acquisition unit (e.g., image acquisition unit 120) including
one or more image capture devices that provide a sight range of
5306. Region 5308 may define a range outside of the sight range
5306 of the image acquisition unit of vehicle 200.
[0749] Vehicle 200 may need to slow down in speed or implement
steering to account for curve 5302 in road 5304. To plan a slowdown
in speed or implement steering, it may be useful to know in advance
where the curve 5302 is located. However, curve 5302 may be located
in region 5308, which is beyond the focal length of an image
capture device aboard vehicle 200. Thus, vehicle 200 may use a
predetermined position of curve 5302, for example, as represented
in sparse data map 800, as well as the position of vehicle 200
along predetermined road model trajectory 5310, to determine a
distance 5320 to curve 5302. This distance may be used to slow
vehicle 200 change a course of vehicle 200, etc. before the curve
appears in images captured by an onboard camera.
[0750] Consistent with disclosed embodiments, to determine distance
5320 to curve 5302, the image acquisition device of vehicle 200 may
capture an image of the environment. The image may include a
recognized landmark 5318. A processor of vehicle 200 (e.g.,
processing unit 110) may receive the image and determine a position
of vehicle 200 along predetermined road model trajectory 5310 based
on the captured image and the position of recognized landmark 5318.
Based on the determined position of vehicle 200, the processor may
then identify curve 5302 beyond sight range 5306 forward of the
vehicle 200 based on information included in sparse data map 800
relevant to curve 5302. Position information included in sparse
data map 800 for curve 5302 may be compared with a determined
position for vehicle 200 along a target trajectory for vehicle 200
to determine a distance between vehicle 200 and curve 5302. This
distance can be used in generating a navigational response for
vehicle 200 prior to identification of curve 5302 within images
captured by a camera onboard vehicle 200.
[0751] FIG. 54 is a flow chart showing an exemplary process 5400
for autonomously navigating vehicle 200 consistent with the
disclosed embodiments. A processing unit (e.g., processing unit
110) of vehicle 200 may use one of or both of application processor
180 and image processor 190 to implement process 5400. As discussed
below in further detail, vehicle 200 may autonomously navigate
along a road segment based on a predetermined landmark location.
Furthermore, the predetermined landmark location may be beyond a
sight range of vehicle 200.
[0752] At step 5402, a processing unit (e.g., processing unit 110)
of vehicle 200 may receive at least one image from an image capture
device (e.g., image capture device 122) of vehicle 200. The at
least one image may be representative of an environment of vehicle
200. The at least one image may include data representative of one
or more landmarks in the environment. For example, the at least one
image may include data representative of landmarks such as road
signs (including stop signs, yield signs, and the like), traffic
lights, general signs, lines on the road, and curves along a road
segment. As discussed in previous sections, a processing unit of
vehicle 200 may verify recognized landmarks that appear in the at
least one image.
[0753] At step 5404, the processing unit of vehicle 200 may
determine a position of vehicle 200. For example, the processing
unit of vehicle 200 may determine a position of vehicle 200 along a
predetermined road model trajectory associated with a road segment
based, at least in part, on information associated with the at
least one image.
[0754] At step 5406, a recognized landmark beyond the focal range
of the image capture device of vehicle 200 and forward of vehicle
200 may be identified. The identification may be based on the
determined position of vehicle 200 along the predetermined road
model trajectory associated with the road segment. For example,
information about recognized landmarks along a predetermined road
model trajectory may be previously stored in a sparse map, such as
sparse map 800, discussed above. Based on the determined position
of vehicle 200 along the predetermined road model trajectory
associated with the road segment, the processing unit of vehicle
200 may determine that one or more recognized landmarks are located
forward of vehicle 200 along the predetermined road model
trajectory, but beyond a sight range of the image capture device of
vehicle 200. Moreover, the processing unit of vehicle 200 may
access a predetermined position of the recognized landmarks by
accessing sparse map 800.
[0755] At step 5408, a current distance between the vehicle and the
recognized landmark located forward of vehicle 200 beyond a sight
range of the image capture device of vehicle 200 may be determined.
The current distance may be determined by comparing the determined
position of vehicle 200 along the predetermined road model
trajectory associated with the road segment to the predetermined
position of the recognized landmark forward of vehicle 200 beyond
the sight range.
[0756] At step 5410, an autonomous navigational response for the
vehicle may be determined based on the determined current distance
between vehicle 200 and the recognized landmark located forward of
vehicle 200. Processing unit 110 may control one or more of
throttling system 220, braking system 230, and steering system 240
to perform a certain navigational response, as discussed in other
sections of this disclosure. For example, an autonomous
navigational response may include sending a control signal to
braking system 230 to provide the application of brakes associated
with vehicle 200. In another example, an autonomous navigational
response may include sending a control signal to steering system
240 to modify a steering angle of vehicle 200.
[0757] Autonomous Navigation Based on Road Signatures
[0758] Consistent with disclosed embodiments, the system may
navigate based on predetermined road signatures without using
landmarks. As discussed above, such road signatures may be
associated with any discernible or measurable variation in at least
one parameter associated with a road. For example, in some cases,
road signatures may be associated with variations in surface
roughness of a particular road segment, variations in road width
over a particular road segment, variations in distances between
dashed lines painted along a particular road segment, variations in
road curvature along a particular road segment, etc. The road
signatures may be identified as a vehicle traverses a road segment
based on visual information (e.g., images obtained from a camera)
or based on other sensor output (e.g., one or more suspension
sensor outputs, accelerometers, etc.). These signatures may be used
to locate the vehicle along a predetermined road profile, and the
forward trajectory can then be determined for the vehicle based on
the direction of the road model at the determined location compared
to a heading direction for the vehicle.
[0759] FIG. 55 is a diagrammatic representation of exemplary
vehicle control systems, consistent with the disclosed embodiments.
As illustrated in FIG. 55, vehicle 200 (which may be an autonomous
vehicle) may include processing unit 110, which may have features
similar to those discussed above with respect to FIGS. 1 and 2F.
Vehicle 200 may also include imaging unit 220, which may also have
features similar to those discussed above with respect to FIGS. 1
and 2F. In addition, vehicle 200 may include one or more suspension
sensors 5500 capable of detecting movement of the suspension of
vehicle 200 relative to a road surface. For example, signals from
suspension sensors 5500 located adjacent each wheel of vehicle 200
may be used to determine a local shape, inclination, or banking of
the road surface over which vehicle 200 may be located. In some
exemplary embodiments, vehicle 200 may additionally or
alternatively include accelerometers or other position sensors that
may acquire information regarding variations in the road surface as
vehicle 200 travels over the road surface. It is also contemplated
that system 100 illustrated in FIG. 55 may include some or all of
the components described above with respect to, for example, FIGS.
1 and 2F.
[0760] FIG. 56 illustrates vehicle 200 travelling on road segment
5600 in which the disclosed systems and methods for navigating
vehicle 200 using one or more road signatures may be used. Road
segment 5600 may include lanes 5602 and 5604. As illustrated in
FIG. 56, lane 5602 may be delimited by road center 5606 and right
side 5608, whereas lane 5604 may be delimited by left side 5610 and
road center 5606. Lanes 5602 and 5604 may have the same or
different widths. It is also contemplated that each of lanes 5602,
5604 may have uniform or non-uniform widths along a length of road
segment 5600. Although FIG. 56 depicts road segment 5600 as
including only two lanes 5602, 5604, it is contemplated that road
segment 5600 may include any number of lanes.
[0761] In one exemplary embodiment as illustrated in FIG. 56,
vehicle 200 may travel along lane 5602. Vehicle 200 may be
configured to travel along predetermined road model trajectory
5612, which may define a preferred path (e.g., a target road model
trajectory) within lane 5602 of road segment 5600. In some
exemplary embodiments, predetermined road model trajectory 5612 may
be located equidistant from road center 5606 and right side 5608.
It is contemplated however that predetermined road model trajectory
5612 may be located nearer to one or the other of center 5606 and
right side 5608 of road segment 5600. In some embodiments, road
model trajectory 5612 may be located elsewhere with respect to the
road. For example, road model trajectory 5612 may be located to
approximately coincide with a center of a roadway, a road edge, a
lane edge, etc.
[0762] In some embodiments, predetermined road model trajectory
5612 may be mathematically represented by a three-dimensional
polynomial function, which may be stored in memories 140, 150
associated with vehicle 200. It is also contemplated that the
three-dimensional polynomial representation of road model
trajectory 5612 may be stored in a storage device located remotely
from vehicle 200. Processing unit 110 of vehicle 200 may be
configured to retrieve predetermined road model trajectory 5612
from storage device over a wireless communications interface.
[0763] Vehicle 200 may be equipped with image capture devices 122,
124, 126 of image acquisition unit 120. It is contemplated that
vehicle 200 may include more or fewer image capture devices than
those shown in FIG. 56. Image capture devices 122, 124, 126 may be
configured to acquire a plurality of images representative of an
environment of vehicle 200, as vehicle 200 travels along road
segment 5600. For example, one or more of image capture devices
122, 124, 126 may obtain the plurality of images showing views
forward of vehicle 200. Processing unit 110 of vehicle 200 may be
configured to detect a location of vehicle 200 at vehicle travels
along road segment 5600 based on the one or more images obtained by
image capture devices 122, 124, 126 or based on signals received
from, for example, suspension sensor 5500.
[0764] As illustrated in FIG. 56, vehicle 200 may travel via
locations 5622, 5624, 5626, to current location 5628. Although only
three prior locations 5622-5626 are illustrated in FIG. 56, one of
ordinary skill in the art would recognize that any number of
previous locations of vehicle 200 may be present on road segment
5600. Processing unit 110 may analyze the one or more images
received from image capture devices 122, 124, 126, to determine,
for example, road widths W.sub.p1, W.sub.p2, W.sub.p3, W.sub.c at
locations 5622, 5624, 5626, 5628, respectively, where the subscript
"p" refers to a previous location and the subscript "c" refers to
current location 5628 of vehicle 200. In some exemplary
embodiments, processing unit 110 may additionally or alternatively
determine, for example, lane widths D.sub.p1, D.sub.p2, D.sub.p3,
D.sub.c at locations 5622, 5624, 5626, 5628, respectively.
Processing unit 110 may generate a road width profile or a lane
width profile over portion 5614 of road segment 5600. The
determined road width profile or lane width profile may correspond
to current location 5628.
[0765] FIG. 57 illustrates an exemplary profile 5700 generated by
processing unit 110 of vehicle 200. As illustrated in FIG. 57, road
width, lane width, or other parameters may be charted on the y-axis
against a distance travelled by vehicle 200 along road segment 5600
on the x-axis. Processing unit 110 may determine the distance
travelled using systems and methods similar to those discussed
above with respect to FIGS. 34-36.
[0766] Processing unit 110 may determine a local feature of road
segment 5600 corresponding to current location 5628 of vehicle 200.
For example, processing unit 110 may determine a mathematical
representation for the profile (e.g. profile shown in FIG. 57) by
curve fitting the determined road widths W.sub.p1, W.sub.p2,
W.sub.p3, W.sub.c and/or lane widths D.sub.p1, D.sub.p2, D.sub.p3,
D.sub.c. In one exemplary embodiment, processing unit 110 may
determine, for example, coefficients (e.g. a.sub.1, a.sub.2, . . .
a.sub.n) associated with the curve fit of the road width profile or
the lane width profile. The determined coefficients may represent
the local feature of road segment 5600 at current location 5628. In
another exemplary embodiment, processing unit 110 may determine a
slope of profile 5700 as the local feature. It is contemplated that
processing unit 110 may perform other mathematical operations on
profile 5700 to determine the local feature of road segment 5600
corresponding to a current location of vehicle 200.
[0767] Processing unit 110 may retrieve a predetermined signature
feature associated with road segment 5600, for example, from
database 160 stored in memories 140, 150. In one exemplary
embodiment, the predetermined signature features may include
coefficients of best or preferred fit lines representing road width
profiles or lane width profiles corresponding to various locations
along predetermined road model trajectory 5612. For example, the
predetermined signature features may include coefficients b.sub.1,
b.sub.2, . . . b.sub.n at location 1; c.sub.1, c.sub.2, c.sub.n at
location 2; d.sub.1, d.sub.2, . . . d.sub.n at location 3, etc.
Processing unit 110 may compare the coefficients (e.g. a.sub.1,
a.sub.2, . . . a.sub.n) determined based on road widths W.sub.p1,
W.sub.p2, W.sub.p3, W.sub.c and/or lane widths D.sub.p1, D.sub.p2,
D.sub.p3, D.sub.c with the coefficients (e.g. b.sub.1, b.sub.2,
b.sub.n; c.sub.1, c.sub.2, . . . c.sub.n; d.sub.1, d.sub.2, . . .
d.sub.n; etc.). Processing unit 110 may determine current location
5628 of vehicle 200 based on a match of the coefficients. For
example, if coefficients a.sub.1, a.sub.2, . . . a.sub.n match with
coefficients c.sub.1, c.sub.2, . . . c.sub.n, respectively,
processing unit 110 may determine location 5628 of vehicle 200 as
corresponding to location 2 of predetermined road model trajectory
5612.
[0768] Processing unit 110 may determine a match in many ways. In
one exemplary embodiment, processing unit 110 may determine a
distance measure between the coefficients (e.g. a.sub.1, a.sub.2, .
. . a.sub.n) and each set of coefficients (e.g. b.sub.1, b.sub.2,
b.sub.n; c.sub.1, c.sub.2, . . . c.sub.n; d.sub.1, d.sub.2, . . .
d.sub.n; etc.) corresponding to locations 1, 2, 3, etc. Processing
unit 110 may determine that there is a match when at least one of
the determined distance measures is less than a threshold distance.
In other exemplary embodiments, processing unit 110 may determine
an error between the coefficients (e.g. a.sub.1, a.sub.2, . . .
a.sub.n) and each set of coefficients (e.g. b.sub.1, b.sub.2, . . .
b.sub.n; c.sub.1, c.sub.2, c.sub.n; d.sub.1, d.sub.2, d.sub.n;
etc.) corresponding to locations 1, 2, 3, etc. Processing unit 110
may determine a match when at least one error is less than a
threshold error. One of ordinary skill in the art would recognize
that processing unit 110 may use other mathematical computations to
determine a correlation or match between the two sets of
coefficients.
[0769] In some exemplary embodiments, processing unit 110 may use
road width W4 and/or lane width w.sub.4 as local feature to
determine current location 5628 of vehicle 500. For example, the
predetermined signature features of road segment 5600 may include
road widths w.sub.1, w.sub.2, w.sub.3, w.sub.4, w.sub.5, . . .
w.sub.n corresponding to locations 1, 2, 3, 4, 5, . . . n along
predetermined road model trajectory 5612. Additionally, or
alternatively, the predetermined signature features of road segment
5600 may include lane widths d.sub.1, d.sub.2, d.sub.3, d.sub.4,
d.sub.5, . . . d.sub.n corresponding to locations locations 1, 2,
3, 4, 5, . . . n along predetermined road model trajectory 5612.
Processing unit 110 may compare road width W.sub.c and/or lane
width D.sub.c with road widths w.sub.1, w.sub.2, w.sub.3, w.sub.4,
w.sub.5, . . . w.sub.n and/or lane widths d.sub.1, d.sub.2,
d.sub.3, d.sub.4, d.sub.5, . . . d.sub.n, respectively, to
determine current location 5628. For example, if road width W.sub.c
matches with road width w.sub.5, processing unit 110 may determine
location 5628 as corresponding to location 5. Likewise, if lane
width D.sub.c matches with lane width d.sub.3, processing unit 110
may determine location 5628 as corresponding to location 3.
Processing unit may determine whether road width W.sub.4 and/or
lane width D.sub.4 and match using matching techniques similar to
those discussed above.
[0770] Processing unit 110 may use other parameters to determine
current location 5628. For example, processing unit 110 may
determine one or more of average road width W.sub.avg (e.g. average
of W.sub.p1, W.sub.p2, W.sub.p3, W.sub.c), road width variance
W.sub.var (e.g. variance of W.sub.p1, W.sub.p2, W.sub.p3, W.sub.c),
average lane width D.sub.avg (e.g. average of D.sub.p1, D.sub.p2,
D.sub.p3, D.sub.c), lane width variance D.sub.var (e.g. variance of
D.sub.p1, D.sub.p2, D.sub.p3, D.sub.c), or other parameters such as
median, mode, etc. to represent the local feature corresponding to
current location 5628. The corresponding predetermined road
signature feature may also be represented by average road widths,
road width variances, average lane widths, lane width variances,
median or mode values of road widths, median or mode values of lane
widths, etc., at predetermined locations on predetermined road
model trajectory 5612. Processing unit 110 may determine current
location 5628 of vehicle 200 by comparing the determined local
feature and the predetermined road signature features as discussed
above.
[0771] In some exemplary embodiments, the local features and
predetermined signature features of road segment 5600 may be based
on lengths of, or spacing between, marks on road (road markings)
segment 5600. FIG. 58 illustrates vehicle 200 travelling on road
segment 5600 in which the predetermined road signatures may be
based on road markings on road segment 5600. For example, FIG. 58
illustrates road center 5606 as a dashed line represented by road
markings 5802-5816. As vehicle 200 travels along road segment 5600,
processing unit 110 may analyze the one or more images received
from the one or more image capture devices 122, 124, 126, etc. to
detect road markings 5802-5816. Processing unit 110 may also
determine, for example, spacings S.sub.p1, S.sub.p2, S.sub.p3,
S.sub.c between road markings 5802-5804, 5804-5806, 5806-5808,
5808-5810, respectively. Processing unit 110 may additionally or
alternatively determine lengths L.sub.p1, L.sub.p2, L.sub.p3,
L.sub.c for road markings 5802, 5804, 5806, 5808, respectively. In
one exemplary embodiment, processing unit may generate a dashed
line spacing profile or a dashed line length profile based on
spacings S.sub.p1, S.sub.p2, S.sub.p3, S.sub.c or lengths L.sub.p1,
L.sub.p2, L.sub.p3, L.sub.c, respectively, in a manner similar to
the profiles discussed above with respect to FIGS. 56 and 57.
Processing unit 110 may also determine a local feature based on
coefficients of curve fits to the dashed line spacing profile
and/or dashed line length profile as discussed above with respect
to FIGS. 56 and 57. Processing unit 110 may compare the local
feature (e.g., coefficients representing the dashed line spacing
profile or dashed line length profile) to predetermined signature
features of road segment 5600. For example, processing unit 110 may
compare the coefficients representing the determined dashed line
spacing profile or dashed line length profile with predetermined
coefficients of dashed line spacing/length profiles at known
locations along predetermined road model trajectory. Processing
unit 110 may determine current location 5628 of vehicle 200 when
the coefficients of the determined dashed line spacing/length
profiles match the predetermined coefficients at a particular known
location as discussed above with respect to FIGS. 56 and 57.
[0772] In some exemplary embodiments, processing unit 110 may use
dashed line spacing S.sub.c and/or dashed line length L.sub.c as a
local feature to determine current location 5628 of vehicle 500.
For example, the predetermined signature features of road segment
5600 may include dashed lane spacings s.sub.1, s.sub.2, s.sub.3,
s.sub.4, s.sub.5, . . . s.sub.n corresponding to locations 1, 2, 3,
4, 5, . . . n along predetermined road model trajectory 5612.
Additionally, or alternatively, the predetermined signature
features of road segment 5600 may include dashed line lengths
l.sub.1, l.sub.2, l.sub.3, l.sub.4, l.sub.5, . . . l.sub.n
corresponding to locations 1, 2, 3, 4, 5, . . . n along
predetermined road model trajectory 5612. Processing unit 110 may
compare dashed line spacing S.sub.c and/or dashed line length
L.sub.c with dashed lane spacings s.sub.1, s.sub.2, s.sub.3,
s.sub.4, s.sub.5, . . . s.sub.n and/or dashed line lengths l.sub.1,
l.sub.2, l.sub.3, l.sub.4, l.sub.5, . . . l.sub.n, respectively, to
determine current location 5628. For example, if dashed line
spacing S.sub.c matches with dashed line spacing s.sub.5,
processing unit 110 may determine location 5628 as corresponding to
location 5. Likewise, if dashed line length L.sub.c matches with
dashed line length l.sub.3, processing unit 110 may determine
location 5628 as corresponding to location 3. Processing unit may
determine whether dashed line spacing S.sub.c and/or dashed lane
length L.sub.c match the predetermined dashed line lengths or
spacings using matching techniques similar to those discussed
above.
