U.S. patent application number 17/186249 was filed with the patent office on 2021-07-29 for automated performance checks for autonomous vehicles.
The applicant listed for this patent is Waymo LLC. Invention is credited to Colin Braley, Volker Grabe.
Application Number | 20210229686 17/186249 |
Document ID | / |
Family ID | 1000005523137 |
Filed Date | 2021-07-29 |
United States Patent
Application |
20210229686 |
Kind Code |
A1 |
Braley; Colin ; et
al. |
July 29, 2021 |
Automated Performance Checks For Autonomous Vehicles
Abstract
Aspects of the disclosure provides for a method for performing
checks for a vehicle. In this regard, a plurality of performance
checks may be identified including a first check for a detection
system of a plurality of detection systems of the vehicle and a
second check for map data. A test route for the vehicle may be
determined based on a location of the vehicle and the plurality of
performance checks. The vehicle may be controlled along the test
route in an autonomous driving mode, while sensor data may be
received from the plurality of detection systems of the vehicle. An
operation mode may be selected based on results of the plurality of
performance checks, and the vehicle may be operated in the selected
operation mode.
Inventors: |
Braley; Colin; (Mountain
View, CA) ; Grabe; Volker; (Redwood City,
CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Waymo LLC |
Mountain View |
CA |
US |
|
|
Family ID: |
1000005523137 |
Appl. No.: |
17/186249 |
Filed: |
February 26, 2021 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
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16219386 |
Dec 13, 2018 |
10960894 |
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17186249 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
B60W 50/0205 20130101;
B60W 2050/0215 20130101; G05D 1/0077 20130101; B60W 2050/0297
20130101; B60W 50/029 20130101; G05D 1/0061 20130101 |
International
Class: |
B60W 50/029 20060101
B60W050/029; G05D 1/00 20060101 G05D001/00; B60W 50/02 20060101
B60W050/02 |
Claims
1. A system in a vehicle, the system comprising: memory configured
to store map data; a plurality of sensors configured to detect
traffic features and objects in an environment of the vehicle and
generate sensor data; and a computing device including one or more
processors configured to perform a plurality of performance checks
on the vehicle, the plurality of performance checks including a map
check on the map data stored in the memory, the one or more
processors being further configured to: receive sensor data from at
least one of the plurality of sensors, the received sensor data
being associated with a stationary object or a traffic feature;
determine that the stationary object or the traffic feature is
included in the map data stored in the memory; perform the map
check by comparing the received sensor data to the map data stored
in the memory; select an operation mode from a plurality of
operation modes for the vehicle based on a result of the map check;
and operate the vehicle in the selected operation mode.
2. The system of claim 1, wherein the one or more processors are
further configured to: detect a difference between the map data and
the received sensor data; and determine whether the difference was
due to an error in the map data.
3. The system of claim 2, wherein the one or more processors
determine whether the difference was due to an error in the map
data by determining which particular sensor of the plurality of
sensors generated the sensor data, and then determining whether
sensor data collected by a different one of the plurality of
sensors should be compared to the map data.
4. The system of claim 1, wherein the one or more processors are
further configured to: determine whether differences exist in the
comparison of the received sensor data to the map data; and
determine that the map check is passed when a threshold number or
percentage of traffic features or objects of the map data have
locations matching locations of traffic features or objects
detected by the plurality of sensors.
5. The system of claim 1, wherein the stationary object is traffic
sign.
6. The system of claim 1, wherein the sensor data generated by the
plurality of sensors indicates at least one of location or
orientation of the stationary object.
7. The system of claim 1, wherein the traffic features and objects
include a traffic light.
8. The system of claim 1, wherein the traffic features and objects
include a stop sign.
9. The system of claim 1, wherein the traffic features and objects
include a crosswalk.
10. The system of claim 1, the one or more processors are
configured to: select a plurality of road segments based on a
location of the vehicle and the plurality of performance checks,
wherein each of the plurality of road segments is selected for
performing one or more of the plurality of performance checks;
determine a test route for the vehicle by connecting the plurality
of road segments and by connecting the location of the vehicle to
one of the plurality of road segments; and control the vehicle
along the test route in an autonomous driving mode.