[0773] In other exemplary embodiments, processing unit 110 may
determine an average dash line length L.sub.avg, dash line variance
L.sub.var, dash line spacing average S.sub.avg, or dash line
spacing variance S.sub.var as a local parameter. Processing unit
110 may compare dash mark length L.sub.avg, dash mark variance
lL.sub.var, dash mark spacing average S.sub.avg, or dash mark
spacing variance S.sub.var with predetermined values of dash mark
length, dash mark variance, dash mark spacing average, or dash mark
spacing variance at various locations along predetermined road
model trajectory 5612. The predetermined values of dash mark
length, dash mark variance, dash mark spacing average, or dash mark
spacing variance at various locations may constitute predetermined
signature features of road segment 5600. Processing unit 110 may
determine current location 5628 of vehicle 200 as the location for
which at least one of dash mark length L.sub.avg, dash mark
variance L.sub.var, dash mark spacing average S.sub.avg, or dash
mark spacing variance S.sub.var matches a predetermined
corresponding value of dash mark length, dash mark variance, dash
mark spacing average, or dash mark spacing.
[0774] In yet other exemplary embodiments, processing unit 110 may
use a number of dashed lines as a local feature. For example, road
markings 5802-5816 may be painted at a fixed length and spacing
when they are painted by a machine. Thus, it may be possible to
determine current location 5628 of vehicle 200 based on a count of
the road markings as vehicle 200 travels on road segment 5600.
Processing unit 110 may determine a count "N.sub.c" of dash marks
that vehicle 200 may have passed till it reaches current location
5628. Processing unit 110 may compare count N.sub.c with counts
n.sub.1, n.sub.2, n.sub.3, . . . n.sub.n corresponding to the
number of road markings up to locations 1, 2, 3, . . . n,
respectively, along predetermined road model trajectory 5612.
Counts n.sub.1, n.sub.2, n.sub.3, . . . n.sub.n may correspond to
the predetermined signature feature of road segment 5600. In one
example, when count N.sub.c matches n.sub.2, processing unit may
determine current location 5628 as corresponding to location 2.
[0775] In some exemplary embodiments, the local features and
predetermined signature features of road segment 5600 may be based
on radii of curvature of the predetermined road model trajectory
and an actual trajectory travelled by vehicle 200. For example, as
illustrated in FIG. 59, vehicle 200 may travel over road segment
5900, which may include lanes 5902 and 5904. As illustrated in FIG.
59, lane 5902 may be delimited by road center 5906 and right side
5908, whereas lane 5904 may be delimited by left side 5910 and road
center 5906. Vehicle 200 may be configured to travel along
predetermined road model trajectory 5912, which may define a
preferred path (e.g., a target road model trajectory) within lane
5902 of road segment 5900 that vehicle 200 may follow as vehicle
200 travels along road segment 5900. As also illustrated in FIG.
59, vehicle 200 may travel via previous locations 5922, 5924, 5926,
5928, 5930, 5932 to current location 5934. Although only six
previous locations 5922-5932 are illustrated in FIG. 59, one of
ordinary skill in the art would recognize that any number of
previous locations of vehicle 200 may be present on road segment
5900.
[0776] Processing unit 110 may determine travelled trajectory 5914
of vehicle 200 as passing through previous locations 5922-5932 of
vehicle 200. In one exemplary embodiment, processing unit 110 may
fit a curve, which may be a three-dimensional polynomial similar to
that representing predetermined road model trajectory 5912, through
locations 5922-5932. Processing unit 110 may also determine first
parameter values representative of curvatures of various segments
(portions or sections) of predetermined road model trajectory 5912.
Further, processing unit 110 may determine second parameter values
representative of a curvature of travelled trajectory 5914.
Processing unit 110 may determine current location 5934 of vehicle
200 based on the first and second parameter values.
[0777] For example, consider the case where R.sub.1, R.sub.2,
R.sub.3, . . . R.sub.z represent radii of curvature of segments
C.sub.1, C.sub.2, C.sub.3, . . . C.sub.z of predetermined road
model trajectory 5912. Referring to FIG. 59, portions C.sub.1,
C.sub.2, C.sub.3, . . . C.sub.z of predetermined road model
trajectory 5912 may represent sections of predetermined road model
trajectory 5912 between locations 5922-5944, 5922-5946, 5922-5948,
etc. Processing unit may determine, for example, a radius of
curvature R.sub.t of travelled trajectory 5914 between locations
5922 and 5934. Processing unit 110 may compare radius of curvature
R.sub.t with the radii R.sub.1, R.sub.2, R.sub.3, . . . R.sub.z.
Processing unit 110 may determine current location 5934 of vehicle
200 as a location 5970 when radius of curvature R.sub.t matches the
radius of curvature R.sub.p of a portion of predetermined road
model trajectory lying between location 5922 and 5970. Processing
unit 110 may determine a match between radii R.sub.t and R.sub.p
using matching techniques similar to those discussed above.
[0778] FIG. 60 is a flowchart showing an exemplary process 6000,
for navigating vehicle 200 along road segment 5900 (or 5600), using
road signatures, consistent with disclosed embodiments. Steps of
process 6000 may be performed by one or more of processing unit 110
and image acquisition unit 120, with or without the need to access
memory 140 or 150. The order and arrangement of steps in process
6000 is provided for purposes of illustration. As will be
appreciated from this disclosure, modifications may be made to
process 6000 by, for example, adding, combining, removing, and/or
rearranging the steps for the process.
[0779] As illustrated in FIG. 60, process 6000 may include a step
6002 of receiving information regarding one or more aspects of road
segment 5900 (or 5600) from a sensor. In one exemplary embodiment,
sensor may include one or more of image capture devices 122, 124,
126, which may acquire one or more images representative of an
environment of the vehicle. In one exemplary embodiment, image
acquisition unit 120 may acquire one or more images of an area
forward of vehicle 200 (or to the sides or rear of a vehicle, for
example). For example, image acquisition unit 120 may obtain an
image using image capture device 122 having a field of view 202. In
other exemplary embodiments, image acquisition unit 120 may acquire
images from one or more of image capture devices 122, 124, 126,
having fields of view 202, 204, 206. Image acquisition unit 120 may
transmit the one or more images to processing unit 110 over a data
connection (e.g., digital, wired, USB, wireless, Bluetooth,
etc.).
[0780] In another exemplary embodiment, the sensor may include one
or more suspension sensors 5500 on vehicle 200. Suspension sensor
5500 may be configured to generate signals responsive to a movement
of the suspension of vehicle 200 relative to a surface of road
segment 5900 (or 5600). Processing unit 110 may receive signals
from the one or more suspension sensors 5500 on vehicle 200 as
vehicle 200 moves along road segment 5900 (or 5600). For example,
processing unit 110 may receive information regarding the relative
height of vehicle 200 adjacent each of its wheels based on
suspension sensors 5500 located adjacent to the wheels. Processing
unit 110 may use this information to determine a road surface
profile at the location of vehicle 200. The road surface profile
may provide information regarding a bank or inclination of, for
example, lane 5902 (or 5602) relative to road center 5906 or right
side 5908. In some embodiments, the road surface profile may also
identify a bump in road segment 5900 (or 5600) based on the signals
from the one or more suspension sensors 5500.
[0781] Process 6000 may also include a step 6004 of determining a
local feature based on the information received from the sensor
(e.g. imaging unit 110, one or more suspension sensors 5500, etc.).
The local feature may represent one or more aspects of the road
segment at current location 5628 or 5932 of vehicle 200. For
example, the local feature may include at least one of a road
width, a lane width, or a road surface profile at current location
5932 of vehicle 200. In some exemplary embodiments, the local
feature may be based on data collected by processing unit 110 as
vehicle travels along predetermined road model trajectory to
current location 5932. In particular, based on road widths, lane
widths, lengths of road markings (dashes), spacing between adjacent
road markings (dashes), etc., determined as vehicle travels to
current location 5932, processing unit 110 may determine a road
width profile, a lane width profile, a dashed line length profile,
a dashed line spacing profile, second parameter values representing
a curvature of travelled trajectory 5914, and/or other parameters
as discussed above with respect to FIGS. 55-58.
[0782] Process 6000 may include a step 6006 of receiving
predetermined signature features for road segment 5900. For
example, processing unit 110 may retrieve the predetermined
signature features from a database 160 stored in memories 140, 150
associated with vehicle 200 or from a database 160 located remotely
from vehicle 200. As discussed above with respect to FIGS. 56-59,
the predetermined signature features may include one or more
predetermined parameter values representing at least one of a road
width, a lane width, a dashed line length, a dashed line spacing,
etc., at predetermined locations along predetermined road model
trajectory 5912. In some exemplary embodiments, the predetermined
signature features may also include one or more of a road width
profile over at least a portion of the road segment, a lane width
profile over at least a portion of the road segment, a dashed line
spacing profile over at least a portion of the road segment, a
predetermined number of road markings along at least a portion of
the road segment, a road surface profile over at least a portion of
the road segment, or a predetermined curvature associated with the
road segment at various predetermined locations along predetermined
road model trajectory 5912. In some exemplary embodiments,
processing unit 110 may retrieve a first set of parameter values
representing at least one predetermined signature feature of road
segment 5900.
[0783] Further still, in some exemplary embodiments, the
predetermined signature features may start at a known location
(e.g., an intersection) and, if lane marking segment lengths and
spacing are known and lane marking segments are counted, processing
unit 110 may determine a location about the road from the known
location. In some embodiments, a combination of known lengths for
specific segments (e.g., typically close to the intersection)
together with statistics on regarding consistent segment lengths
and spacing may also be used as a predetermined signature feature.
Further, in some embodiments, a predetermined signature feature may
include a combination of two repetitive features, such as the
combination of lane marking segments and lampposts. In still yet
other embodiments, a predetermined signature feature may include a
combination of GPS data (e.g., an approximate location) and lane
mark segments.
[0784] Process 6000 may also include a step 6008 of determining
whether the local feature determined, for example, in step 6004
matches the at least one predetermined signature feature, retrieved
for example in step 6006. Processing unit 110 may determine whether
there is a match as discussed above with respect to FIGS. 57-59.
When processing unit 110 determines that the local feature matches
a predetermined signature feature (Step 6008: Yes), processing unit
110 may proceed to step 6010. In step 6010, processing unit may
determine current location 5628, 5932 of vehicle 200. Processing
unit 110 may determine current location 5932 as discussed above
with respect to FIGS. 57-59. Returning to step 6008, when
processing unit 110 determines, however, that the local feature
does not match a predetermined signature feature (Step 6008: No),
processing unit 110 may return to step 6006 to retrieve another
predetermined signature feature from database 160.
[0785] Process 6000 may include a step 6012 of determining heading
direction 5980 of vehicle 200 at current location 5628, 5932.
Processing unit 110 may determine heading direction 5980 using one
or more operations discussed above with respect to FIGS. 37-39. For
example, processing unit 110 may determine heading direction 5980
as a gradient of travelled trajectory 5914 at current location 5932
of vehicle 200. Process 6000 may also include a step 6014 of
determining a direction 5990 of predetermine road model trajectory
5912. Processing unit 110 may determine direction 5990 using one or
more operations discussed above with respect to FIGS. 37-43. For
example, processing unit 110 may determine direction 5990 as a
vector oriented tangentially to predetermined road model trajectory
5912 at current location 5932 of vehicle 200. Processing unit 110
may determine the tangential vector as a vector pointing along a
gradient of the mathematical representation of predetermined road
model trajectory 5912 at current location 5932.
[0786] Process 6000 may also include a step 6016 of determining an
autonomous steering action for vehicle 200. Processing unit 110 may
determine a rotational angle .quadrature. between heading direction
5980 and direction 5990 of predetermined road model trajectory
5912. Processing unit 110 may execute the instructions in
navigational module 408 to determine an autonomous steering action
for vehicle 200 that may help ensure that heading direction 5980 of
vehicle 200 is aligned (i.e., parallel) with direction 5990 of
predetermined road model trajectory 5912 at current location 5932
of vehicle 200. Processing unit 110 may also send control signals
to steering system 240 to adjust rotation of the wheels of vehicle
200 to turn vehicle 200 so that heading direction 5980 may be
aligned with direction 5990 of predetermined road model trajectory
5912 at current location 5932.
[0787] Processing unit 110 and/or image acquisition unit 120 may
repeat steps 6002 through 6016 after a predetermined amount of
time. In one exemplary embodiment, the predetermined amount of time
may range between about 0.5 seconds to 1.5 seconds. By repeatedly
determining current location 5932 of vehicle 200 based on road
signatures, heading direction 5980 based on travelled trajectory
5914, direction 5990 of predetermined road model trajectory 3410 at
current location 5932, and the autonomous steering action required
to align heading direction 5980 with direction 5990, processing
unit 110 and/or image acquisition unit 120 may help to navigate
vehicle 200, using road signatures, so that vehicle 200 may travel
along road segment 5912.
[0788] Forward Navigation Based on Rearward Facing Camera
[0789] Consistent with disclosed embodiments, in situations where
adverse lighting conditions inhibit navigation using a forward
facing camera (e.g., driving into bright sun), navigation can be
based on image information obtained from a rearward facing
camera.
[0790] In one embodiment, a system for autonomously navigating a
vehicle may include at least one processor. The at least one
processor may be programmed to receive from a rearward facing
camera, at least one image representing an area at a rear of the
vehicle, analyze the at least one rearward facing image to locate
in the image a representation of at least one landmark, determine
at least one indicator of position of the landmark relative to the
vehicle, determine a forward trajectory for the vehicle based, at
least in part, upon the indicator of position of the landmark
relative to the vehicle, and cause the vehicle to navigate along
the determined forward trajectory.
[0791] In related embodiments, the indicator of position of the
landmark may include a distance between the vehicle and the
landmark and/or a relative angle between the vehicle and the
landmark. The landmark may include a road marking, a lane marking,
a reflector, a pole, a change in line pattern on a road, a road
sign, or any other observable feature associated with a road
segment. The landmark may include a backside of a road sign, for
example. The at least one processor may be further programmed to
determine a lane offset amount of the vehicle within a current lane
of travel based on the indicator of position of the landmark, and
determination of the forward trajectory may be based on the
determined lane offset amount. The at least one processor may be
further programmed to receive from another camera, at least one
image representing another area of the vehicle, and determination
of the forward trajectory may be further based on the at least one
image received from the other camera.
[0792] In some embodiments, the rearward facing camera may be
mounted on an object connected to the vehicle. The object may be a
trailer, a bike carrier, a mounting base, a ski/snowboard carrier,
or a luggage carrier. The rearward camera interface may be a
detachable interface or a wireless interface.
[0793] FIG. 61A is a diagrammatic side view representation of an
exemplary vehicle 6100 consistent with the disclosed embodiments.
Vehicle 6100 may be similar to vehicle 200 of FIG. 2A, except that
vehicle 6100 includes in its body an image capture device 6102
facing in a rearward direction relative to vehicle 6100. System
6104 may be similar to system 100 of FIG. 1 and may include a
processing unit 6106 similar to processing unit 110. As shown in
FIG. 61A, image capture device 6102 may be positioned in the
vicinity of a trunk of vehicle 6100. Image capture device 6102 may
also be located, for example, at one of the following locations: on
or in a side mirror of vehicle 6100; on the roof of vehicle 6100;
on a side of vehicle 6100; mounted on, positioned behind, or
positioned in front of any of the windows/windshield of vehicle
6100; on or in a rear bumper; mounted in or near light figures on
the back of vehicle 6100; or any other locations where image
capture device 6102 may capture an image of an area rear of vehicle
6100. In some embodiments, as discussed above, image capture device
6102 may be mounted behind a glare shield that is flush with the
rear windshield of vehicle 6100. Such a shield may minimize the
impact of reflections from inside vehicle 6100 on image capture
device 6102.
[0794] FIG. 61A shows one image capture device 6102 facing a
rearward direction of vehicle 6100. However, other embodiments may
include a plurality of image capture devices located at different
positions and facing a rearward direction of vehicle 6100. For
example, a first image capture device may be located in the trunk
of vehicle 6100 facing a rearward, slightly downward direction of
the vehicle 6100, and a second image capture device may be mounted
on the roof of vehicle 6100 facing a rearward, slightly upward
direction of vehicle 6100. In another example, a first image
capture device may be mounted on a left side mirror of vehicle
6100, and a second image capture device may be mounted on a right
side mirror of vehicle 6100. Both the first and second image
capture devices may face a rearward direction of vehicle 6100.
[0795] In some embodiments, the relative positioning of the image
capture devices may be selected such that the fields of view of the
image capture devices overlap fully, partially, or not at all.
Further the image capture devices may have the same or different
fields of view and the same or different focal lengths.
[0796] FIG. 61A shows an image capture device 6102 facing in a
rearward direction relative to vehicle 6100. However, a skilled
artesian would recognize that vehicle 6100 may further include any
number of image capture devices facing in various directions and
that processing unit 6106 may be further programmed to operate
these additional image capture devices. For example, vehicle 6100
may further include image capture devices 122 and 124 of FIG. 2A,
and processing unit 6106 may be programmed to perform the
programmed functions of processing unit 110 of system 100. A
skilled artesian would further recognize that having a plurality of
image capture devices, one facing a rearward direction of vehicle
6100 and another facing a forward direction of vehicle 6100, may be
beneficial in situations where adverse lighting conditions may
inhibit navigation using one of the image capture devices (e.g.,
driving into bright sun). Since adverse lighting conditions rarely
affect both image capture devices thereof, system 6104 may be
configured to navigate based on images received from an image
capture device affected less adversely by the lighting condition in
these situations.
[0797] In some embodiments, a forward trajectory for the vehicle
may be determined based on an image received from a rearward facing
camera together with or independent from an image received from a
forward facing camera. In some embodiments, a forward trajectory
may be determined by, for example, averaging two determined
trajectories, one based on images received from a rearward facing
camera, and another based on images received from a forward facing
camera.
[0798] In some embodiments, images from a forward facing camera and
a rearward facing camera may be analyzed to determine which is
currently providing more useful images. Based on this
determination, images from the forward facing camera or the
rearward facing camera may selectively be used in navigating the
vehicle. For example, in a situation where vehicle 6100 may face a
bright light source (e.g., the sun) that causes the forward facing
camera to capture an image lacking sufficient detail on which
navigational responses may accurately be determined, images
collected from a rearward facing camera, not affected by the same
light source may be used in navigating the vehicle. This
determination and selection of images from the available image
streams may be made on the fly.
[0799] In some embodiments, navigation may be based on images
received from a rearward facing camera because one or more objects
(e.g., a large truck or other vehicle) is blocking a portion of a
field of view of a forward facing camera. In other situations,
navigation may be based on a images collected from a rearward
facing camera as a supplement to images collected from a forward
facing camera. For example, in some embodiments a vehicle may
locate a recognized landmark in a field of view of its forward
facing camera. From a time when that recognized landmark first
comes into view of the forward facing camera until a time when the
vehicle has passed the recognized landmark (or the landmark has
otherwise passed out of the field of view of the forward facing
camera), navigation can proceed based on images captured of the
recognized landmark (e.g., based on any of the techniques described
above). Navigation based on the recognized landmark, however, need
not end when the vehicle passes the landmark. Rather, a rearward
facing camera can capture images of the same recognized landmark as
the vehicle travels away from the landmark. These images can be
used, as described above, to determine a location of the vehicle
relative to a target trajectory for a particular road segment, and
images of the backside of the recognized landmark may be usable as
long as the backside of the landmark is visible or appears in the
images captured by the rearward facing camera. Using such a
technique may extend an amount of time that a vehicle can navigate
with the benefit of a recognized landmark and delay a time when the
vehicle must transition to dead reckoning or another navigational
technique not anchored by a known location of a recognized
landmark. As a result, navigational error may be even further
reduced such that the vehicle even more closely follows a target
trajectory.
[0800] In some embodiments, an object(s) may be present at the back
of vehicle 6100, and this object may be in the field of vision of
image capture device 6102, interfering with image capture device's
6102 ability to accurately capture images representing an area at a
rear of vehicle 6100. The object may be, for example, a trailer, a
mounting base, a bike carrier, a ski/snowboard carrier, or luggage
carrier. In these embodiments, image capture device 6102 may be
mounted on the object and arranged to capture images representing
an area at a rear of the object. FIG. 61B illustrates an exemplary
vehicle with such an object.
[0801] FIG. 61B is a diagrammatic side view representation of an
exemplary vehicle 6150 consistent with the disclosed embodiments.
Vehicle 6150 is similar to vehicle 6100 except that vehicle 6150 is
towing a trailer 6108 and image capture device 6102 is mounted on
trailer 6108. As shown in FIG. 61B, image capture device 6102 is
facing a rearward direction of trailer 6108 and positioned to
capture images representing an area at a rear of trailer 6108. As
discussed above, the presence of trailer 6108 may interfere with
any image capture devices that may be mounted on the body of
vehicle 6150 and face a rearward direction of vehicle 6108.
[0802] In some embodiments, image capture device 6102 of FIG. 61B
may have been previously mounted in or on the body of vehicle 6150
(similar to image capture device 6102 of FIG. 61A) and is now
repositioned on trailer 6108. In these embodiments, image capture
device 6102 may be electrically connected to system 6104 via a
detachable electrical interface 6110. A "detachable electrical
interface" may be broadly defined as a set of connectors that can
be connected and disconnected by a driver or a passenger (or any
other person, who may not be a skilled artesian). Detachable
electrical interface 6110 allows a user who may not be a skilled
artesian, such as the driver or passengers of vehicle 6150, to
remount image capture device 6102, for example, from trailer 6108
to vehicle 6150. This capability may be especially useful in
situations where vehicle 6150 frequently switches between operating
with and without trailer 6108. In other embodiments, rearward
facing camera 6102 may be configured to communicate wirelessly with
processing unit 6106.
[0803] FIG. 62 is a diagrammatic top view representation of an
exemplary vehicle autonomously navigating on a road consistent with
disclosed embodiments. FIG. 62 shows vehicle 6100 of FIG. 61A
including image capture device 6102 (with its line of sight 6102A)
and system 6104 for autonomously navigating vehicle 6100. FIG. 62
also shows several potential, recognized landmarks, including a
road roughness profile 6208 associated with a particular road
segment, a change in lane markings 6210, reflectors 6202A-C, a road
sign 6204 facing away from vehicle 6100, a road sign 6206 facing
towards vehicle 6100, and a pole 6214.
[0804] FIG. 62 also shows indicators of position of a landmark
relative to vehicle 6100. The indicators of position in FIG. 62
include a distance 6212A and/or a relative angle 6212B between a
landmark (e.g., reflector 6202A) and vehicle 6100. An "indicator of
position" may refer to any information that relates to a position.
Thus, an indicator of position of a landmark may include any
information related to the position of the landmark. In the example
of FIG. 62, indicators of position of a landmark are determined
relative to vehicle 6100.
[0805] As shown in FIG. 62, distance 6212A is the distance between
image capture device 6102 and the landmark, and angle 6212B is the
angle between line of sight 6102A of image capture device 6102 and
an imaginary line from image capture device 6102 to the landmark.
However, in some embodiments, distance 6212A may be the distance
between a reference point in the vicinity of vehicle 6100 and the
landmark, and angle 6212B may be the angle between a reference line
through the reference point and an imaginary line from the
reference point to the landmark. The reference point may be, for
example, the center of vehicle 6100, and the reference line may be,
for example, a line through the center of vehicle 6100.
[0806] In some embodiments, one or more landmarks and/or one or
more indicators of position may be used in autonomous navigation of
vehicle 6100. For example, the indicators of position may be used
to determine a current location of a vehicle relative to a target
trajectory stored in sparse map 800, for example. Any of the
techniques discussed above with respect to landmark recognition and
use in determining one or more navigational responses for a vehicle
may be employed based on images received from a rearward facing
camera.
[0807] FIG. 63 is a flowchart showing an exemplary process for
using one or more landmarks and one or more indicators of position
for autonomously navigating a vehicle. Process 6300 may use at
least one image from a rearward facing camera and analyze the at
least one image to navigate a vehicle along a forward
trajectory.
[0808] At step 6302, processing unit 6106 may receive from a
rearward facing camera, at least one image representing an area at
a rear of vehicle 6100. At an optional step, processing unit 6106
may receive from another camera, at least one image representing
another area of vehicle 6100. In some embodiments, processing unit
6106 may receive the images via one or more camera interfaces. For
example, processing unit 6106 may receive at least one image
representing an area at a rear of the vehicle via a rearward camera
interface and receive one image representing an area at a front of
the vehicle via a forward camera interface. In some embodiments, as
discussed above, the one or more camera interfaces may include a
detachable interface or a wireless interface.
[0809] At step 6304, processing unit 6106 may analyze the at least
one rearward facing image to locate in the image a representation
of at least one landmark. As discussed above in reference to FIG.
62, a landmark may include, for example, a road profile 6208, a
change in lane markings 6210, reflectors 6202A-C, a road sign 6204
facing away from vehicle 6100, a road sign 6206 facing towards
vehicle 6100, and a pole 621. Alternatively, or additionally, a
landmark may include, for example, a traffic sign, an arrow
marking, a lane marking, a traffic light, a stop line, a
directional sign, a landmark beacon, a lamppost, a directional
sign, a speed limit sign, a road marking, a business sign, a
distance marker, or a change in spacing of lines on the road.
[0810] In some embodiments, before or after a landmark is located
in the received image, processing unit 6106 may retrieve
information relating to recognized landmarks in the vicinity of the
autonomous vehicle. The information relating to landmarks may
include, for example, information relating to size and/or shape of
a landmark. The information relating to landmarks may be retrieved
from, for example, a database, which may be located in system 6104
or located external to vehicle 6100 (connected to system 6104 via a
communication system such as a cellular network or other wireless
platform).
[0811] At step 6306, processing unit 6106 may determine at least
one indicator of position of the landmark relative to the vehicle.
For example, an indicator of position may include a distance
between a landmark and the vehicle and/or a relative angle between
a landmark and the vehicle. As discussed above in reference to FIG.
62, the distance may be, for example, distance 6212A, which is the
distance between image capture device 6102 and the landmark, and
the angle may be angle 6212B, which is the angle between line of
sight 6102A of image capture device 6102 and an imaginary line from
image capture device 6102 to the landmark.
[0812] In some embodiments, processing unit 6106 may determine at
least one indicator of position of the landmark relative to the
vehicle based on the representation of the located landmarks in the
received image. For example, the size and shape of the
representation of the located landmarks in the received image may
be used to estimate distance 6212A from vehicle 6100 (e.g., by
monitoring a change in scale of the object over multiple image
frames). In another example, coordinates of the pixels occupied by
the representation of the located landmarks in the received image
may be used to estimate angle 6212B. In embodiments where
information relating to landmarks is retrieved by processing unit
6106, the information may be used to model and compare with the
representation of the located landmarks in the received image. In
these embodiments, the information may improve the accuracy of the
determined indicators of position.
[0813] At an optional step 6308, processing unit 6106 may determine
a lane offset amount of the vehicle within a current lane of travel
(or even make a determination of a current lane of travel) based on
the indicator of position of the landmark relative to the vehicle.
For example, such an offset determination or lane determination may
be determined by knowing a position of a recognized landmark along
with a relative positioning of the recognized landmark with respect
to lanes of a road segment. Thus, once a distance and direction are
determined relative to a recognized landmark, the current lane of
travel and/or amount of lane offset within a particular lane of
travel may be calculated. A lane offset of a vehicle may refer to a
perpendicular distance from a lane indicator to a reference point.
In some embodiments, the reference point may correspond to an edge
of a vehicle or a point along a centerline of a vehicle. In other
embodiments, the reference point may correspond to a mid-point of a
lane or a road. A lane indicator may include, for example, a lane
marking, a road edge, reflectors for improving visibility of a
lane, or any other object that is on or near the boundaries of a
lane. In the above example, the lane offset may be the
perpendicular distance from road edge 6208 to vehicle 6100.
[0814] At step 6310, processing unit 6106 may determine a forward
trajectory for the vehicle based, at least in part, upon the
indicator of position of the landmark relative to the vehicle. In
embodiments where the optional step 6308 is performed, processing
unit 6106 may determine the forward trajectory for the vehicle
further based on the determined lane offset amount. In embodiments
where processing unit 6106 received from another camera, at least
one image representing another area of vehicle 6100, processing
unit 6106 may determine the forward trajectory for the vehicle
further based on the at least one image received from the another
camera. Such a trajectory determination may be based on any of the
techniques described above (e.g., navigation based on recognized
landmarks, tail alignment, etc.)
[0815] At step 6312, processing unit 6106 may cause vehicle 6100 to
navigate along the determined forward trajectory. In some
embodiments, processing unit 6106 may cause one or more
navigational responses in vehicle 6100 to navigate along the
determined forward trajectory. Navigational responses may include,
for example, a turn, a lane shift, a change in acceleration, and
the like. Additionally, multiple navigational responses may occur
simultaneously, in sequence, or any combination thereof to navigate
along the determined forward trajectory. For instance, processing
unit 6106 may cause vehicle 6100 to shift one lane over and then
accelerate by, for example, sequentially transmitting control
signals to steering system 240 and throttling system 220.
Alternatively, processing unit 110 may cause vehicle 6100 to brake
while at the same time shifting lanes by, for example,
simultaneously transmitting control signals to braking system 230
and steering system 240.
[0816] Navigation Based on Free Space Determination
[0817] Consistent with disclosed embodiments, the system can
recognize parked vehicles, road edges, barriers, pedestrians, and
other objects to determine free space boundaries within which the
vehicle can travel.
[0818] In some situations, free space boundaries may be used to
navigate a vehicle. These situations may include, for example, when
lane markings are not visible because lanes do not exist and/or
because obstacles are covering the lane marks (e.g., parked cars
and snow). Alternatively, free space boundaries may be used to
navigate a vehicle in addition to the lane-mark-based navigation
method to increase the robustness of the system.
[0819] FIG. 64 is a diagrammatic perspective view of an environment
6400 captured by a forward facing image capture device on an
exemplary vehicle consistent with disclosed embodiments. The
exemplary vehicle may be, for example, vehicle 200 described above
in reference to FIGS. 2A-2F and may include a processing unit, such
as processing unit 110 of vehicle 200. The forward facing image
capture device may be, for example, image capture device 122, image
capture device 124, or image capture device 126 of vehicle 200.
[0820] FIG. 64 shows a non-road area 6410 with a road edge 6410A, a
sidewalk 6412 with a curb 6412A, and a road horizon 6414. A road
barrier 6418 may be present in the vicinity of road edge 6410A, and
a car 6416 may be parked in the vicinity of curb 6412A. FIG. 64
also shows a first free space boundary 6404, a second free space
boundary 6406, and a forward free space boundary 6408. Forward free
space boundary 6408 may extend between first free space boundary
6404 and second free space boundary 6406. A free space region 6402
forward of the vehicle (not shown in the figure) may be a region
bound by these three boundaries and may represent a physically
drivable region within environment 6400. First and second free
space boundaries 6404, 6406 may each correspond to, for example, a
road edge, a road barrier, a parked car, a curb, a lane dividing
structure, a tunnel wall, and/or a bridge structure, or a
combination thereof. Forward free space 6408 may correspond to, for
example, an end of the road, a road horizon, a road barrier, a
vehicle, or a combination thereof.
[0821] In the example of FIG. 64, first and second free space
boundaries 6404, 6406 may each correspond to a plurality of
objects. For example, first free space boundary 6404 may correspond
to a portion of road edge 6410A and road barrier 6418, and second
free space boundary 6406 may correspond to a portion of curb 6412A
and parked car 6414. However, in some embodiments, each free space
boundary may correspond to a single object. Similarly, forward free
space 6408 may correspond to one or more objects. For example, in
FIG. 64, forward free space 6408 corresponds to road horizon
6414.
[0822] In some embodiments, one or more obstacles may exist in free
space region 6402 bound by the three free space boundaries 6404,
6406, and 6408. In these embodiments, the obstacles may be excluded
from free space region 6402. In FIG. 64, for example, pedestrian
6420 is standing inside free space region 6402 bound by the three
free space boundaries 6404, 6406, and 6408. Therefore, pedestrian
6420 may be excluded from free space region 6402. Alternatively,
regions surrounding the obstacles may be excluded from free space
region 6402. For example, a region 6422 surrounding pedestrian
6420, instead of the region occupied by pedestrian 6420, may be
excluded from free space region 6402. Obstacles may include, for
example, a pedestrian, another vehicle, and debris.
[0823] A size of a region (e.g., region 6422) surrounding an
obstacle (e.g., pedestrian 6420) may determine the minimum distance
that may exist between the vehicle and the obstacle during
navigation. In some embodiments, the size of the region may be
substantially the same as the size of the obstacle. In other
embodiments, the size of the region may be determined based on the
type of the obstacle. For example, region 6422 surrounding
pedestrian 6420 may be relatively large for safety reasons, while
another region surrounding debris may be relatively small. In some
embodiments, the size of the region may be determined based on the
speed at which the obstacle is moving, frame rate of the image
capture device, speed of the vehicle, or a combination thereof. In
some embodiments, a shape of a region surrounding an obstacle may
be a circle, a triangle, a rectangle, or a polygon.
[0824] In FIG. 64, first free space boundary 6404 corresponds to a
portion of road edge 6410 and road barrier 6148, and second free
space boundary 6406 corresponds to a portion of curb 6412A and
parked car 6416. However, in other embodiments, first and second
free space boundaries 6404, 6406 may correspond to road edge 6410A
and curb 6412A, respectively, and road barrier 6148 and parked car
6416 may be considered as obstacles.
[0825] In some embodiments, regions between obstacles may be
excluded from free space region 6402. For example, if a width of a
region between two obstacles is less than the width of the vehicle,
the region may be excluded from free space region 6402.
[0826] FIG. 65 is an exemplary image received from a forward facing
image capture device of a vehicle consistent with disclosed
embodiments. FIG. 65 shows a first free space boundary 6504 and a
second free space boundary 6506. Both free space boundaries
correspond to curbs and parked cars (e.g., parked car 6516) on each
side of the road. FIG. 65 also shows a free space region 6502,
which may be defined by first free space boundary 6504, a second
free space boundary 6506, and a forward free space boundary (not
shown). Additionally, FIG. 65 shows a moving car 6520, which may be
considered as an obstacle. Therefore, moving car 6520 may be
excluded from free space region 6502.
[0827] FIG. 66 is a flowchart showing exemplary process 6600 for
navigating vehicle 200 based on free space region 6402 in which
vehicle 200 can travel consistent with disclosed embodiments.
Process 6300 may use a plurality of images from a forward facing
image capture device, analyze at least one image of the plurality
of images to identify free space boundaries and define a free space
region bound by the identified free space boundaries. Furthermore,
process 6300 may navigate a vehicle based on the defined free space
region.
[0828] At step 6602, processing unit 110 may receive from image
capture device 122, a plurality of images associated with
environment 6400 of vehicle 200. As discussed above, FIG. 65 is an
example of an image that may be received from image capture device
122. In some embodiments, images may be captured at different times
by image capture device 122 (e.g., images may be captured apart by
less than a second, 1 second, 2 second, etc.). In some embodiments,
vehicle 200 may include a plurality of image capture devices (e.g.,
image capture devices 122 and 124 of vehicle 200), and processing
unit 110 may receive from each image capture device, a plurality of
images associated with environment 6400 of vehicle 200. The
plurality of images received from each image capture device may be
images captured at different times by each image capture
device.
[0829] At step 6604, processing unit 110 may analyze at least one
of the plurality of images received from, for example, image
capture device 122. In embodiments where a single plurality of
images is generated based on images received from a plurality of
image capture devices, processing unit 110 may analyze at least one
image of the single plurality of images. Alternatively, each image
received from each image capture device may be analyzed
independently.
[0830] Additionally, processing unit 110 may identify a first free
space boundary 6404 on a driver side of vehicle 200 and extending
forward of vehicle 200, a second free space boundary 6406 on a
passenger side of vehicle 200 and extending forward of vehicle 200,
and a forward free space boundary 6408 forward of vehicle 200 and
extending between first free space boundary 6404 and second free
space boundary 6406. Additionally, processing unit 110 may further
identify a free space region 6402 forward of the vehicle as the
region bound by first free space boundary 6404, the second free
space boundary 6406, and forward free space boundary 6408. As
discussed above, first and second free space boundaries 6404, 6406
may each correspond to, for example, a road edge, a road barrier, a
parked car, a curb, a lane dividing structure, a tunnel wall,
and/or a bridge structure, or a combination thereof. Furthermore,
as discussed above, forward free space 6408 may correspond to, for
example, an end of the road, a road horizon, a road barrier, a
vehicle, or a combination thereof.
[0831] At an optional step, processing unit 110 may identify, based
on the analysis at step 6604, an obstacle (e.g., pedestrian 6420)
forward of vehicle 200 and exclude the identified obstacle from
free space region 6402 forward of vehicle 200. Alternatively,
processing unit 110 may identify, based on analysis of the at least
one of the plurality of images at step 6640, an obstacle (e.g.,
pedestrian 6420) forward of the vehicle and exclude a region (e.g.,
region 6422) surrounding the identified obstacle from free space
region 6402 forward of the vehicle.
[0832] In some embodiments, as discussed above, the size of the
region surrounding the identified obstacle may be substantially the
same as the size of the obstacle, or alternatively, the size of the
region surrounding the obstacle may be determined based on the type
of obstacle. In some embodiments, as discussed above, the size of
the region may be determined based on the speed at which the
obstacle is moving, a frame rate of image capture device 122, speed
of vehicle 200, or a combination thereof.
[0833] At another optional step, processing unit 110 may exclude,
from free space region 6402, regions between the identified
obstacles and/or regions between identified obstacles and free
space boundaries 6404, 6406. In some embodiments, as discussed
above, processing unit 110 may determine whether to exclude the
regions between the identified obstacles based on a distance
between the identified obstacles. Furthermore, processing unit 110
may determine whether to exclude the regions between identified
obstacles and free space boundaries 6404, 6406 based on the
distance between identified obstacles and free space boundaries
6404, 6406.
[0834] At step 6606, processing unit 110 may determine a
navigational path for vehicle 200 through free space region 6402.
In some embodiments, the navigational path may be a path through
the center of free space region 6402. In other embodiments, the
navigational path may be a path that is a predetermined distance
away from one of first and second free space boundaries 6404, 6406.
The predetermined distance may be a fixed distance, or
alternatively, the predetermined distance may be determined based
on, for example, one or more of the following: speed of vehicle
200, width of free space region 6402, and number of obstacles
within free space region 6402. Alternatively, the navigational path
may be, for example, a path that uses the minimum number of
navigational responses or the shorted path.
[0835] At step 6608, processing unit 110 may cause vehicle 200 to
travel on at least a portion of the determined navigational path
within the free space region 6402 forward of vehicle 200. In some
embodiments, processing unit 110 may cause one or more navigational
responses in vehicle 200 to navigate along the determined
navigational path. Navigational responses may include, for example,
a turn, a lane shift, a change in acceleration, and the like.
Additionally, multiple navigational responses may occur
simultaneously, in sequence, or any combination thereof to navigate
along the determined forward trajectory. For instance, processing
unit 110 may cause vehicle 200 to move laterally and then
accelerate by, for example, sequentially transmitting control
signals to steering system 240 and throttling system 220.
Alternatively, processing unit 110 may cause vehicle 200 to brake
while at the same time moving laterally, for example,
simultaneously transmitting control signals to braking system 230
and steering system 240. Further, for example, the free space
boundary may serve as a localization aid in the same way that lane
marks are being used. Once the free space boundary is encoded, it
may describe a 3D curve in space. At the localization stage, the
projection of that 3D curve to the image may provide a localization
cue, since it may collide with the free space detection at that
location.