11. A method for performing a map check for a vehicle, the method
comprising: storing, by a memory, map data; detecting, by a
plurality of sensors, traffic features and objects in an
environment of the vehicle; generating, by the plurality of
sensors, sensor data; and performing, by one or more processors of
a computing device, a map check on the map data stored in the
memory by: receiving, by the one or more processors, sensor data
from at least one of the plurality of sensors, the received sensor
data being associated with a stationary object or a traffic
feature; determining, by the one or more processors, that the
stationary object or the traffic feature is included in the map
data stored in the memory; and performing, by the one or more
processors, the map check by comparing the received sensor data to
the map data stored in the memory, wherein the one or more
processors are configured to select an operation mode from a
plurality of operation modes for the vehicle based on a result of
the map check, and operate the vehicle in the selected operation
mode.
12. The method of claim 11, further comprising: detecting, by the
one or more processors, a difference between the map data and the
received sensor data; and determining, by the one or more
processors, whether the difference was due to an error in the map
data.
13. The method of claim 12, wherein determining whether the
difference was due to an error in the map data includes determining
which particular sensor of the plurality of sensors generated the
sensor data, and then determining whether sensor data collected by
a different one of the plurality of sensors should be compared to
the map data.
14. The method of claim 11, further comprising: determining, by the
one or more processors, whether differences exist in the comparison
of the received sensor data to the map data; and determining, by
the one or more processors, that the map check is passed when a
threshold number or percentage of traffic features or objects of
the map data have locations matching locations of traffic features
or objects detected by the plurality of sensors.
15. The method of claim 11, wherein the stationary object is
traffic sign.
16. The method of claim 11, wherein the sensor data generated by
the plurality of sensors indicates at least one of location or
orientation of the stationary object.
17. The method of claim 11, wherein the traffic features and
objects include a traffic light.
18. The method of claim 11, wherein the traffic features and
objects include a stop sign.
19. The method of claim 11, wherein the traffic features and
objects include a crosswalk.
20. The method of claim 11, further comprising: selecting, by the
one or more processors, a plurality of road segments based on a
location of the vehicle and a plurality of performance checks,
wherein each of the plurality of road segments is selected for
performing one or more of the plurality of performance checks;
determining, by the one or more processors, a test route for the
vehicle by connecting the plurality of road segments and by
connecting the location of the vehicle to one of the plurality of
road segments; and controlling, by the one or more processors, the
vehicle along the test route in an autonomous driving mode.
Description
CROSS REFERENCE TO RELATED APPLICATIONS
[0001] The present application is a continuation of U.S. patent
application Ser. No. 16/219,386, filed Dec. 13, 2018, the
disclosure of which is hereby incorporated herein by reference.
BACKGROUND
[0002] Autonomous vehicles, such as vehicles which do not require a
human driver when operating in an autonomous driving mode, may be
used to aid in the transport of passengers or items from one
location to another. An important component of an autonomous
vehicle is the perception system, which allows the vehicle to
perceive and interpret its surroundings using cameras, radar,
sensors, and other similar devices. The perception system executes
numerous tasks while the autonomous vehicle is in motion, which
ultimately leads to decisions, such as speeding up, slowing down,
stopping, turning, etc. The perception system may include a
plurality of detection systems, such as cameras, sensors, and
global positioning devices, which gathers and interprets images and
sensor data about its surrounding environment, e.g., parked cars,
trees, buildings, etc.
SUMMARY
[0003] Aspects of the disclosure provides for a system comprising
one or more computing devices configured to identify a plurality of
performance checks including a first check for a detection system
of a plurality of detection systems of the vehicle and a second
check for map data; select a plurality of road segments based on a
location of the vehicle and the plurality of performance checks,
wherein each of the plurality of road segments is selected for
performing one or more of the plurality of performance checks;
determine a test route for the vehicle by connecting the plurality
of road segments and by connecting the location of the vehicle to
one of the plurality of road segments; control the vehicle along
the test route in an autonomous driving mode; while controlling the
vehicle, receive sensor data from the plurality of detection
systems of the vehicle; perform the plurality of performance checks
based on the received sensor data; select an operation mode from a
plurality of operation modes for the vehicle based on results of
the plurality of performance checks; and operate the vehicle in the
selected operation mode.
[0004] The plurality of road segments may include a first road
segment, wherein one or more of the plurality of performance checks
may be performed using one or more traffic features or stationary
objects that are detectable along the first road segment. The
plurality of road segments may include a second road segment on
which a maneuver required for one or more of the plurality of
performance checks can be performed.