[0836] Navigating in Snow
[0837] Consistent with disclosed embodiments, the system may
determine the edges of a road in poor weather conditions, such as
when a road is covered in snow. For example, the system may take
into account changes in light, the curve of a tree line, and tire
tracks to determine probable locations of the edges of the
road.
[0838] FIG. 67 is a diagrammatic top view representation of an
exemplary vehicle navigating on a road with snow covering at least
some lane markings and road edges consistent with disclosed
embodiments. The exemplary vehicle may be, for example, vehicle 200
described above in reference to FIGS. 2A-2F and may include a
processing unit, such as processing unit 110 of vehicle 200. The
forward facing image capture device may be, for example, image
capture device 122, image capture device 124, or image capture
device 126 of vehicle 200.
[0839] In FIG. 67, the road may include a driver side lane mark
6702 and a passenger side lane mark 6704. FIG. 67 also shows a
non-road area 6710 and a road edge 6706. In one example, non-road
area 6710 may be a non-paved area, a sidewalk, or a beginning of a
hill. In another example, non-road area 6710 may be an area without
a platform, such as an area with a sharp vertical drop (i.e.,
cliff).
[0840] FIG. 67 also shows an area covered by snow 6708.
Specifically, area 6708 covers a portion of road edge 6706 and
portions of lane marks 6702, 6704. Thus, road edge 6706 and/or one
or more of lane marks 6702, 6704 may not be readily apparent
through analysis of images captured during navigation of vehicle
200. In such situations, vehicle 200 may navigate based on analysis
of captured images by determining probable locations for road edges
bounding the portion of the road that is covered with snow.
[0841] In some embodiments, the determination of the probable
locations for road edges may be based on tire tracks 6712 over an
area covered by snow 6708. For example, the presence of tire tracks
6712 may indicate that the portion of an area covered by snow 6708
with tire tracks 6712 is within the bounds of road edges. In some
embodiments, the processing unit of the vehicle may consider the
path of the tire tracks as a viable navigational path and may cause
the vehicle to follow the tire tracks subject to consideration of
other criteria (e.g., whether the tracks remain within an area
determined as likely corresponding to the road or, more
specifically, a lane of travel for the vehicle).
[0842] In other embodiments, the determination of the probable
locations for road edges may be based on a change of light across a
surface of area covered by snow 6708. The source of the light may
include, for example, headlights of vehicle 200, light from other
vehicles, street lights, or the sun. The change of light across the
surface of area covered by snow 6708 may occur for various reasons.
In one example, surface roughness of non-road area 6710 and surface
roughness of the road may be different;
[0843] non-road area 6710 may be a gravel area, while the road may
be a paved area. In another example, non-road area 6710 and the
road may not be level. Non-road area 6710 may be a sidewalk, which
is typically raised above the road; alternatively, non-road area
6710 may be a hill or a cliff. Each of these may alter the surface
of a covering of snow and may be recognized based on certain
variations in the surface of the snow covering (e.g., changes in
height, changes in texture, etc.) which may be accentuated by
shadows cast across the snow surface.
[0844] In other embodiments, the determination of the probable
locations for road edges may be based on a plurality of trees
(e.g., forming a tree line) along an edge of the road. For example,
in FIG. 67, trees 6714 may be present along the road edge and
visible even when snow covers road edge 6706. In situations where
trees 6714 are present close to road edge 6714, the location of
trees 6714 may be used as a probable location of a road edge.
However, in some situations, trees 6714 may be present some
distance away from a road edge. Therefore, in some embodiments, the
probable location of a road edge may be determined as a location
that is a distance away from the location of trees 6714. The
distance may be a fixed value, or alternatively, the distance may
be dynamically determined based on, for example, a last visible
road edge.
[0845] In other embodiments, the determination of the probable
locations for road edges may be based on an observed changes in
curvature at a surface of the snow. The change in curvature at a
surface of the snow may occur for various reasons. For example, a
change in curvature at a surface of snow may occur when non-road
area 6710 and the road are not level. In situations where non-road
area 6710 is, for example, a sidewalk typically raised above the
road, the snow may pile up near road edge 6706 thereby changing the
curvature of snow near road edge 6706. In other situations, the
non-road area may be a beginning of a hill, and the curvature at
the surface of the snow may follow the curvature of the hill
beginning at road edge 6706. In these embodiments, the location
where the curvature begins to change may be determined as a
probable location of a road edge.
[0846] FIG. 68 is a flowchart showing an exemplary process 6800 for
navigating vehicle 200 on a road with snow covering at least some
lane markings and road edges consistent with disclosed embodiments.
Process 6800 may use the probable locations for road edges, as
described above, to navigate vehicle 200.
[0847] At 6802, processing unit 110 may receive from an image
capture device, at least one environmental image forward of the
vehicle, including areas where snow covers at least some lane
markings (e.g., lane marks 6702, 6704) and road edges (e.g., road
edge 6706).
[0848] At step 6804, processing unit 110 may identify, based on an
analysis of the at least one image, at least a portion of the road
that is covered with snow and probable locations for road edges
bounding the at least a portion of the road that is covered with
snow. As discussed above, the analysis of the at least one image
may include identifying at least one tire track in the snow, a
change of light across a surface of the snow, and/or a trees along
an edge of the road. Further, as discussed above, the analysis of
the at least one image may include recognizing a change in
curvature at a surface of the snow, where the recognized change in
curvature is determined to correspond to a probable location of a
road edge.
[0849] In some embodiments, the analysis of the at least one image
may include a pixel analysis of the at least one image in which at
least a first pixel is compared to at least a second pixel in order
to determine a feature associated with a surface of the snow
covering at least some lane markings and road edges. For example,
each pixel in the image may be compared with every adjacent pixel.
In some embodiments, a color of the first pixel may be compared to
a color of at least the second pixel.
[0850] Alternatively, or additionally, an intensity of a color
component of the first pixel may be compared to an intensity of the
color component of at least the second pixel. In other embodiments,
the following properties of a pixel may be compared.
[0851] In some embodiments, the pixel analysis may identify
features such as an edge of tire track 6712 or road edge 6706. The
analysis for identifying such features may include identifying a
set of pixels where a rate in which a pixel property changes
exceeds a threshold rate. The pixel property may include, for
example, color of a pixel and/or intensity of a color component of
a pixel.
[0852] At step 6806, processing unit 110 may cause the vehicle to
navigate a navigational path that includes the identified portion
of the road and falls within the determined probable locations for
the road edges.
[0853] In embodiments where the probable location for road edges
are determined based on the identified tire tracks 6712, processing
unit 110 may cause vehicle 200 to navigate by at least partially
following the identified tire tracks 6712 in the snow. In
embodiments where the probable location for road edges (e.g., road
edges 6702, 6704) are determined based on a change of light across
a surface of area covered by snow 6708, a plurality of trees (e.g.,
forming a tree line) along an edge of the road, and/or a change in
curvature at a surface of the snow, processing unit 110 may cause
vehicle 200 to navigate between the determined edges of the
road.
[0854] Furthermore, in embodiments where edges of the road are
determined by analyzing pixels of the image received at step 6802,
processing unit 110 may cause vehicle 200 to navigate between the
determined edges of the road. In embodiments where an edge of a
tire track is determined by analyzing pixels of the image received
at step 6802, processing unit 110 may cause vehicle 200 to navigate
by at least partially following tire tracks in the snow.
[0855] In some embodiments, processing unit 110 may cause one or
more navigational responses in vehicle 200 to navigate along the
determined navigational path. Navigational responses may include,
for example, a turn, a lane shift, a change in acceleration, and
the like. Additionally, multiple navigational responses may occur
simultaneously, in sequence, or any combination thereof to navigate
along the determined forward trajectory. For instance, processing
unit 110 may cause vehicle 200 to move laterally and then
accelerate by, for example, sequentially transmitting control
signals to steering system 240 and throttling system 220.
Alternatively, processing unit 110 may cause vehicle 200 to brake
while at the same time moving laterally, for example,
simultaneously transmitting control signals to braking system 230
and steering system 240.
[0856] Additional techniques may also be employed by processing
unit 110 for navigating a vehicle on a road at least partially
covered with snow. For example, in some embodiments, one or more
neural networks may be employed to aid in determination of a
proposed path of travel along a road covered in snow. This
technique may be referred to as holistic path prediction (HPP).
Such a neural network may be trained, for example, by being
supplied with images as a user drives along a road. To train the
neural network in navigation of a snow covered road, various
testing situations involving snow covered roads may be used. Using
images (perhaps thousands of training images, millions of images,
or more) of roads covered with snow captured as a driver navigates
a vehicle along snow covered roads, the neural network will learn
to develop a proposed navigational path along the snow. The process
may involve setting up the neural network to periodically or
continuously generate a proposed navigational path based on
observed features of the snow covered road (including, for example,
aspects of the surface of the road, edges of the road, sides of the
road, barriers present, objects adjacent to the road, cars on the
road, etc.) and test the proposed navigational path against actual
behavior of the driver. Where the proposed navigational path
diverges from the actual path the driver follows, the neural
network will analyze the available images and make adjustments to
its processing algorithm in order to provide a different response
in a similar situation in the future (e.g., to provide a proposed
navigational path that more closely matches the behavior of the
driver). Once trained, the neural network may provide a proposed
navigational path over a road covered with snow. Navigation through
snow may be based solely on the output of a single trained neural
network
[0857] In some embodiments, however, other techniques may be used
to navigate the vehicle through snow. In some embodiments, the free
space determination technique described in another section of this
disclosure may be used to define a path forward of the vehicle
through an area perceived as free space. For example, based on a
captured image or image stream, processing unit 110 may analyze at
least one of the plurality of images to identify a first free space
boundary on a driver side of the vehicle and extending forward of
the vehicle. A second free space boundary may be identified on a
passenger side of the vehicle and extending forward of the vehicle.
A forward free space boundary may be identified forward of the
vehicle and extending between the first free space boundary and the
second free space boundary. Of course, these boundaries need not be
straight lines, but instead, can be represented by a complex series
of curves or line segments that delineate sometimes highly
irregular boundary conditions (especially on the sides of the
vehicle). Together, first free space boundary, the second free
space boundary, and the forward free space boundary define a free
space region forward of the vehicle. Processing unit 110 may then
determine a proposed navigational path for the vehicle through the
free space region. Navigation of the vehicle through snow may be
based on the free space determination technique alone. It should be
noted that the free space determination technique may be
implemented using one or more neural networks. In some embodiments,
the neural network that implements the free space determination
technique may be different from the neural network that implements
the HPP technique.
[0858] In some embodiments, navigation through snow may be based on
one or more techniques used in combination. For example, any of the
disclosed navigational systems may be used together to navigate a
vehicle in snow. In some embodiments, the free space determination
technique may be combined with the HPP technique. That is, a
plurality of captured images may be supplied to a neural network
implementing the free space technique in order to obtain a first
proposed navigational path for the vehicle. The plurality of
captured images may also be supplied to the neural network
implementing the HPP technique to obtain a second proposed
navigational path for the vehicle. If the processing unit
determines that the first proposed navigational path agrees with
the second proposed navigational path, then the processing unit may
cause the vehicle to travel on at least a portion of one of the
proposed navigational paths (or an aggregate of the proposed
navigational paths). In this context, agreement does not
necessarily require an exact match of the proposed navigational
paths. Rather, agreement may be determined if the proposed
navigational paths have greater than a predetermined degree of
correlation (which may be determined using any suitable compare
function).
[0859] If the first proposed navigational path does not agree with
the second proposed navigational path, then a prompt may be
provided to the user to take over control of at least some aspect
of the vehicle. Alternatively, additional information may be
considered in order to determine an appropriate navigational path
for the vehicle. For example, where there is disagreement in the
proposed navigational paths from the free space and HPP techniques,
processing unit 110 may look to a target trajectory from sparse
data map 800 (along with an ego motion estimation or landmark based
determination of a current position relative to the target
trajectory) to determine a direction of travel for the vehicle.
Outputs from other modules operating on processing unit 110 may
also be consulted. For example, a vehicle detection module may
provide an indication of the presence of other vehicles in the
environment of the host vehicle. Such vehicles may be used to aid
in path prediction for the host vehicle (e.g., by following a lead
vehicle, avoiding a parked vehicle, etc.). A hazard detection
module may be consulted to determine the presence of any edges in
or along the roadway having a height exceeding a threshold. A curve
detection module may be consulted to locate a curve forward of the
vehicle and to propose a path through the curve. Any other suitable
detection/analysis module operating on processing unit 110 may also
be consulted for input that may aid in establishing a valid path
forward for the host vehicle.
[0860] The description and the figures above show a road that is
covered by snow; however, in some embodiments, a road may be
covered with object(s) other than snow. For example, the road may
be covered with sand or gravel instead of snow, and the disclosed
embodiments may similarly be applied to roads covered with these
objects.
[0861] Autonomous Vehicle Speed Calibration
[0862] In some situations, vehicle navigation can be based on dead
reckoning (for example, at least for short segments) where the
vehicle determines its current location based on its last known
position, its speed history, and its motion history. Dead
reckoning, however, may introduce accumulating errors because every
new position determination may rely upon measurements of
translational and rotational velocities, which may introduce a
certain level of error. Similarly, each new position determination
may rely upon a previously determined coordinate, which, in turn,
may have been based on measurements including their own
inaccuracies. Such inaccuracies and errors may be imparted into the
dead reckoned position determinations through various sources, such
as the outputs of vehicle speed sensors for example. Even small
inaccuracies in speed sensing may accumulate over time. For
example, in some cases, small errors in speed sensing (e.g., on the
order of 1 km/hr or even less) may result position determination
errors on the order of 1 meter, 5 meters, or more over a kilometer.
Such errors, however, may be reduced or eliminated through
calibration of vehicle speed sensors. According to the disclosed
embodiments, such calibration may be performed by an autonomous
vehicle based on known landmark positions or based on a reference
distance along a road segment being traversed by the vehicle.
[0863] FIG. 69 is a diagrammatic top view representation of an
exemplary vehicle including a system for calibrating a speed of the
vehicle consistent with disclosed embodiments. The exemplary
vehicle may be, for example, vehicle 200 described above in
reference to FIGS. 2A-2F and may include a processing unit, such as
processing unit 110 of vehicle 200. The forward facing image
capture device may include, for example, image capture device 122,
image capture device 124, or image capture device 126 of vehicle
200. Such image capture devices may be configured to obtain images
of an environment forward, to the side, and/or to the rear of
vehicle 200.
[0864] In some embodiments, vehicle 200 may include various
sensors. Such sensors may include one or more speed sensors, GPS
receivers, accelerometers, etc.
[0865] In some embodiments, recognized landmarks may be used in a
speed calibration process for the vehicle. Such recognized
landmarks may include those landmarks represented in sparse map
800, for example. FIG. 69 shows examples of landmarks that may be
used for calibrating speed of vehicle 200. For example, FIG. 69
shows landmarks such as a traffic sign 6906, a dashed lane marking
6902, a traffic light 6908, a stop line 6912, reflectors 6910, and
a lamp post 6914. Other landmarks may include, for example, an
arrow marking, a directional sign, a landmark beacon, a speed bump
6904, etc.
[0866] In some embodiments, processing unit 110 of vehicle 200 may
identify one or more recognized landmarks. Processing unit 110 may
identify the one or more recognized visual landmarks based on any
of the previously described techniques. For example, processing
unit 110 may receive a local map associated with sparse map 800 (or
may even receive or be loaded with sparse map 800) including
representations of recognized landmarks Because these landmarks may
be indexed and/or because processing unit 110 may be aware of a
current position of vehicle 200 (e.g., with respect to a target
trajectory along a road segment), processor unit 110 may anticipate
a location for the next expected recognized landmark as it
traverses a road segment. In this way, processor unit 110 may even
"look" to a particular location within images received from image
capture device 122 where the next recognized landmark is expected
to appear. Once the recognized landmark is located within a
captured image or captured images, processor unit 110 may verify
that the landmark appearing in the images is the expected
recognized landmark. For example, various characteristics
associated with the landmark in a captured image may be compared
with information stored in sparse data map 800 relative to the
recognized landmark. Such characteristics may include a size,
landmark type (e.g., speed limit sign, hazard sign, etc.),
position, distance from a previous landmark, etc. If the observed
characteristics for a landmark match those stored relative to a
recognized landmark, then processor unit 110 can conclude that the
observed landmark is the expected recognized landmark.
[0867] In some embodiments, after identifying a recognized
landmark, processing unit 110 may retrieve information associated
with the recognized landmarks. The information may include, for
example, positional information of the recognized landmarks. In
some embodiments, the information associated with the recognized
landmarks may be stored on a remote server, and processing unit 110
may instruct a wireless system of vehicle 200, which may include a
wireless transceiver, to retrieve the information associated with
the recognized landmarks. In other cases, the information may
already reside on vehicle 200 (e.g., within a local map from sparse
data map 800 received during navigation or within a sparse data map
800 preloaded into memory of vehicle 200). In some embodiments,
this positional information may be used to calibrate one or more
indicators of speed of an autonomous vehicle (e.g., one or more
speed sensors of vehicle 200).
[0868] FIG. 70 is a flowchart showing an exemplary process 7000 for
calibrating a speed of vehicle 200 consistent with disclosed
embodiments. At step 7002, processing unit 110 may receive from an
image capture device 122 a plurality of images representative of an
environment of vehicle 200. In some embodiments, images may be
captured at different times by image capture device 122 (e.g.,
images may be captured many times per second, for example). In some
embodiments, vehicle 200 may include a plurality of image capture
devices (e.g., image capture devices 122 and 124 of vehicle 200),
and processing unit 110 may receive from each image capture device,
a plurality of images representative of an environment of vehicle
200. The plurality of images received from each image capture
device may include images captured at different times by one or
more of the image capture devices on the vehicle.
[0869] At step 7004, processing unit 110 may analyze the plurality
of images to identify at least two recognized landmarks present in
the images. The two recognized landmarks need not be present in a
single image from among the plurality of images. In fact, in many
cases, the two recognized landmarks identified in the plurality of
images will not appear in the same images. Rather, a first
recognized landmark may be identified in a first image received
from an image capture device. At a later time, and perhaps many
image frames later (e.g., 10 s, 100 s, or 1000 s of image frames
later, or more), a second recognized landmark may be identified in
another of the plurality of images received from the image capture
device. The first recognized landmark may be used to determine a
first location S1 of the vehicle along a target trajectory at time
T1, and the second recognized landmark may be used to determine a
second location S2 of the vehicle along the target trajectory at
time T2. Using information such as a measured distance between S1
and S2 and knowing a time difference between T1 and T2 may enable
the processor unit of the vehicle to determine a speed over which
the distance between S1 and S2 was covered. This speed can be
compared to an integrated velocity obtained based on an output of
the vehicle's speed sensor. In some embodiments, this comparison
may yield a correction factor needed to adjust/calibrate the
vehicle's speed sensor to match the speed determined based on the
S1 to S2 speed calculation.
[0870] Alternatively, or additionally, the processor unit may use
an output of the vehicle's speed sensor to determine a sensor-based
distance reading between S1 and S2. This sensor based distance
reading can be compared to a calculated distance between S1 and S2
in order to determine an appropriate correction factor to calibrate
the vehicle's speed sensor.
[0871] Processing unit 110 may identify recognized landmarks in a
captured image stream according to any of the techniques described
elsewhere in the disclosure. For example, processing unit 110 may
compare one or more observed characteristics of a potential
landmark to characteristics for a recognized landmark stored in
sparse data map 800. Where one or more of the observed
characteristics is found to match the stored characteristics, then
processing unit 110 may conclude that the observed potential
landmark is, in fact, a recognized landmark. Such characteristics
may include, among other things, size, shape, location, distance to
another recognized landmark, landmark type, condensed image
signature, etc.
[0872] At step 7006, processing unit 110 may determine, based on
known locations of the two recognized landmarks, a value indicative
of a distance between the at least two recognized landmarks. For
example, as discussed above, processing unit 110 may retrieve or
otherwise rely upon information associated with the recognized
landmarks after identifying the recognized landmarks. Further, the
information may include positional information of the recognized
landmarks, and processing unit 110 may compute a distance between
the two recognized landmarks based on the retrieved positional
information associated with the two landmarks Positional
information may include, for example, global coordinates of each
recognized landmark determined, for example, based on an
aggregation of position determinations (e.g., GPS based position
determinations) made by a plurality of vehicles upon prior
traversals along the road segments including the two recognized
landmarks.
[0873] At step 7008, processing unit 110 may determine, based on an
output of at least one sensor associated with the autonomous
vehicle, a measured distance between the at least two
landmarks.
[0874] In some embodiments, processing unit 110 may use an odometry
technique based on images captured by image capture device 122,
inertial sensors, and/or a speedometer of vehicle 200 to measure
the distance between the two recognized landmarks. For example, as
noted above, a first position of the vehicle S1 may be used as a
starting point and a second position of the vehicle S2 may be used
as an ending point. These positions may be determined based on
images collected of the first and second recognized landmarks,
respectively, using techniques described in other sections of the
disclosure. The vehicle sensors (e.g., the speedometer) can be used
to measure a distance between location S1 and S2. This measured
distance may be compared to a calculated distance between locations
S1 and S2, for example, along a predetermined target trajectory of
the vehicle.
[0875] In some embodiments, S1 and S2 may be selected according to
a particular relationship with the recognized landmarks. For
example, S1 and S2 may be selected as locations where lines
extending from the first and second landmarks, respectively,
intersect the target trajectory at right angles. Of course, any
other suitable relationship may also be used. In such embodiments,
where S2 and S1 are defined according to a predetermined
relationship, a distance between S2 and S1 may be known and
represented, for example, in sparse data map 800 (e.g., as a
distance value to the preceding recognized landmark). Thus, rather
than having to calculate a distance between S1 and S2, in such
embodiments, this distance value may already be available from
sparse data map 800. As in previous embodiments, the predetermined
distance between S1 and S2 may be compared to the distance between
S1 and S2 measured using the vehicle sensors.
[0876] For example, in some embodiments, measuring the distance
between the two landmarks may be done via a GPS device (e.g.,
position sensor 130). For example, two landmarks may be selected,
which are distant from each other (e.g., 5 km) and the road between
them may be rather straight. A length of that road segment may be
measured, for example, by subtracting the GPS coordinates of the
two landmarks. Each such coordinate may be measured with an error
of a few meters (i.e., the GPS error), but due to the long length
of the road segment this may be a relatively small error.
[0877] At step 7010, processing unit 110 may determine a correction
factor for the at least one sensor based on a comparison of the
value indicative of the distance between the at least two
recognized landmarks and the measured distance between the at least
two landmarks. The correctional factor may be, for example, a ratio
of the value indicative of the distance between the at least to
recognized landmarks and the measured distance between the at least
two landmarks. In some embodiments, the correction factor may be
referred to as a calibration factor and may represent a value that
may be used to transform the measured distance value based on the
vehicle's sensors into the calculated/predetermined distance
value.
[0878] In an optional step, processing unit 110 may determine a
composite correction factor based on a plurality of determined
correction factors. Correction factors of the plurality of
determined correction factors may be determined based on different
set of landmarks. In some embodiments, the composite correction
factor is determined by averaging the plurality of determined
correction factors or by finding a mean of the plurality of
determined correction factors.
[0879] FIG. 71 is a diagrammatic top view representation of
exemplary vehicle 200 including a system for calibrating an
indicator of speed of the vehicle consistent with disclosed
embodiments. In the example of FIG. 71, vehicle 200 is traveling on
a first road segment 7102A. FIG. 71 also shows a second road
segment 7102B and lane marks 7104, 7106. A road segment is includes
any portion of a road.
[0880] In some embodiments, processing unit 110 may determine a
distance along a road segment (e.g., road segments 7102A or 7102B)
using one or more sensors of vehicle 200. In one example,
processing unit 110 may determine, using one or more sensors of
vehicle 200, a road signature profile associated with the road
segment vehicle 200 is traveling on (e.g., road segment 7102A).
Such road signature profile may be associated with any
discernible/measurable variation in at least one parameter
associated with the road segment. In some cases, such profile may
be associated with, for example, variations in surface roughness of
a particular road segment, variations in road width over a
particular road segment, variations in distances between dashed
lines painted along a particular road segment, variations in road
curvature along a particular road segment, etc. As discussed above,
FIG. 11D shows exemplary road signature profile 1160. While a road
signature profile may represent any of the parameters mentioned
above, or others, in one example, the road signature profile may
represent a measure of road surface roughness, as obtained, for
example, by monitoring one or more sensors providing outputs
indicative of an amount of suspension displacement as vehicle 200
travels on first road segment 7102A. Alternatively, road signature
profile 1160 may represent variation in road width, as determined
based on image data obtained via image capture device 122 of
vehicle 200 traveling on first road segment 7102A. Such profile may
be useful, for example, in determining a particular location of an
autonomous vehicle relative to a particular target trajectory. That
is, as it traverses a road segment, an autonomous vehicle may
measure a profile associated with one or more parameters associated
with the road segment. If the measured profile can be
correlated/matched with a predetermined profile that plots the
parameter variation with respect to position along the road
segment, then the measured and predetermined profiles may be used
(e.g., by overlaying corresponding sections of the measured and
predetermined profiles) in order to determine a current position
along the road segment and, therefore, a current position relative
to a target trajectory for the road segment. A distance along a
road segment may be determined based on a plurality of positions
determined along a road segment.
[0881] FIG. 72 is a flowchart showing exemplary process 7200 for
calibrating an indicator of speed of vehicle 200 consistent with
disclosed embodiments. In some embodiments, vehicle 200 may
calibrate the indicator of speed of vehicle 200 by calculating a
correction factor based on a distance determined along the road
segment and a distance value received via the wireless transceiver.
That is, rather than determining positions S1 and S2 based on
landmarks and then calculating a distance between positions S1 and
S2, a distance value for a predetermined portion of a road segment
may be received via sparse data map 800 (e.g., via a wireless
transceiver).
[0882] At step 7204, processing unit 110 may receive, via a
wireless transceiver, a distance value associated with the road
segment. In one example, the wireless transceiver may be a
3GPP-compatible or an LTE-compatible transceiver. The distance
value associated with the road segment stored on the remote server
may be determined based on prior measurements made by a plurality
of measuring vehicles. For example, a plurality of vehicles may
have previously traveled on the same road segment in the past and
uploaded the determined distance values associated with the road
segment (e.g., between two or more predetermined reference points,
landmarks, etc.) to the remote server. The distance value
associated with the road segment stored on the remote server may be
an average of the distance values determined by the plurality of
measuring vehicles.
[0883] In some embodiments, the distance value associated with the
road segment stored on the remote server may be determined based on
prior measurements made by at least 100 measuring vehicles. In
other embodiments, the distance value associated with the road
segment stored on the remote server may be determined based on
prior measurements made by at least 1000 measuring vehicles.
[0884] At step 7206, processing unit 110 may determine a correction
factor for the at least one speed sensor based on the determined
distance along the road segment and the distance value received via
the wireless transceiver. The correctional factor may be, for
example, a ratio of the distance along the road segment determined
using a sensor and the distance value received via the wireless
transceiver. And, the correction factor may represent a value that
may be used to transform the measured distance value based on the
vehicle's sensors into the received/predetermined distance
value.
[0885] In an optional step, processing unit 110 may determine a
composite correction factor based on a plurality of determined
correction factors. Correction factors of the plurality of
determined correction factors may be determined based on different
landmarks. In some embodiments, the composite correction factor is
determined by averaging the plurality of determined correction
factors or by finding a mean of the plurality of determined
correction factors.
[0886] Determining Lane Assignment Based on Recognized Landmark
Location
[0887] In addition to determining a lane assignment based on
analysis of a camera output (e.g., seeing additional lanes to the
right and/or left of a current lane of travel for the vehicle), the
system may determine and/or validate a lane assignment based on a
determined lateral position of recognized landmarks relative to the
vehicle.
[0888] FIG. 73 is a diagrammatic illustration of a street view of
an exemplary road segment, consistent with disclosed embodiments.
As shown in FIG. 73, road segment 7300 may include a number of
components, including road 7310, lane marker 7320, landmarks 7330,
7340, and 7350, etc. In addition to the components depicted in
exemplary road segment 7300, a road segment may include other
components, including fewer or additional lanes, landmarks, etc.,
as would be understood by one of ordinary skill in the art.
[0889] In one embodiment, road segment 7300 may include road 7310,
which may be divided by one or more lane markers 7320 into two or
more lanes. Road segment 7300 may also include one or more
vehicles, such as vehicle 7360. Moreover, road segment 7300 may
include one or more landmarks, such as landmarks 7330, 7340, and
7350. In one embodiment, such as shown in FIG. 73, landmarks may be
placed alongside road 7310. Landmarks placed alongside road 7310
may include, for example, traffic signs (e.g., speed limit signs,
such as landmarks 7330 and 7340), mile markers (e.g., landmark
7350), billboards, exit signs, etc. Landmarks may also include
general purpose signs (e.g., non-semantic signs relating to
businesses or information sources, etc.). Alternatively, landmarks
may be placed on or above road 7310. Landmarks placed on or above
road 7310 may include, for example, lane markers (e.g., lane marker
7320), reflectors, exit signs, marquees, etc. Landmarks can also
include any of the examples discussed elsewhere in this
disclosure.
[0890] FIG. 74 is a diagrammatic illustration of a birds-eye view
of an exemplary road segment, consistent with disclosed
embodiments. As shown in FIG. 74, exemplary road segment 7400 may
include a number of components, including road 7405, lane marker
7410, vehicle 7415, traversed path 7420, heading 7425, predicted
path 7430, predetermined road model trajectory 7435, landmarks
7440, 7455, and 7470, direct offset distances 7445, 7460, and 7475,
and lateral offset distances 7450 and 7465. In addition to the
components depicted in exemplary road segment 7300, a road segment
may include other components, including fewer or additional lanes,
landmarks, and vehicles, as would be understood by one of ordinary
skill in the art.
[0891] In one embodiment, road segment 7400 may include road 7405,
which may be divided by one or more lane markers 7410 into two or
more lanes. Road segment 7300 may also include one or more
vehicles, such as vehicle 7415. Moreover, road segment 7400 may
include one or more landmarks, such as landmarks 7440, 7455, and
7470.
[0892] In one embodiment, vehicle 7415 may travel along one or more
lanes of road 7405 in a path. The path that vehicle 7415 has
already traveled is represented in FIG. 74 as traversed path
7420.
[0893] The direction in which vehicle 7415 is headed is depicted as
heading 7425. Based on the current location of vehicle 7145 and
heading 7425, among other factors, a path that vehicle 7415 is
expected to travel, such as predicted path 7430, may be determined.
FIG. 74 also depicts predetermined road model trajectory 7435,
which may represent an ideal path for vehicle 7415.
[0894] In one embodiment, direct offset distances 7445, 7460, and
7475 may represent the distance between vehicle 7415 and landmarks
7440, 7455, and 7470, respectively. Lateral offset distances 7450
and 7465 may represent the distance between vehicle 7415 and the
landmarks 7440 and 7455 when vehicle 7415 is directly alongside
those landmarks.
[0895] For example, two techniques may be used to calculate the
number of lanes based on the lateral distance estimation between
the host vehicle and the landmark. As a first example, a clustering
technique may be used. Using mean-shift clustering, the system may
calculate the number of lanes and the lane assignment for each
drive. Next, for enriching the number of observations and to
provide observations from each lane, the system may add
observations for the adjacent lanes (e.g., if the lanes' DNN
networks decided there are such lanes). Next, the system may
determine the road width and splitting it into lanes based on the
calculated lane width. As a second example, in another technique,
based on sightings of vehicles where the lanes' DNN network
determined they are either on the extreme (left or right) lane or
on the one adjacent to it, the system may create a set of
estimations of the lateral distance between the land mark and the
extreme left or right lane mark. Next, using either a voting or a
least squares mechanism, the system may determine an agreed
distance estimation between the land mark and the road edges. Next,
from the distance estimates to the road edges, the system may
extract the road width, and determine the number of lanes by
dividing the road width by the median lane width observed in the
drives. The system may assign a lane to each drive based on which
bin the observed distance between the host.
[0896] FIG. 75 is a flowchart showing an exemplary process 7500 for
determining a lane assignment for a vehicle (which may be an
autonomous vehicle) along a road segment, consistent with disclosed
embodiments. The steps associated with this exemplary process may
be performed by the components of FIG. 1. For example, the steps
associated with the process may be performed by application
processor 180 and/or image processor 190 of system 100 illustrated
in FIG. 1.
[0897] In step 7510, at least one processor receives from a camera
at least one image representative of an environment of the vehicle.
For example, image processor 128 may receive one or more images
from one or more of cameras 122, 124, and 126 representing an
environment of the vehicle. Image processor 128 may provide the one
or more images to application processor 180 for further analysis.
The environment of the vehicle may include the area surrounding the
exterior of the vehicle, such as the road segment and any signs,
buildings, or landscaping along the road segment. In one
embodiment, the environment of the vehicle includes the road
segment, a number of lanes, and the at least one recognized
landmark.
[0898] In step 7520, the at least one processor analyzes the at
least one image to identify at least one recognized landmark. In
one embodiment, the at least one recognized landmark includes at
least one of a traffic sign, an arrow marking, a lane marking, a
dashed lane marking, a traffic light, a stop line, a directional
sign, a reflector, a landmark beacon, or a lamppost, etc. For
example, the at least one recognized landmark may include landmarks
7330, 7340, and 7350, each of which is a traffic sign. In
particular, landmarks 7330 and 7340 are speed limit signs, and
landmark 7350 is a mile marker sign. In another embodiment, the at
least one recognized landmark includes a sign for a business. For
example, the at least one recognized landmark may include a
billboard advertisement for a business or a sign marking the
location of a business.
[0899] In step 7530, the at least one processor determines an
indicator of a lateral offset distance between the vehicle and the
at least one recognized landmark. In some embodiments, the
determination of the indicator of the lateral offset distance
between the vehicle and the at least one recognized landmark may be
based on a known position of the at least one recognized landmark.
The known position of the at least one recognized landmark may be
stored, for example, in memory 140 or map database 160 (e.g., as
part of sparse map 800).
[0900] In step 7540, the at least one processor determines a lane
assignment of the vehicle along the road segment based on the
indicator of the lateral offset distance between the vehicle and
the at least one recognized landmark. For example, the at least one
processor may determine which lane the vehicle is travelling in
based the indicator of lateral offset distance. For example, a lane
assignment may be determined based on knowledge of a lateral
distance from the recognized landmark to a lane edge closest to the
recognized landmark, to any lane edges present on the road, to a
target trajectory associated with a road segment, or to multiple
target trajectories associate with the road segment, etc. The
determined indicator of lateral offset distance between the
recognized landmark and the host vehicle may be compared to any of
these quantities, among others, and then used to determine a
current lane assignment based on one or more arithmetic and/or
trigonometric calculations.
[0901] In one embodiment, the at least one recognized landmark
includes a first recognized landmark on a first side of the vehicle
and a second recognized landmark on a second side of the vehicle
and wherein determination of the lane assignment of the vehicle
along the road segment is based on a first indicator of lateral
offset distance between the vehicle and the first recognized
landmark and a second indicator of lateral offset distance between
the vehicle and the second recognized landmark. The lane assignment
may be determined based on a ratio of the first indicator of
lateral offset distance to the second indicator of lateral offset
distance. For example, if the vehicle is located 20 feet from a
landmark posted on the left edge of the road and 60 feet from a
landmark posted on the right edge of the road, then the lane
assignment may be determined based on this ratio, given information
on the number of lanes on the road segment or lane width.
Alternatively, the lane assignment may be calculated separately
based on the indicators of lateral offset distance between the
vehicle and the first and second recognized landmarks, and these
separate calculations may be checked against one another to verify
that the determined lane assignment(s) are correct.
[0902] Super Landmarks as Navigation Aids
[0903] The system may navigate by using recognized landmarks to aid
in determining a current location of an autonomous vehicle along a
road model trajectory. In some situations, however, landmark
identity may be ambiguous (e.g., where there is a high density of
similar types of landmarks). In such situations, landmarks may be
grouped together to aid in their recognition. For example,
distances between landmarks within a group of landmarks may be used
to create a super landmark signature to aid in positive
identification of the landmarks. Other characteristics, such as
landmark sequences within a group of landmarks, may also be
used.
[0904] FIG. 76 is an illustration of a street view of an exemplary
road segment, consistent with disclosed embodiments. As shown in
FIG. 76, road segment 7600 may include a number of components,
including road 7610, lane marker 7620, vehicle 7630, and landmarks
7640, 7650, 7660, 7670, and 7680. In addition to the components
depicted in exemplary road segment 7600, a road segment may include
other components, including fewer or additional lanes, landmarks,
and vehicles, as would be understood by one of ordinary skill in
the art.
[0905] In one embodiment, road segment 7600 may include road 7610,
which may be divided by one or more lane markers 7620 into two or
more lanes. Road segment 7600 may also include one or more
vehicles, such as vehicle 7630. Moreover, road segment 7600 may
include one or more landmarks, such as landmarks 7640, 7650, 7660,
7670, and 7680. In one embodiment, landmarks may be assigned to
structures/objects associated with road 7610 (e.g., landmarks 7670
and 7680). Landmarks along road 7610 may include, for example,
traffic signs (e.g., mile markers, such as landmark 7670),
billboards (e.g., landmark 7680), lane markers (e.g., landmark
7620), reflectors, traffic signs (e.g., exit signs, such as
landmarks 7640, 7650, and 7660), marquees, etc. Landmarks
identified or otherwise represented in sparse data map 800 may be
referred to as recognized landmarks.
[0906] Some areas, especially in urban environments, may have high
densities of recognized landmarks. Thus, in some cases,
distinguishing between certain recognized landmarks may be
difficult based on comparisons based solely on landmark size,
shape, type, indexed location, etc. To further aid in identifying
one or more recognized landmarks from within images captured of a
vehicle's environment, a group of two or more landmarks may be
designated as a super landmark. Such a super landmark may offer
additional characteristics that may aid in identifying or verifying
one or more recognized landmarks (e.g., from among the group of
landmarks).
[0907] In FIG. 76, for example, a super landmark may be formed from
the group consisting of landmarks 7640, 7650, 7660, 7670, and 7680,
or some subset of two or more of those landmarks. By grouping two
or more landmarks together, the probability of accurately
identifying constituent landmarks from a distant vantage point may
be increased.
[0908] A super landmark may be associated with one or more
characteristics, such as distances between constituent landmarks, a
number of landmarks in the group, an ordering sequence, one or more
relative spatial relationships between the members of the landmark
group, etc. Moreover, these characteristics may be used to generate
a super landmark signature. The super landmark signature may
represent a unique form of identifying the group of landmarks or
even a single landmark within the group.
[0909] FIG. 77A is an illustration of birds-eye view of an
exemplary road segment, consistent with disclosed embodiments. As
shown in FIG. 77, exemplary road segment 7700 may be associated
with a number of components, including road 7705, lane marker 7710,
vehicle 7715, traversed path 7720, predetermined road model
trajectory 7725, landmarks 7730, 7735, 7740, 7745, and 7750,
lateral offset vector 7755, and direct offset vector 7760. In
addition to the components depicted in exemplary road segment 7700,
a road segment may be associated with other components, including
fewer or additional lanes, landmarks, and vehicles, as would be
understood by one of ordinary skill in the art.
[0910] In one embodiment, road segment 7700 may include road 7705,
which may be divided by one or more lane markers 7710 into two or
more lanes. Road segment 7700 may also include one or more
vehicles, such as vehicle 7715. Moreover, road segment 7700 may
include one or more landmarks, such as landmarks 7730, 7735, 7740,
7745, and 7750.
[0911] In one embodiment, vehicle 7715 may travel along one or more
lanes of road 7705 in a path. The path that vehicle 7715 has
already traveled is represented in FIG. 77 as traversed path 7720.
FIG. 77 also depicts predetermined road model trajectory 7725,
which may represent a target path for vehicle 7715.