[0005] The first check may include comparing characteristics of a
detected traffic feature with previously detected characteristics
of the traffic feature. The second check may include comparing a
location of a detected traffic feature with a location of the
detected traffic feature in the map data.
[0006] The plurality of performance checks may further include a
third check for a component of the vehicle, the third check may
include comparing one or more measurements related to the component
of the vehicle with a threshold measurement.
[0007] The operation mode may be selected based on the results
satisfying a threshold number of the plurality of performance
checks. The operation mode may be selected based on the results
satisfying one or more set of performance checks of the plurality
of performance checks.
[0008] The one or more computing devices may be further configured
to determine one or more corrections to at least one of the
detection systems based on the results of the plurality of
performance checks. Operating in the selected operation mode may
include using the one or more corrections.
[0009] The one or more computing devices may be further configured
to update the map data based on the results of the plurality of
performance checks. Operating in the selected operation mode may
include using the updated map data.
[0010] The selected operation mode may be an inactive mode.
[0011] The system may further comprise the vehicle.
[0012] The disclosure further provides for identifying, by one or
more computing devices, a plurality of performance checks including
a first check for a detection system of a plurality of detection
systems of the vehicle and a second check for map data; selecting,
by the one or more computing devices, a plurality of road segments
based on a location of the vehicle and the plurality of performance
checks, wherein each of the plurality of road segments is selected
for performing one or more of the plurality of performance checks;
determining, by the one or more computing devices, a test route for
the vehicle by connecting the plurality of road segments and by
connecting the location of the vehicle to one of the plurality of
road segments; controlling, by the one or more computing devices,
the vehicle along the test route in an autonomous driving mode;
while controlling the vehicle, receiving, by the one or more
computing devices, sensor data from the plurality of detection
systems of the vehicle; performing, by the one or more computing
devices, the plurality of performance checks based on the received
sensor data; selecting, by the one or more computing devices, an
operation mode from a plurality of operation modes for the vehicle
based on results of the plurality of performance checks; and
operating, by the one or more computing devices, the vehicle in the
selected operation mode.
[0013] The plurality of road segments may include a first road
segment, wherein one or more of the plurality of performance checks
may be performed using one or more traffic features or stationary
objects that are detectable along the first road segment. The
plurality of road segments may include a second road segment on
which a maneuver required for one or more of the plurality of
performance checks can be performed.
[0014] The method may further comprise determining, by the one or
more computing devices, one or more corrections to at least one of
the detection systems based on the results of the plurality of
performance checks, wherein operating in the selected operation
mode may include using the one or more corrections. The method may
further comprise updating, by the one or more computing devices,
the map data based on the results of the plurality of performance
checks, wherein operating in the selected operation mode may
include using the updated map data.
[0015] The plurality of performance checks may be performed at a
regular interval.
BRIEF DESCRIPTION OF THE DRAWINGS
[0016] FIG. 1 is a functional diagram of an example vehicle in
accordance with aspects of the disclosure.
[0017] FIG. 2 is an example representation of map data in
accordance with aspects of the disclosure.
[0018] FIG. 3 is an example external view of a vehicle in
accordance with aspects of the disclosure.
[0019] FIG. 4 is an example pictorial diagram of a system in
accordance with aspects of the disclosure.
[0020] FIG. 5 is an example functional diagram of a system in
accordance with aspects of the disclosure.
[0021] FIG. 6 is an example situation in accordance with aspects of
the disclosure.
[0022] FIG. 7 shows examples of collected sensor data in accordance
with aspects of the disclosure.
[0023] FIG. 8 show examples of collected component data in
accordance with aspects of the disclosure.
[0024] FIG. 9 show another example situation in accordance with
aspects of the disclosure.
[0025] FIG. 10 is an example flow diagram in accordance with
aspects of the disclosure.
DETAILED DESCRIPTION
Overview
[0026] The technology relates to performance checks for a vehicle
to be performed after a full calibration, prior to operation, or at
regular intervals. Before operating a vehicle on the road, a human
driver may check various systems and components of the vehicle,
such as making sure that the mirrors are adjusted, that the GPS
system is functioning, and components such as steering wheel,
brake, and signal lights, are responsive. Likewise, various systems
of an autonomous vehicle also need to be checked before operation,
particularly when the vehicle is to be operated in an autonomous
mode, where a human driver may not be present to notice problems
with the vehicle's systems. For instance, even if the vehicle had
been fully calibrated in the past, a sensor in a perception system
of the vehicle might have been moved during previous operation such
as by another road user or a cleaner, or have been damaged by
environmental factors such as temperature, humidity, etc.
[0027] As such, a plurality of performance checks may be performed
on the vehicle including, for example, a sensor check, a map check,
and/or a component check. The sensor check may include determining
a level of function of a given sensor or detection system, such as
detection accuracy, detection resolution, field of view, etc. The
map check may include determining an accuracy of the map data in
relation to a given geographic area. The component check may
include determining a level of function of a given component, such
as tire pressure, tire alignment, etc. The results of the plurality
of performance checks may be used to determine what functions of
the vehicle are within set guidelines, such as for safety and
comfort. The results may also be used to designate or clear the
vehicle for particular modes of operation.
[0028] To perform the plurality of performance checks, one or more
computing devices may determine a test route based on the location
of the vehicle, the map data, and the plurality of performance
checks for the plurality of systems of the vehicle. The test route
need not include a designated depot or testing center, or be a
closed route.
[0029] The vehicle's computing devices may navigate the vehicle
along the test route using the one or more components and collect
data using the plurality of detection systems. Collecting the data
may include using a detection system of the plurality of detection
system to detect one or more traffic features or stationary objects
along the test route. In addition, collecting the data may include
detecting one or more measurements related to a component of the
vehicle.
[0030] During the test route or after the vehicle completes the
test route, the vehicle's computing devices may perform the
plurality of performance checks by analyzing collected data. For a
sensor check, characteristics of a detected traffic feature (such
as location, orientation, shape, color, reflectivity, etc.) may be
compared with previously detected or stored characteristics of the
traffic feature. For a map check, a location or orientation of a
detected traffic feature may be compared with the location or
orientation of a previously detected or stored traffic feature in
map data of the vehicle. For a component check, the one or more
measurements related to a component of the vehicle may be compared
with a threshold measurement.
[0031] Based on results from the plurality of performance checks,
such as based on which performance checks have been satisfied, the
vehicle's computing devices may select an operation mode for
operating the vehicle. Operation modes may include, for example,
task designations (passenger or non-passenger tasks), or limits on
speeds, distance, or geographic area. Operation modes may also
include an inactive mode, for example if the vehicle is not cleared
for any other mode. In some implementations, modes may be selected
for a plurality of vehicles by a remote system, such as a fleet
management system. The vehicle may then be operated by the
vehicle's computing devices in a particular mode based on the
plurality of performance checks.
[0032] The features described above may allow autonomous vehicles
to be quickly and properly prepared for operation. Quicker
preparation means vehicles may be sent to users in a more timely
fashion, even as demand fluctuates. As a result, users of
autonomous vehicles may be able to be picked up in a timely manner.
In addition, fewer resources, such as fuel, need be used in the
preparation of the autonomous vehicle for service, which may reduce
overall costs. The features also allow for management of an entire
fleet of autonomous vehicles designated for a plurality of modes
that may service users more efficiently and safely.
EXAMPLE SYSTEMS
[0033] As shown in FIG. 1, a vehicle 100 in accordance with one
aspect of the disclosure includes various components. While certain
aspects of the disclosure are particularly useful in connection
with specific types of vehicles, the vehicle may be any type of
vehicle including, but not limited to, cars, trucks, motorcycles,
busses, recreational vehicles, etc. The vehicle may have one or
more computing devices, such as computing device 110 containing one
or more processors 120, memory 130 and other components typically
present in general purpose computing devices.
[0034] The memory 130 stores information accessible by the one or
more processors 120, including instructions 132 and data 134 that
may be executed or otherwise used by the processor 120. The memory
130 may be of any type capable of storing information accessible by
the processor, including a computing device-readable medium, or
other medium that stores data that may be read with the aid of an
electronic device, such as a hard-drive, memory card, ROM, RAM, DVD
or other optical disks, as well as other write-capable and
read-only memories. Systems and methods may include different
combinations of the foregoing, whereby different portions of the
instructions and data are stored on different types of media.