[0912] In one embodiment, a direct offset vector may be a vector
connecting vehicle 7715 and a landmark. For example, direct offset
vector 7760 may be a vector connecting vehicle 7715 and landmark
7730. The distance between vehicle 7715 and a landmark may be
equivalent to the magnitude of direct offset vector connecting
vehicle 7715 with the landmark. A lateral offset vector may be a
vector connecting vehicle 7715 with a point on the side of the road
in line with a landmark. The lateral offset distance for a vehicle
with respect to a landmark may be equivalent to the magnitude of
the lateral offset vector and, further, may be equivalent to the
distance between vehicle 7715 and the landmark when vehicle 7715 is
directly alongside the landmark. The lateral offset distance
between vehicle 7715 and a landmark may be computed by determining
a sum of a first distance between the vehicle and the edge of the
road on which the landmark is located and a second distance between
that edge and the landmark.
[0913] FIG. 77B provides a street level view of a road segment
including a super landmark made up of four recognized landmarks--a
speed limit sign 7790, a stop sign 7791, and two traffic lights
7792 and 7793. Any of the recognized landmarks included in the
super landmark group may be identified based on recognition of
various relationships between the landmarks included in the group.
For example, a sequence, which may be stored in sparse data map
800, of a speed limit sign at a distance D1, followed by a stop
sign at a distance D2, and two traffic lights at a distance D3 from
a host vehicle (where D3>D2>D1) may constitute a unique,
recognizable characteristic of the super landmark that may aid in
verifying speed limit sign 7790, for example, as a recognized
landmark from sparse data map 800.
[0914] Other relationships between the members of a super landmark
may also be stored in sparse data map 800. For example, at a
particular predetermined distance from recognized landmark 7790 and
along a target trajectory associated with the road segment, the
super landmark may form a polynomial 7794 between points A, B, C,
and D each associated with a center of a member of the super
landmark. The segment lengths A-B, B-C, C-D, and D-A may be
determined and stored in sparse data map 800 for one or more
positions relative to the location of the super landmark.
Additionally, a triangle 7795 may be formed by traffic light 7793,
traffic light 7792, and stop sign 7791. Again, the lengths of the
sides as well as angles of triangle 7795 may be referenced in
sparse data map 800 for ne or more positions relative to the
location of the super landmark. Similar information may be
determined and stored for a triangles 7796 (between points A, C,
and D) and 7797 (between points A-B-C). Such angles, shapes, and
segment lengths may aid in recognition of a super landmark from a
certain viewing location relative to the super landmark. For
example, once the vehicle is located at a viewing location for
which visual information for the super landmark is included in
sparse data map 800, the processing unit of the vehicle can analyze
images captured by one or more cameras onboard the vehicle to look
for expected shapes, patterns, angles, segment lengths, etc. to
determine whether a group of objects forms an expected super
landmark. Upon verifying the recognized super landmark, position
determinations for the vehicle along a target trajectory may
commence based on any of the landmarks included in a super landmark
group.
[0915] FIG. 78 is a flowchart showing an exemplary process 7800 for
autonomously navigating a vehicle along a road segment, consistent
with disclosed embodiments. The steps associated with this
exemplary process may be performed by the components of FIG. 1. For
example, the steps associated with the process may be performed by
application processor 180 and/or image processor 190 of system 100
illustrated in FIG. 1.
[0916] In step 7810, at least one processor may receive from a
camera at least one image representative of an environment of the
vehicle. For example, image processor 128 may receive one or more
images from one or more of cameras 122, 124, and 126 representing
an environment of the vehicle. Image processor 128 may provide the
one or more images to application processor 180 for further
analysis. The environment of the vehicle may include the area
surrounding the exterior of the vehicle, such as the road segment
and any signs, buildings, or landscaping along the road segment. In
one embodiment, the environment of the vehicle includes the road
segment, a number of lanes, and the at least one recognized
landmark.
[0917] In step 7820, the at least one processor may analyze the at
least one image to identify a super landmark and identify at least
one recognized landmark from the super landmark. In one embodiment,
the at least one recognized landmark includes at least one of a
traffic sign, an arrow marking, a lane marking, a dashed lane
marking, a traffic light, a stop line, a directional sign, a
reflector, a landmark beacon, or a lamppost. For example, the at
least one recognized landmark may include landmarks 7640, 7650,
7660, and 7670, each of which is a traffic sign. In particular,
landmarks 7640, 7650, and 7660 are exit signs, and landmark 7670 is
a mile marker sign. In another embodiment, the at least one
recognized landmark includes a sign for a business. For example,
the at least one recognized landmark may include a billboard
advertisement for a business (e.g., landmark 7680) or a sign
marking the location of a business.
[0918] As noted above, identification of the at least one landmark
is based, at least in part, upon one or more landmark group
characteristics associated with the group of landmarks. In one
embodiment, the one or more landmark group characteristics may
include relative distances between members of the group of
landmarks. For example, the landmark group characteristics may
include information that specifies the distance that separates each
landmark in the group from each of the other landmarks in the
group. In another embodiment, the one or more landmark group
characteristics may include an ordering sequence of members of the
group of landmarks. For example, the group of landmarks may be
associated with a sequence indicating the order in which the
landmarks appear from left to right, front to back, etc., when
viewed from the road. In yet another embodiment, the one or more
landmark group characteristics may include a number of landmarks
included in the group of landmarks.
[0919] Referring to FIG. 76 as an example, a landmark group (or
super landmark) may consist of landmarks 7640, 7650, 7660, 7670,
and 7680. This landmark group may be associated with landmark group
characteristics, including the relative distances between each
landmark and each of the other landmarks in the group, an ordering
sequence of landmarks in the group, and a number of landmarks. In
the example depicted in FIG. 76, the landmark group characteristics
may include information that specifies the distance between
landmark 7680 and each of landmarks 7640, 7650, 7660, and 7670, the
distance between landmark 7640 and each of landmarks 7650, 7660,
and 7670, the distance between landmark 7650 and each of landmarks
7660 and 7670, and the distance between landmarks 7660 and
7670.
[0920] Further, in this example, an ordering sequence may indicate
that the order of landmarks in the group from left to right (when
viewed from the perspective of a vehicle driving along the road,
e.g., vehicle 7630) is 7680, 7640, 7650, 7660, and 7670.
Alternatively or additionally, the ordering sequence may indicate
that the order of landmarks in the group from front to back (e.g.,
earliest to latest traversed in a path along the road) is first
7670, then 7640, 7650, and 7660, and last 7680. Moreover, the
landmark group characteristics may specify that this exemplary
landmark group includes five landmarks.
[0921] In one embodiment, identification of the at least one
landmark may be based, at least in part, upon a super landmark
signature associated with the group of landmarks. A super landmark
signature may be a signature for uniquely identifying a group of
landmarks. In one embodiment, a super landmark signature may be
based on one or more of the landmark group characteristics
discussed above (e.g., number of landmarks, relative distance
between landmarks, and ordering sequence of landmarks).
[0922] Once a recognized landmark is identified based on an
identified characteristic of the super landmark group,
predetermined characteristics of the recognized landmark may be
used to assist a host vehicle in navigation. For example, in some
embodiments, the recognized landmark may be used to determine a
current position of the host vehicle. In some cases, the current
position of the host vehicle may be determined relative to a target
trajectory from sparse data model 800. Knowing the current position
relative to a target trajectory may aid in determining a steering
angle needed to cause the vehicle to follow the target trajectory
(for example, by comparing a heading direction to a direction of
the target trajectory at the determined current position of the
vehicle relative to the target trajectory).
[0923] A position of the vehicle relative to a target trajectory
from sparse data map 800 may be determined in a variety of ways.
For example, in some embodiments, a 6D Kalman filtering technique
may be employed. In other embodiments, a directional indicator may
be used relative to the vehicle and the recognized landmark. For
example, in step 7830, the at least one processor may determine,
relative to the vehicle, a directional indicator associated with
the at least one landmark. In one embodiment, the directional
indicator may include a line or vector connecting the vehicle and
the at least one landmark. The directional indicator may indicate
the direction in which the vehicle would have to travel to arrive
at the at least one landmark. For example, in the exemplary
embodiment depicted in FIG. 77, direct offset vector 7760 may
represent a directional indicator associated with landmark 7730
relative to vehicle 7715.
[0924] In step 7840, the at least one processor may determine an
intersection of the directional indicator with a predetermined road
model trajectory associated with the road segment. In one
embodiment, the predetermined road model trajectory may include a
three-dimensional polynomial representation of a target trajectory
along the road segment. The target trajectory may include an ideal
trajectory for the vehicle for a specific location along the road
segment. In one embodiment, the at least one processor may further
be programmed to determine a location along the predetermined road
model trajectory based on a vehicle velocity. For example, the at
least one processor may access information the location and
velocity of the vehicle at a specific time, compute an estimated
distance traveled based on the velocity and time passed since the
vehicle was at that location, and identify a point along the
predetermined road model trajectory that is the estimated distance
beyond the previously observed location.
[0925] In step 7850, the at least one processor may determine an
autonomous steering action for the vehicle based on a direction of
the predetermined road model trajectory at the determined
intersection. In one embodiment, determining an autonomous steering
action for the vehicle may include comparing a heading direction of
the vehicle to the predetermined road model trajectory at the
determined intersection. In one embodiment, the autonomous steering
action for the vehicle may include changing the heading of the
vehicle. In another embodiment, the autonomous steering action for
the vehicle may include changing the speed of the vehicle by
applying the gas or brake to accelerate or decelerate,
respectively.
[0926] Adaptive Autonomous Navigation
[0927] In some embodiments, the disclosed systems and methods may
provide adaptive autonomous navigation and update a sparse map. For
example, the disclosed systems and methods may adapt navigation
based on user intervention, provide adapt navigate based on
determinations made by the system (e.g., a self-aware system),
adapt a road model based on whether observed conditions on a road
are transient or non-transient (e.g., an adaptive road model
manager), and manage a road model based on selective feedback
received from one or more systems. These adaptive systems and
methods are discussed in further detail below.
[0928] Adaptive Navigation Based on User Intervention
[0929] In some embodiments, the disclosed systems and methods may
involve adaptive navigation based on user intervention. For
example, as discussed in earlier sections, a road model assembled
based upon input from existing vehicles may be distributed from a
server (e.g., server 1230, discussed earlier) to vehicles. Based on
feedback received from autonomous vehicles, the system may
determine whether one or more updates (e.g., adaptations to the
model) are needed to the road model to account for changes in road
situations, for example. For example, in some embodiments, a user
may intervene to alter a maneuver of a vehicle (which may be an
autonomous vehicle) while the vehicle is traveling on a roadway
according to the road model. An altered maneuver of the vehicle
based on user intervention may be made in contradistinction to
override predetermined vehicular trajectory instructions provided
by the road model. Further, the disclosed systems and methods may
capture and store navigational situation information about the
situation in which the override occurred and/or send the
navigational situation information from the vehicle to the server
over one or more networks (e.g., over a cellular network and/or the
Internet, etc.) for analysis. As discussed herein, navigational
situation information may include one or more of a location of a
vehicle, a distance of a vehicle to a recognized landmark, an
observed condition, a time of day, an image or a video captured by
an image capture device of a vehicle, or any other suitable
informational source regarding a navigational situation.
[0930] FIG. 79A illustrates a plan view of vehicle 7902 traveling
on a roadway 7900 approaching wintery and icy road conditions 7930
at a particular location consistent with disclosed embodiments.
Vehicle 7902 may include a system that provides navigation
features, including features that adapt navigation based on user
intervention. Vehicle 7902 may include components such as those
discussed above in connection with vehicle 200. For example, as
depicted, vehicle 7902 may be equipped with image capture devices
122 and 124; more or fewer image capture devices (including
cameras, for example) may be employed.
[0931] As shown, roadway 7900 may be subdivided into lanes, such as
lanes 7910 and 7920. Lanes 7910 and 7920 are shown as examples; a
given roadway 7900 may have additional lanes based on the size and
nature of the roadway, for example, an interstate highway. In the
example of FIG. 79A, vehicle 7902 is traveling in lane 7910
according to instructions derived from the road model (e.g., a
heading direction along a target trajectory) and approaching
wintery and icy road conditions 7930 at a particular vehicle
location as identified by, e.g., position sensor 130, a temperator
sensor, and/or an ice sensor. Where a user intervenes in order to
override autonomously generated steering instructions (e.g., those
enabling the vehicle to maintain a course along the target
trajectory) and alter the course of the vehicle 7902 traveling in
lane 7910 (e.g., to turn due to the icy conditions), processing
unit 110 may store navigational situation information and/or send
the navigational situation information to a server of the road
model system for use in making a possible update. In this example,
the navigational situation information may include a location of
the vehicle identified by position sensor 130 or based on a
landmark-based determination of position along a target trajectory,
an image captured by an image capture device included in the
vehicle depicting the vehicle's environment, an image stream (e.g.,
a video), sensor output data (e.g., from speedometers,
accelerometers, etc.).
[0932] In some embodiments, processing unit 110 may send the
navigational situational information from the vehicle to the server
via a wireless data connection over one or more networks (e.g.,
over a cellular network and/or the Internet, etc.). The server side
may analyze the received information (e.g., using automated image
analysis processes) to determine whether any updates to sparse data
model 800 are warranted based on the detected user intervention. In
this example, the server may recognize the presence of wintery or
icy road conditions in the images (a temporary or transient
condition) and, therefore, may determine not to change or update
the road model.
[0933] FIG. 79B illustrates a plan view of vehicle 7902 traveling
on a roadway approaching a pedestrian consistent with disclosed
embodiments. In the example of FIG. 79B, vehicle 7902 is driving in
lane 7910 of roadway 7900 with a pedestrian 7922. As shown,
pedestrian 7922 may suddenly become positioned directly in the
roadway 7900 crossing either lane 7910 or 7920. In this example,
when a user intervenes to override the road model in order to avoid
the pedestrian and alter the maneuver of the vehicle 7902 traveling
in lane 7910 along a target trajectory associated with the road
segment, navigational situation information including a position of
the vehicle along a target trajectory for a road segment (e.g.,
determined based on a distance d.sub.1 to a recognized landmark,
such as speed limit sign 7923), video or images including capturing
conditions of the vehicle's surroundings during the user
intervention, sensor data, etc. In example shown in FIG. 49B, given
the temporary nature of a crossing pedestrian the server may
determine not change or update the road model.
[0934] Although the example shown in FIG. 79B depicts speed limit
sign 7923, other recognized landmarks (not shown) may be used.
Landmarks may include, for example, any identifiable, fixed object
in an environment of at least one road segment or any observable
characteristic associated with a particular section of the road
segment. In some cases, landmarks may include traffic signs (e.g.,
speed limit signs, hazard signs, etc.). In other cases, landmarks
may include road characteristic profiles associated with a
particular section of a road segment. Further examples of various
types of landmarks are discussed in previous sections, and some
landmark examples are shown in FIG. 10.
[0935] FIG. 79C illustrates a plan view of a vehicle traveling on a
roadway in close proximity to another vehicle consistent with
disclosed embodiments. In the example of FIG. 79C, two vehicles
7902a and 7902b are driving in lane 7910 of roadway 7900. As shown,
vehicle 7902b has suddenly driven directly in front of vehicle
7902a in lane 7910 of roadway 7900. Where a user intervenes to
override the road model and alter the course of the vehicle 7902a
traveling in lane 7910 (e.g., to turn due to the proximate
vehicle), navigational situation information may be captured and
stored in memory (e.g., memory 140) and/or sent to a server (e.g.,
server 1230) for making a possible update to the road model.
[0936] For example, in this example, the navigational situation
information may include a location of vehicle 7902a. The
navigational situation information may further include one more
images depicting the environment of vehicle 7902 at the time of the
user intervention. Given the temporary nature of another contiguous
or proximate vehicle, however, the server may not change or update
the road model.
[0937] FIG. 79D illustrates a plan view of a vehicle traveling on a
roadway in a lane that is ending consistent with disclosed
embodiments. Vehicle 7902 may receive from image capture devices
122 and 124 at least one environmental image of a turning roadway
7900 representative of a lane 7910 ending. Lane 7910 may be ending
based on a recent change to lane 7910 resulting an abrupt
shortening distance of d.sub.2. For example, the lane may be ending
as a result of recently positioned concrete barriers at the site of
a construction zone. As a result of this unexpected shortening, a
user may intervene to change the course of vehicle 7902 in view of
the change to lane 7910. As will be discussed in more detail in
another section, it is also possible for processing unit 110 to
recognize the ending lane (e.g., based on captured images of
concrete barriers in front of the vehicle) and automatically adjust
the course of the vehicle and send navigational situation
information to the server for use in possible updates to sparse
data model 800. As a result of the user intervention, the system
may measure distances (such as c.sub.1 and c.sub.2). For example,
distances c.sub.1 and c.sub.2 may represent the distance from a
side of vehicle 7902 to the edge of lane 7910, be it lane
constraint 7924 or the dashed center line in the middle of roadway
7900 dividing lanes 7910/7920. In other embodiments, a distance may
be measured to lane constraint 7924 on the far side of lane 7920
(not shown). In addition to distances c.sub.1 and c.sub.2 described
above, in some embodiments, processing unit 110 may further be
configured to calculate distances w.sub.1 and w.sub.2 and midpoint
m of lane 7910 relative to one or more lane constraints associated
with that lane. When summed together, distances w.sub.1 and w.sub.2
equal measurement w as shown in FIG. 79D.
[0938] In this example, where a user intervenes to override the
road model to alter the maneuver of the vehicle 7902 traveling in
lane 7910, navigational situation information including distances
c.sub.1, c.sub.2, d.sub.2, w.sub.1, and w.sub.2 to a lane
constraint 7924 may be captured and stored in memory (e.g., memory
140) and/or sent to the server for making a possible update to the
road model. Of course, other navigational situation information may
also be collected and sent to a server for review. Such information
may include sensor outputs, captured images/image streams, a
position of the vehicle, etc. Given the permanent or semi-permanent
nature of an ending lane marked by concrete barriers, the server
may decide to change or update the road model. Accordingly,
vehicles may receive an updated to the road model that causes the
vehicles to follow a new or updated target trajectory for the road
segment upon approaching new lane constraint 7924.
[0939] FIG. 80 illustrates a diagrammatic side view representation
of an exemplary vehicle 7902 including system 100 consistent with
the disclosed embodiments. As is shown, vehicle 7902 may be limited
by a vision inhibitor such as glare 8002 from the sun and/or a
malfunctioning lamp 8004. Vehicle 7902 is additionally depicted
with sensors 8006 and system 100 is capable of determining whether
or not it is day or night. The sensors 8006 may include, for
example, an IR sensor and/or an accelerometer. For example, where a
user intervenes to override the road model to move vehicle 7902 to
avoid a glare produced by sun, the processing unit 110 may capture
navigational situation information reflecting a time of day and/or
the presence of glare. Processing unit 110 may store the
navigational situational information and/or transmit the
navigational situation information to a server for storage and/or
analysis. Given the temporary nature of the glare, the server may
decide not to change or update the road model.
[0940] FIG. 81 illustrates an example flowchart representing a
method for adaptive navigation of a vehicle based on user
intervention overriding the road model consistent with the
disclosed embodiments. In particular, FIG. 81 illustrates a process
8100 for adaptive navigation of a vehicle consistent with disclosed
embodiments. Steps of process 8100 may be performed by processing
unit 110 of system 100. Process 8100 may allow for user input and a
navigational maneuver based on analysis of an environmental image
Where there is user input that deviates from a navigational
maneuver prescribed by the road model, the maneuver may be altered
according to the user input and the conditions surrounding the user
input may be captured and stored and/or sent to a server for making
a possible update to the road model.
[0941] At step 8110, processing unit 110 may receive at least one
environmental image of an area forward of vehicle 7902. For
example, the image may show one or more recognized landmarks. As
discussed elsewhere in detail, a recognized landmark may be
verified in the captured image and used to determine a position of
the vehicle along a target trajectory for a particular road
segment. Based on the determined position, the processing unit 110
may cause one or more navigational responses, for example, to
maintain the vehicle along the target trajectory.
[0942] At step 8112, processing unit 110 may include determining a
navigational maneuver responsive to an analysis of at least one
environmental image of an area forward of vehicle 7902. For
example, based on the landmark-based position determination for the
vehicle along the target trajectory, the processing unit 110 may
cause one or more navigational responses to maintain the vehicle
along the target trajectory.
[0943] At step 8114, process 8100 may cause vehicle 7902 to
initiate the navigational maneuver. For example, processing unit
110 may send instructions to one or more systems associated with
vehicle 7902 to initiate the navigational maneuver and may cause
vehicle 7902 to drive according to a predetermined trajectory along
roadway 7900. Consistent with the disclosed embodiments, an
initiation instruction may be sent to a throttling system 220,
braking system 230, and/or steering system 240.
[0944] At step 8116, the system may receive a user input that
differs from one or more aspects of the navigational maneuver
implemented by processing unit 110 based on sparse data map 800.
For example, a user input to one or more of throttling system 220,
braking system 230, and/or steering system 240 may differ from an
initiated maneuver and cause an override to alter the maneuver
based on the received user input.
[0945] Based on detection of a user override or intervention
condition, processing unit 110 may collect navigational situation
information relating the vehicle and the user input at the time
before, during, and/or after the user intervention. For example,
processing unit 110 may receive information relating to the user
input, including information specifying at least one of a degree of
turn, an amount of acceleration, and an amount of braking of a
vehicle 7902, etc. caused by the user intervention (step 8118).
[0946] At step 8118, processing unit 110 may determine additional
navigational situation information relating to vehicle user input.
The navigational situation information may include, for example, a
location of the vehicle, a distance to one or more recognized
landmarks, a location determined by position sensor 130, one or
more images captured by an image capture device of vehicle 7902,
sensor outputs etc.
[0947] At step 8020, processing unit 110 may store the navigational
situation information into memory 140 or 150 of system 100 in
association with information relating to the user input.
Alternatively, in other embodiments, the navigational situation
information may be transmitted to a server (e.g., server 1230) for
use in a making a possible update to the road model. Alternatively,
in still yet other embodiments, system 100 may not store the
navigational situation information if system 100 determines that
the navigation situation information is associated with a condition
that may not occur in the future (e.g., a special condition or a
transient condition), such as related to pedestrian or an animal
moving in front of vehicle 7902. System 100 may determine that such
conditions do not warrant further analysis and this may determine
to not store the navigational situation information associated with
the transient condition.
[0948] Self-Aware System for Adaptive Navigation
[0949] In some embodiments, the disclosed systems and methods may
provide a self-aware system for adaptive navigation. For example, a
server (e.g., server 1230), may distribute a road model to
vehicles. Based on feedback received from autonomous vehicles, the
system may determine whether one or more updates (e.g., adaptations
to the model) are needed to the road model to account for changes
in road situations. For example, in some embodiments, a vehicle
(which may be an autonomous vehicle) may travel on a roadway based
on the road model and may make use of observations made by the
self-aware system in order to adjust a navigational maneuver of the
vehicle based on a navigational adjustment condition. As discussed
herein, a navigational adjustment condition may include any
observable or measurable condition in an environment of a vehicle.
The system may determine a navigational maneuver for the vehicle
based, at least in part, on a comparison of a motion of the vehicle
with respect to a predetermined model representative of a road
segment. The system may receive from a camera, at least one image
representative of an environment of the vehicle, and then
determine, based on analysis of the at least one image, an
existence in the environment of the vehicle of a navigational
adjustment condition. Based on this analysis, the system may,
without user intervention, cause the vehicle to adjust the
navigational maneuver based on the existence of the navigational
adjustment condition. The system may store information relating to
the navigational adjustment condition, including, for example,
data, an image, or a video related to the navigational adjustment
condition. And, the system may transmit the stored information to
one or more server-based systems for analysis and/or determination
of whether an update to the road model is needed.
[0950] In some embodiments, the system onboard the vehicle or in
the cloud may identify an object or a condition that is estimated
to be associated with the navigational adjustment condition. The
system may establish whether the navigational adjustment condition
is temporary or not and whether the road model should be updated or
not. The system may also establish in this way whether to collect
further information from future traversals of the same area,
location, road, region, etc.
[0951] FIG. 82A illustrates a plan view of a vehicle traveling on a
roadway with a parked car consistent with disclosed embodiments. In
particular, FIG. 82A illustrates vehicle 7902a traveling according
to a three-dimensional spline representative of a predetermined
path of travel 8200 (e.g., a target trajectory) along roadway 7900
where a second vehicle 7902c is parked directly in front of vehicle
7902a. Vehicle 7902a may include a system that provides navigation
features, including features that allow for navigation based on
user input. Vehicle 7902a may include components such as those
discussed above in connection with vehicle 200. For example, as
depicted, vehicle 7902a may be equipped with image capture devices
122 and 124; more or fewer image capture devices (including
cameras, for example) may be employed.
[0952] As shown, roadway 7900 may be subdivided into lanes, such as
lanes 7910 and 7920. Vehicle 7902a may receive from one or more of
image capture devices 122 and 124 at least one environmental image
including an image of a parked vehicle 7902c. In the example of
FIG. 82A, vehicle 7902a is traveling along path 8200 in lane 7910
according to instructions derived from the road model (e.g., a
heading direction along a target trajectory) and approaching parked
vehicle 7902c. Where the system overrides autonomously generated
steering instructions (e.g., those enabling the vehicle to maintain
a course along the target trajectory) to adjust a maneuver of
vehicle 7902a due to a navigational adjustment condition, e.g., to
avoid parked vehicle 7902c, navigational adjustment condition
information may be captured and stored in memory (e.g., memory 140)
and/or sent to a server (e.g., server 1230) for making a possible
update to the road model. In this example, the navigational
adjustment condition information may include a location of vehicle
7902c when the autonomous navigational change (e.g., made by the
self-aware system) was made. The vehicle position may be identified
by position sensor 130 or based on a landmark-based determination
of position along a target trajectory. Other navigational condition
information may be included in one or more images captured by an
image capture device included in vehicle 7902c depicting the
vehicle's environment (e.g., an image including parked vehicle
7902c), an image stream (e.g., a video), and/or sensor output data
(e.g., from speedometers, accelerometers, etc.).
[0953] In some embodiments, processing unit 110 may send the
navigational situational information from the vehicle to the server
via a wireless data connection over one or more networks (e.g.,
over a cellular network and/or the Internet, etc.). The server side
may analyze the received information (e.g., using automated image
analysis processes) to determine whether any updates to sparse data
model 800 are warranted based on the detected system intervention.
In this example, the server may recognize the presence of the
parked car in or near a target trajectory of the host vehicle and
determine that the parked car represents a temporary or transient
condition. Therefore, the server may determine not to change or
update the road model. However, in some embodiments, based on the
location of vehicle 7902a, the server may determine that the parked
car is located in a residential area and therefore may change or
update the road model due to the likelihood of vehicles being
parked along the shoulder of the road. Furthermore, in some
embodiments, system 100 onboard the vehicle may classify an object
or condition and system 100 may determine whether or not to change
or update the road model.
[0954] FIG. 82B illustrates a plan view of a vehicle traveling on a
roadway along a target trajectory associated with the road segment
consistent with the disclosed embodiments. Vehicle 7902 may receive
from image capture devices 122 and 124 at least one environmental
image of a turning roadway 7900 representative of a lane 7910
ending. This change in lane 7910 may be due to recent modifications
to a road, and thus may not be yet reflected in the sparse data
model 800.
[0955] In this example, the vehicle systems may recognize the
ending lane and override navigation according to the road model in
order to adjust a maneuver of the vehicle 7902 traveling along path
8200 in lane 7910. For example, processing unit 110, using one or
more images captured with cameras aboard the vehicle may recognize
a blockage in the path along the target trajectory associated with
the road segment. Processing unit 110 may adjust steering of the
vehicle to leave a path indicated by the target trajectory in order
to avoid lane constraint 7924. As a result of the system generated
navigational adjustment, navigational adjustment condition
information (e.g., including the existence of an ending of lane
7910, any of distances c.sub.1, c.sub.2, d.sub.2, r, w.sub.1, and
w.sub.2 etc.) may be stored in memory (e.g., memory 140) and/or
sent to a server (e.g. server 123) for possible update of the road
model. In some embodiments, in addition or alternatively, the
navigational adjustment condition information may include a
location of vehicle 7902 based on data determined by position
sensor 130 and/or a position of vehicle 7902 relative to one or
more recognized landmarks.
[0956] The server side may analyze the received information (e.g.,
using automated image analysis processes) to determine whether any
updates to sparse data model 800 are warranted based on the
detected system intervention. In some embodiments, the server may
or may not update the road model based on the received navigational
adjustment condition information. For example, given the permanent
nature of an ending lane accompanied by a lane shift, the server
may decide it is necessary to change or update the road model.
Accordingly, the sever may modify the road model in order to steer
or turn to merge at these distances c.sub.1, c.sub.2, d.sub.2,
w.sub.1, and w.sub.2 upon approaching lane constraint 7924. The
model may also be updated based on a received, reconstructed and
actual trajectory taken by vehicle 7902 as it navigated past the
ending lane. Additionally, rather than aggregating the actual path
of vehicle 7902 with other trajectories stored in sparse data model
800 for the particular road segment (e.g., by averaging the path of
vehicle 7902 with other trajectories stored in sparse data model
800), the target trajectory may be defaulted to the path of vehicle
7902. That is, because the server may determine that the cause of
the navigational change was a non-transient (or semi-permanent)
condition, the path of vehicle 7902 may be more accurate for the
particular road segment than other trajectories for the road
segment collected before the condition existed. The same approach
and analysis could also be employed by the server upon receiving
navigational modifications based not on control by the self-aware
vehicle system, but on user intervention (described above).
[0957] FIG. 82C illustrates a plan view of a vehicle traveling on a
roadway approaching a pedestrian consistent with disclosed
embodiments. In the example of FIG. 82C, vehicle 7902 is driving in
lane 7910 of roadway 7900 with pedestrian 7926. As shown,
pedestrian 7926 may be positioned directly in roadway 7900 or
alternatively may be positioned to the side of roadway 7900.
Vehicle 7902 may travel in lane 7910 according to instructions
derived based on the road model (e.g., a heading direction along a
target trajectory) and may approach pedestrian 7926. Vehicle 7902
may receive from image capture devices 122 and 124 at least one
environmental image including an image of pedestrian 7926. Where
the system intervenes to override the road model to adjust a
maneuver of the vehicle 7902 traveling in lane 7910 to avoid
pedestrian 7926, navigational adjustment condition information
including, for example, a distance d.sub.1 to a stop sign and/or a
capture image depicting pedestrian 7926 may be captured and stored
in memory (e.g., memory 140) and/or sent to a server (e.g., server
1230) for making a possible update to the road model. The server
side may analyze the received information (e.g., using automated
image analysis processes) to determine whether any updates to
sparse data model 800 are warranted based on the detected system
intervention. In this example, given the temporary nature of a
pedestrian, the server may determine to not change or update the
road model.
[0958] Optionally, in some embodiments, when the cause of the
intervention is not confidently ascertained by the system, or when
the nature of the cause in not clear or is inherently not constant
or stable, the server may issue an alert and/or provide one, two,
or more alternative paths or road models. In such an embodiment,
the server may cause the system onboard the vehicle to examine the
situation on the ground including when the vehicle arrived at the
point or area where the deviation or intervention occurred. The
server may further provide a location of a suspected and/or
verified cause of the intervention, to allow the system to focus on
that area. As such, the system may have more time and more
information to evaluate the situation.
[0959] FIG. 82D illustrates a plan view of a vehicle traveling on a
roadway approaching an area of construction consistent with the
disclosed embodiments. As shown, vehicle 7902 is traveling a target
trajectory associated with the road segment (e.g., according to a
three-dimensional spline representative of a predetermined target
trajectory 8200) along a roadway 7900 where a construction area
8200d is located directly in front of vehicle 7902. Vehicle 7902
may receive from image capture devices 122 and 124 at least one
environmental image including an image of construction area 8200d.
Where the system intervenes to override one or more navigational
maneuvers generated based on the road model in order to avoid
construction area 8200d, navigational adjustment condition
information may be stored. Such information may include, for
example, the existence of a construction area 8200d (e.g., as
depicted in one or more captured images). The navigational
adjustment condition information may also be sent to a server (e.g.
server 120) for a making one or more possible updates to sparse
data model 800. In some embodiments, the navigational adjustment
condition information may include a location of vehicle 7902 based
on, for example, a position sensor 130 and/or a location of a known
landmark relative to vehicle 7902 at the time of adjustment. The
server side may analyze the received information (e.g., using
automated image analysis processes) to determine whether any
updates to sparse data model 800 are warranted based on the
detected system intervention. In this example, due to the
non-transient nature of the roadway construction (where
non-transient may refer to a condition likely to exist longer than
a predetermined period of time, including, for example, several
hours, a day, a week, a month, or more), the server may determine
to change or update the road model.
[0960] FIG. 83 illustrates an example flowchart representing a
method for model adaptation based on self-aware navigation of a
vehicle consistent with the disclosed embodiments. In particular,
FIG. 83 illustrates a process 8300 that may be performed by
processing unit 110 of system 100. As discussed below, process 8300
may use a road model defining a predetermined vehicle trajectory
8200. Where a maneuver deviates from navigational maneuvers
developed based on the predetermined model vehicle trajectory 8200,
the model and information regarding a navigational adjustment
condition may be captured and stored and/or sent to a server (e.g.,
server 1230) for making a possible update to the road model.
[0961] At step 8310, processing unit 110 may determine a
navigational maneuver based on a comparison of a vehicle position
with respect to a predetermined model associated with a road
segment. As discussed elsewhere in detail, a recognized landmark
may be verified in the captured image and used to determine a
position of the vehicle along a target trajectory for a particular
road segment. Based on the determined position, the processing unit
110 may cause one or more navigational responses, for example, a
navigational maneuver to maintain the vehicle (e.g., steer the
vehicle) along the target trajectory.
[0962] At step 8312, processing unit 110 may receive an
environmental image of an area forward of vehicle 7902. For
example, processing unit 110 may receive an image of an environment
of vehicle 7902 that includes a parked vehicle, a lane constraint
having a road curvature or turning roadway radius r providing
information indicative, for example, of a roadway lane ending, a
pedestrian and/or construction area.
[0963] At step 8314, processing unit 110 may determine an existence
of a navigational adjustment condition. The navigational adjustment
condition may be determined responsive to an analysis of at least
one environmental image of an area forward of vehicle 7902 and may
include, for example, a parked car in front of vehicle 7902a, a
roadway curvature having turn radius r providing information
indicative, for example, of construction area in lane 7910. These
are, of course, examples, and the captured images may include any
of a multitude of conditions within an environment of the vehicle
that may warrant an adjustment in navigation away from a target
trajectory included in sparse data model 800.
[0964] At step 8316, processing unit 110 may cause vehicle 7902 to
adjust the navigational maneuver based on the navigational
adjustment condition. For example, processing unit 110 may cause
vehicle 7902 to change heading directions away from a direction of
the target trajectory in order to avoid a parked car, a road
construction site, a pedestrian, etc. Consistent with the disclosed
embodiments, instructions may be sent to a throttling system 220,
braking system 230, and/or steering system 240 in order to cause
the adjustment to one or more navigational maneuvers generated
based on sparse data model 800.
[0965] At step 8318, processing unit 110 may store information
relating to the navigational adjustment condition information into
memory 140 or 150 of system 100. Such information may include one
or more images captured of the environment of the vehicle at the
time of the navigational adjustment that resulted in a departure
from the target trajectory of sparse data model 800. The
information may also include a position of the vehicle, outputs of
one or more sensors associated with the vehicle, etc.
[0966] At step 8320, processing unit 110 may transmit the
navigational adjustment condition information to a road model
management system (e.g., server 1230) for analysis and for
potentially updating a predetermined model representative of the
roadway.
[0967] Adaptive Road Model Manager
[0968] In some embodiments, the disclosed systems and methods may
provide an adaptive road model manager. The adaptive road model
manager may be provided by a server (e.g., server 1230), which may
receive data from vehicles and decide whether or not to make an
update to the road model if an adjustment from an expected
vehicular navigational maneuver was not due to a transient
condition. The vehicles may send data to the server regarding
navigational departures from the road model using a wireless data
connection over one or more networks (e.g., including over a
cellular network and/or the Internet). For example, the server may
receive from each of a plurality of autonomous vehicles
navigational situation information associated with an occurrence of
an adjustment to a determined navigational maneuver. The server may
analyze the navigational situation information and determine, based
on the analysis of the navigational situation information, whether
the adjustment to the determined navigational maneuver was due to a
transient condition. In some embodiments, the server may detect the
navigational maneuver from raw data provided by the vehicle (e.g.,
by processing image data). The server may update the predetermined
model representative of the at least one road segment if the
adjustment to the determined navigational maneuver was not due to a
transient condition. As discussed herein, a transient condition is
any condition expected to change after a predetermined time period
(e.g., less than a few hours, a day, or a week or more) such that
an update to a road model is not warranted or desirable. Such
transient conditions may be expected to no longer be present after
the predetermined time period and therefore the server may
determine to not change or update to the road model. Conversely, if
the server determines the adjustment was not due to a transient
condition, the server may determine to update the road model.
[0969] In some embodiments, when a navigational maneuver is
detected, the server may mark the respective area of the road model
as being associated with a suspected change. The server may then
determine from further updates from the same location or a nearby
location (e.g., in some embodiments, "pulling" such updates from
vehicles at the location or nearby the location), and may process
the data in an attempt to verify the change. When the change is
verified, the server may update the model, and may subsequently
communicate the updated model of the respective area, replacing the
former version of the model. The server may implement a confidence
level such that the update occurs when the confidence level is
above a certain level. The confidence level may be associated with
the type of maneuver, the similarity between two or more maneuvers,
identification of a source of the adjustment, a frequency of
consistent updates, and the number of ratio of inconsistent
updates, environmental conditions, such as weather, urban vs. rural
environments, etc. The severity of the cause of the maneuver may
also be taken into account when determining the confidence level.
If the maneuver is severe (e.g., a sharp turn) and the cause may be
associated with a potential weather situation and, in some
embodiments, a less restrictive approval process can may be
used.
[0970] FIG. 84A illustrates a plan view of a vehicle traveling on a
roadway with multiple parked cars consistent with the disclosed
embodiments. As shown, vehicle 7902a is traveling according to a
target trajectory (e.g., a three-dimensional spline representative
of a predetermined path of travel 8400) of a road model along
roadway 7900 where another vehicle 7902c is parked directly in
front of vehicle 7902a. Roadway 7900 may be subdivided into lanes,
such as lanes 7910 and 7920. Where either the system or user
intervenes to override a navigational maneuver generated based on
the road model and adjust a maneuver of the vehicle 7902 traveling
along path 8400 in lane 7910 to avoid parked vehicles 7902c,
navigational situation information including, for example, the
existence of parked cars 7902c in lane 7910 (e.g., as depicted in
one or more images captured by an image captured device of vehicle
7902a) may be may be sent to a server (e.g., server 1230) for
analysis.
[0971] The server side may analyze the received information (e.g.,
using automated image analysis processes) to determine whether any
updates to sparse data model 800 are warranted based on whether or
not the adjustment was due to a transient condition. Where the
adjustment was not due to the existence of a transient condition,
the road model may be updated. For example, where an experienced
condition is determined to be one likely to persist beyond a
predetermined time threshold (e.g., a few hours, a day, or a week
or more) updates may be made to the model. In some embodiments, the
threshold for determining a transient condition may be dependent on
a geographic region in which the condition is determined to occur,
on an average number of vehicles that travel the road segment in
which the condition was encountered, or any other suitable
criteria. For example, in geographic regions, such as rural
regions, that include fewer vehicles likely to encounter a
road-related condition, a time threshold for making the transient
or not transient determination may be longer that another
geographic region (e.g., an urban environment) that includes more
vehicles likely to encounter the road-related condition over a
particular time period. That is, as the average number of vehicles
traveling a road segment increases, the time threshold for making
the transient determination may be lower. Such an approach may
reduce the number of cars traveling in an urban environment that
will need to rely upon their internal systems (camera, sensors,
processor, etc.) to recognize a road condition that warrants a
navigational response different from one expected based on sparse
model 800. At the same time, a longer transient time threshold in
lower trafficked areas may reduce the likelihood that the model is
changed to account for an experienced road condition and, a short
time later (e.g., within hours, a day, etc.) needs to be changed
back to its original state, for example, after the experience road
condition no longer exists.
[0972] Conversely, where a navigational adjustment is determined to
be in response to a transient condition, the server may elect to
not make any updates to the road model. For example, where either
the system or user intervenes to navigate the vehicle 7902a into
lane 7920 to avoid vehicles 7902c parked on the shoulder yet
abutting into lane 7910 (FIG. 84A), where with the system or user
navigates the host vehicle to avoid an intervening car 7902d (FIG.
84B), wherein the system or user navigates the host vehicle to
avoid a temporary barrier 8402 (such as a fallen tree, as shown in
FIG. 84C), or where the system or user navigates the host vehicle
to avoid markers 8200d designating temporary roadwork (FIG. 84D),
where the system or user navigates the host vehicle to avoid a
pothole 8502 present in the roadway (FIG. 85A), where the system or
user navigates the host vehicle to avoid a pedestrian 8504 or
pedestrian in the roadway (FIG. 85B), the server may determine in
each case that the experienced condition constitutes a transient
condition not warranting an update to sparse data model 800.
[0973] In some cases, and as described above, certain road
conditions may be classified as transient based on a determination
of a probable time of their existence (less than a few hours, a
day, a week, etc.). In other cases, a determination of whether a
certain road condition is a transient one may be based on factors
other than or in addition to time. For example, in the case of a
pothole captured in one or more images, the server (or the
processing unit associated with a host vehicle) may determine a
depth of the pothole, which may aid in determining whether the
pothole represents a transient condition and, therefore, whether
sparse data model 800 should be updated in view of the pothole. If
the pothole 8502 is determined to have a depth that could result in
potential damage to the host vehicle if driven through (e.g., a
depth on the order of greater than 3 cm, 5 cm, 10 cm or more), then
the pothole may be categorized as non-transient. Similarly, if the
pothole 8502 is located in a geographic region in which road repair
is known to be somewhat slow (e.g., requiring more than a day to
repair, a week to repair, or longer), then a pothole may be
categorized as non-transient.
[0974] Determination of whether a particular road condition
constitutes a transient condition may be fully automated and
performed by one or more server-based systems. For example, in some
embodiments, the one or more server based systems may employ
automated image analysis techniques based on one or more images
captured by cameras onboard a host vehicle. In some embodiments,
the image analysis techniques may include machine learning systems
trained to recognize certain shapes, road features, and/or objects.
For example, the server may be trained to recognized in an image or
image stream the presence of a concrete barrier (possibly
indicating the presence of a non-transient construction or lane
separation condition), a pothole in the surface of the road (a
possible transient or non-transient condition depending on the
size, depth, etc.), a road edge intersecting with an expected path
of travel (potentially indicating a non-transient lane shift or new
traffic pattern), a parked car (a potentially transient condition),
an animal shape in the road (a potentially transient condition), or
any other relevant shapes, objects, or road features.
[0975] The image analysis techniques employed by the server may
also include a text recognition component to determine a meaning
associated with text present in an image. For example, where text
appears in one or more uploaded images from an environment of a
host vehicle, the server may determine whether text exists in the
images. If text exists, the server may use techniques such as
optical character recognition to assist in determining whether the
text may relate to a reason that a system or user of a host vehicle
caused a navigational maneuver differing from that expected based
on sparse model 800. For example, where a sign is identified in an
image, and the sign is determined to include the text "NEW TRAFFIC
PATTERN AHEAD," the text may assist the server in determining that
the experienced condition had a non-transient nature. Similarly,
signs such as "ROAD CLOSED AHEAD" or "BRIDGE OUT" may also help
indicate the presence of a non-transient condition for which an
update to sparse road model 800 may be justified.
[0976] The server-based system may also be configured to take into
account other information when determining whether an experienced
road condition is transient. For example, the server may determine
an average number of vehicles that travel a road segment over a
particular amount of time. Such information may be helpful in
determining the number of vehicles a temporary condition is likely
to affect over an amount of time that the condition is expected to
persist. Higher numbers of vehicles impacted by the condition may
suggest a determination that the sparse data model 800 should be
updated.
[0977] In some embodiments, determination of whether a particular
road condition constitutes a transient condition may include at
least some level of human assistance. For example, in addition to
the automated features described above, a human operator may also
be involved in reviewing information uploaded from one or more
vehicles and/or determining whether sparse data model 800 should be
updated in view of the received information.
[0978] FIG. 86 illustrates an example flowchart representing a
method for an adaptive road model manager consistent with disclosed
embodiments. In particular, FIG. 86 illustrates a process 8600 for
an adaptive road model manager consistent with disclosed
embodiments. Steps of process 8600 may be performed by a server
(e.g., server 1230), which may receive data from a plurality of
autonomous vehicles over one or more networks (e.g., cellular
and/or the Internet, etc.).
[0979] At step 8610, the server may receive from each of a
plurality of autonomous vehicles navigational situation information
associated with an occurrence of an adjustment to a determined
navigational maneuver. The navigational situation information may
result from system or user invention overriding the road model. The
navigational situation information may include at least one image
or a video representing an environment of vehicle 7902. In some
embodiments, the navigational situation information may further
include a location of vehicle 7902 (e.g., as determined by position
sensor 130 and/or based on a distance of vehicle 7902 to a
recognized landmark).
[0980] At step 8612, the server may analyze the navigational
situation information. For example, the server side may analyze the
received information (e.g., using automated image analysis
processes) to determine what is depicted in the at least one image
or video representing an environment of vehicle 7902. This analysis
may include identification of the existence of, for example, a
parked car, an intervening car, a temporary barrier, such as a
fallen tree directly in front of a vehicle, roadwork, a low light
condition, a glare condition, a pothole, an animal, or a
pedestrian.
[0981] At step 8614, the server may determine, based on the
analysis of the navigational situation information, whether the
adjustment to the determined maneuver was due to a transient
condition. For example, A transient condition may include where a
second vehicle is parked directly in front of a vehicle, a vehicle
intervenes directly in front of vehicle, barrier, such as a fallen
tree lies directly in front of a vehicle, a low light condition, a
glare condition, a pothole (e.g., one of a minimal depth), an
animal, or a pedestrian.
[0982] At step 8616, process 8600 may include the server updating
the predetermined model representative of the at least one road
segment if the adjustment to the determined navigational maneuver
was not due to a transient condition. For example, a condition that
may be non-transient may include a substantial pothole, long-term
and/or extensive roadwork, etc. This update may include an update
to the three-dimensional spline representing a predetermined path
of travel along at least one road segment.
[0983] Road Model Management Based on Selective Feedback
[0984] In some embodiments, the disclosed systems and methods may
manage a road model based on selective feedback received from one
or more vehicles. As discussed in earlier sections, the road model
may include a target trajectory (e.g., a three-dimensional spline
representing a predetermined path of travel along a road segment).
Consistent with disclosed embodiments, a server (e.g., server 1230)
may selectively receive road environment information from
autonomous vehicles in order to update the road model. As used
herein, road environment information may include any information
related to an observable or measurable condition associated with a
road or a road segment. The server may selectively receive the road
environment information based on a variety of criteria. Relative to
the disclosed embodiments, selectively receiving information may
refer to any ability of a server based system to limit data
transmissions sent from one or more autonomous vehicles to the
server. Such limitations placed on data transmissions from the one
or more autonomous vehicles may be made based any suitable
criteria.
[0985] For example, in some embodiments, the server may limit a
frequency at which road environment information is uploaded to the
server from a particular vehicle, from a group of vehicles, and/or
from vehicles traveling within a particular geographic region. Such
limitations may be placed based on a determined model confidence
level associated with a particular geographic region. In some
embodiments, the server may limit data transmissions from
autonomous vehicles to only those transmissions including
information suggesting a potential discrepancy with respect to at
least one aspect of the road model (such information, for example,
may be determined as prompting one or more updates to the model).
The server may determine whether one or more updates to the road
model are required based on the road environment information
selectively received from the autonomous vehicles and may update
the road model to include the one or more updates. Examples of a
server selectively receiving road environment information from
autonomous vehicles are discussed below.
[0986] FIG. 87A illustrates a plan view of a vehicle traveling on
an interstate roadway consistent with the disclosed embodiments. As
shown, vehicle 7902 is traveling along a predetermined path of
travel 8700 (e.g., a target trajectory according to a road model)
associated with interstate roadway 7900. As shown, roadway 7900 may
be subdivided into lanes, such as lanes 7910 and 7920. The server
may selectively receive road environment information based on
navigation by vehicle 7902 through a road environment, such as
roadway 7900. For example, the road environment information may
include one or more images captured by an image capture device of
vehicle 7902; location information representing a position of
vehicle 7902 determined by, for example, using position sensor 130
and/or based on a position of vehicle 7902 relative to a recognized
landmark; outputs from one or more sensors associate with vehicle
7902, etc. Based upon the road environment information, the server
may determine whether updates to the road model are required.
[0987] In the example shown in FIG. 87A, a single particular
vehicle 7902 is shown traveling along an interstate roadway 7900
and following a target trajectory 8700. FIG. 87B illustrates a plan
view of a group of vehicles 7902e, 7902f, 7902g, and 7902h
traveling along a city roadway 7900 and following target
trajectories 8700a and 8700b that may be associated with lanes 7910
and 7920 of roadway 7900, for example. FIG. 87C illustrates a plan
view of a vehicle 7902i traveling within a rural geographic region
8722 on roadway 7900. FIG. 87D illustrates a vehicle 7902 traveling
on a roadway 7900 including a newly modified traffic pattern. For
example, where once lane 7910 may have extended forward of vehicle
7902, a new traffic pattern may exist where lane 7910 now comes to
an end forward of vehicle 7902.
[0988] Information relating to the navigation of vehicle 7902 in
any of these situations, among others, may be collected and
uploaded to one or more server based systems that maintain sparse
data map 800. Based on the received information, the server may
analyze whether one or more updates are needed to sparse data map
800 and, if an update is determined to be justified, then the
server may make the update to sparse data map 800. In some
embodiments, the analysis and updating may be performed
automatically by the server via automated image analysis of images
captured by cameras aboard vehicle 7910, automated review of sensor
and position information, automated cross-correlation of
information received from multiple autonomous vehicles, etc. In
some embodiments, an operator associated with the server-based
system may assist in review of the information received from the
autonomous vehicles and determination of whether updates to sparse
data model 800 are needed based on the received information.
[0989] In some embodiments, the server may be configured to receive
navigational information from all available autonomous vehicles.
Further, this information may be uploaded to the server based on a
predetermined protocol. For example, the information may be
uploaded across a streaming data feed. Additionally or
alternatively, the information may be uploaded to the server at a
predetermined periodic rate (e.g., several times per second, once
per second, once per minute, once every several minutes, once per
hour, or any other suitable time interval). The information may
also be uploaded to the server based on aspects of the vehicle's
navigation. For example, navigational information may be uploaded
from a vehicle to the server as the vehicle moves from one road
segment to another or as the vehicle moves from one local map
associated with sparse data map 800 to another.
[0990] In some embodiments, the server may be configured to
selectively control the receipt of navigational information from
one or more autonomous vehicle. That is, rather than receiving all
available navigational information from all available autonomous
vehicles, the server may restrict the amount of information it
receives from one or more available autonomous vehicles. In this
way, the server may reduce the amount of bandwidth needed for
communicating with available autonomous vehicles. Such selective
control of information flow from the autonomous vehicles and the
server may also reduce an amount of processing resources required
to process the communications incoming from the autonomous
vehicles.
[0991] The selective control of information flow between the
autonomous vehicles and the server may be based on any suitable
criteria. In some embodiments, the selectivity may be based on the
type of road that a vehicle is traversing. With reference to the
example shown in FIG. 87A, vehicle 7902 is traversing an
interstate, which may be a well-traveled road. In such situations,
the server may have accumulated a significant amount of
navigational information relating to the interstate road, its
various lanes, the landmarks associated with the road, etc. In such
circumstances, continuing to receive full information uploads from
every vehicle that travels along the interstate roadway may not
contribute to significant or further refinements of the road model
represented in sparse data map 800. Therefore, the server may
limit, or an autonomous vehicle traveling along a certain type of
road or a particular road segment may limit, the amount or type of
information uploaded to the server.
[0992] In some embodiments, the server may forego automatic
information uploads altogether from vehicles traveling along a
particular interstate roadway, a heavily traveled urban road, or
any other road where sparse data model 800 is determined to require
no additional refinements. Instead, in some embodiments, the server
may selectively acquire data from vehicles traveling along such
roads as a means for periodically confirming that sparse data map
800 remains valid along selected roadways. For example, the server
may interrogate one or more vehicles determined to be traveling
along an interstate, heavily traveled road segment, etc. to collect
navigational information from the interrogated vehicle. This
information may include information relating to a reconstructed
trajectory of the vehicle along the roadway, a position of the
vehicle on the roadway, sensor information from the vehicle,
captured images from cameras onboard the vehicle, etc. Using this
technique, the server may periodically monitor the state of a
roadway and determine whether updates are needed to sparse data
model 800 without unnecessary usage of data transmission and/or
data processing resources.
[0993] In some embodiments, the server may also selectively control
data flow from an autonomous vehicle based on the number of cars
determined to be traveling within a group along a roadway. For
example, where a group of autonomous vehicles (e.g., two or more
vehicles) is determined to be traveling within a certain proximity
of one another (e.g., within 100 meters, 1 km, or any other
suitable proximity envelope), information upload may be restricted
from any of the members of the group. For example, the server may
restrict information transfer to only one member of the group, any
subset of members of the group, one member of the group from each
lane of the road, etc.
[0994] In some embodiments, the server may also selectively control
data flow from an autonomous vehicle based on a geographic region.
For example, some geographic regions may include road segments for
which sparse data model 800 already includes refined target
trajectories, landmark representations, landmark positions, etc.
For example, in certain geographic regions (e.g., urban
environments, heavily traveled roadways, etc.), sparse data model
800 may be generated based upon multiple traversals of various road
segments by vehicles in a data collection mode. Each traversal may
result in additional data relevant to road segments in a geographic
region from which sparse data model 800 may be refined. In some
cases, sparse data map 800 for certain geographic regions may be
based upon 100, 1000, 10000 or more prior traversals of various
road segments. In those regions, additional information received
from one or more autonomous vehicles may not serve as a basis for
further, significant refinements of sparse data model. Thus, the
server may restrict uploads from vehicles traveling in certain
geographic regions. For example, in some cases, the server may
preclude all automatic transmissions of road data from vehicles
traveling in selected geographic regions. In other cases, the
server may enable transmission of data from only a portion of
vehicles traveling in a certain geographic region (e.g., 1 of 2
vehicles, 1 of 5, 1 of 100, etc.). In other cases, the server may
receive transmissions from only those vehicles in a geographic
location that the server identifies and queries for updated road
information. The server can use information received from any
portion of the vehicles from a certain geographic region to verify
and/or update any aspect of sparse data model 800.
[0995] In some embodiments, the server may also selectively control
data flow from an autonomous vehicle based on a confidence level
assigned to a particular local map, road segment, geographic
region, etc. For example, like the geographic region example,
certain road segments, local maps, and/or geographic regions may be
associated with a confidence level indicative of, for example, a
level of refinement of sparse data map 800 in those areas. The
server may restrict transmission of road information from vehicles
traveling on any roads, local map areas, or geographic regions
associated with a confidence level above a predetermined threshold.
For example, in some cases, the server may preclude all automatic
transmissions of road data from vehicles traveling in regions with
a confidence level above a predetermined threshold. In other cases,
the server may enable transmission of data from only a portion of
vehicles traveling in those regions (e.g., 1 of 2 vehicles, 1 of 5,
1 of 100, etc.). In other cases, the server may receive
transmissions from only those vehicles in a high-confidence area
(one including a confidence level above a predetermined threshold)
that the server identifies and queries for updated road
information. The server can use information received from any
portion of the vehicles from a high-confidence level region to
verify and/or update any aspect of sparse data model 800.
[0996] In some embodiments, the server may also selectively control
data flow from an autonomous vehicle based on the type of
information included within the navigational information to be
uploaded by a particular autonomous vehicle. For example, in many
cases, the road information uploaded to the server from various
host vehicles may not significantly impact sparse data model 800.
For example, in high-confidence level geographic areas or road
segments etc., additional road information from traversing vehicles
may be useful for verifying the continued accuracy of sparse data
model 800, but such information may not offer a potential for
additional significant refinements to sparse data model 800. Thus,
continued transmission of information that verifies sparse data
model 800, but does not offer a potential for significant further
refinement of sparse data model 800 may consume data transmission
and processing resources without a potential for significant
benefit.
[0997] In such cases, it may be desirable for the server to limit
data transmissions from vehicles. Instead of receiving data
transmissions automatically from all (or even a part of) available
vehicles, the server may restrict data transmissions from vehicles
to only those experiencing situations that may impact sparse road
model 800. For example, where a vehicle traversing a road segment
experiences a situation that requires a navigational response that
departs from one anticipated by the sparse data model 800 (e.g.,
where the vehicle must travel a path different from a target
trajectory for a road segment), then the processing unit 110 may
determine that such a departure has occurred and may relay that
information to the server. In response, the server may query the
vehicle for information relating to the navigational departure so
that the server can determine whether any updates are needed to
sparse data model 800. In other words, the server may elect to
receive road information from vehicles only where the information
suggests that a change may be needed to sparse data model 800.
[0998] FIG. 88 illustrates an example flowchart representing a
method for road model management based on selective feedback
consistent with the disclosed embodiments. Steps of process 8800
may be performed by ae server (e.g., server 1230). As discussed
below, process 8800 may involve selectively receiving feedback to
potentially update the road model based upon road environment
information from autonomous vehicles.
[0999] At step 8810, the server may selectively receive road
environment information based on navigation from a plurality of
autonomous vehicles through their respective road environments. For
example, the server may selectively apply a limitation on a
frequency of information transmissions received from a particular
vehicle, from a group of vehicles, from vehicles traveling within a
particular geographic region, or from vehicles based on a
determined model confidence level associated with a particular
geographic region. Further, in some embodiments, the server may
selectively limit data transmissions from vehicles only to those
transmissions that reflect a potential discrepancy with respect to
at least one aspect of a predetermined road model.
[1000] At step 8812, the server may determine whether one or more
updates to the road model are required based on the road
environment information. If the server determines that updates to
the road model are justified based on information selectively
received from one or more autonomous vehicles, those updates may be
made at step 8814.
[1001] The foregoing description has been presented for purposes of
illustration. It is not exhaustive and is not limited to the
precise forms or embodiments disclosed. Modifications and
adaptations will be apparent to those skilled in the art from
consideration of the specification and practice of the disclosed
embodiments. Additionally, although aspects of the disclosed
embodiments are described as being stored in memory, one skilled in
the art will appreciate that these aspects can also be stored on
other types of computer readable media, such as secondary storage
devices, for example, hard disks or CD ROM, or other forms of RAM
or ROM, USB media, DVD, Blu-ray, 4K Ultra HD Blu-ray, or other
optical drive media.
[1002] Computer programs based on the written description and
disclosed methods are within the skill of an experienced developer.
The various programs or program modules can be created using any of
the techniques known to one skilled in the art or can be designed
in connection with existing software. For example, program sections
or program modules can be designed in or by means of .Net
Framework, .Net Compact Framework (and related languages, such as
Visual Basic, C, etc.), Java, C++, Objective-C, HTML, HTML/AJAX
combinations, XML, or HTML with included Java applets.
[1003] Moreover, while illustrative embodiments have been described
herein, the scope of any and all embodiments having equivalent
elements, modifications, omissions, combinations (e.g., of aspects
across various embodiments), adaptations and/or alterations as
would be appreciated by those skilled in the art based on the
present disclosure. The limitations in the claims are to be
interpreted broadly based on the language employed in the claims
and not limited to examples described in the present specification
or during the prosecution of the application. The examples are to
be construed as non-exclusive. Furthermore, the steps of the
disclosed methods may be modified in any manner, including by
reordering steps and/or inserting or deleting steps. It is
intended, therefore, that the specification and examples be
considered as illustrative only, with a true scope and spirit being
indicated by the following claims and their full scope of
equivalents.
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