[0035] The instructions 132 may be any set of instructions to be
executed directly (such as machine code) or indirectly (such as
scripts) by the processor. For example, the instructions may be
stored as computing device code on the computing device-readable
medium. In that regard, the terms "instructions" and "programs" may
be used interchangeably herein. The instructions may be stored in
object code format for direct processing by the processor, or in
any other computing device language including scripts or
collections of independent source code modules that are interpreted
on demand or compiled in advance. Functions, methods and routines
of the instructions are explained in more detail below.
[0036] The data 134 may be retrieved, stored or modified by
processor 120 in accordance with the instructions 132. As an
example, data 134 of memory 130 may store predefined scenarios. A
given scenario may identify a set of scenario requirements
including a type of object, a range of locations of the object
relative to the vehicle, as well as other factors such as whether
the autonomous vehicle is able to maneuver around the object,
whether the object is using a turn signal, the condition of a
traffic light relevant to the current location of the object,
whether the object is approaching a stop sign, etc. The
requirements may include discrete values, such as "right turn
signal is on" or "in a right turn only lane", or ranges of values
such as "having an heading that is oriented at an angle that is 30
to 60 degrees offset from a current path of vehicle 100." In some
examples, the predetermined scenarios may include similar
information for multiple objects.
[0037] The one or more processor 120 may be any conventional
processors, such as commercially available CPUs. Alternatively, the
one or more processors may be a dedicated device such as an ASIC or
other hardware-based processor. Although FIG. 1 functionally
illustrates the processor, memory, and other elements of computing
device 110 as being within the same block, it will be understood by
those of ordinary skill in the art that the processor, computing
device, or memory may actually include multiple processors,
computing devices, or memories that may or may not be stored within
the same physical housing. As an example, internal electronic
display 152 may be controlled by a dedicated computing device
having its own processor or central processing unit (CPU), memory,
etc. which may interface with the computing device 110 via a
high-bandwidth or other network connection. In some examples, this
computing device may be a user interface computing device which can
communicate with a user's client device. Similarly, the memory may
be a hard drive or other storage media located in a housing
different from that of computing device 110. Accordingly,
references to a processor or computing device will be understood to
include references to a collection of processors or computing
devices or memories that may or may not operate in parallel.
[0038] Computing device 110 may have all of the components normally
used in connection with a computing device such as the processor
and memory described above as well as a user input 150 (e.g., a
mouse, keyboard, touch screen and/or microphone) and various
electronic displays (e.g., a monitor having a screen or any other
electrical device that is operable to display information). In this
example, the vehicle includes an internal electronic display 152 as
well as one or more speakers 154 to provide information or audio
visual experiences. In this regard, internal electronic display 152
may be located within a cabin of vehicle 100 and may be used by
computing device 110 to provide information to passengers within
the vehicle 100. In addition to internal speakers, the one or more
speakers 154 may include external speakers that are arranged at
various locations on the vehicle in order to provide audible
notifications to objects external to the vehicle 100.
[0039] In one example, computing device 110 may be an autonomous
driving computing system incorporated into vehicle 100. The
autonomous driving computing system may capable of communicating
with various components of the vehicle. For example, computing
device 110 may be in communication with various systems of vehicle
100, such as deceleration system 160 (for controlling braking of
the vehicle), acceleration system 162 (for controlling acceleration
of the vehicle), steering system 164 (for controlling the
orientation of the wheels and direction of the vehicle), signaling
system 166 (for controlling turn signals), navigation system 168
(for navigating the vehicle to a location or around objects),
positioning system 170 (for determining the position of the
vehicle), perception system 172 (for detecting objects in the
vehicle's environment), and power system 174 (for example, a
battery and/or gas or diesel powered engine) in order to control
the movement, speed, etc. of vehicle 100 in accordance with the
instructions 132 of memory 130 in an autonomous driving mode which
does not require or need continuous or periodic input from a
passenger of the vehicle. Again, although these systems are shown
as external to computing device 110, in actuality, these systems
may also be incorporated into computing device 110, again as an
autonomous driving computing system for controlling vehicle
100.
[0040] The computing device 110 may control the direction and speed
of the vehicle by controlling various components. By way of
example, computing device 110 may navigate the vehicle to a
drop-off location completely autonomously using data from the map
data and navigation system 168. Computing devices 110 may use the
positioning system 170 to determine the vehicle's location and
perception system 172 to detect and respond to objects when needed
to reach the location safely. In order to do so, computing devices
110 may cause the vehicle to accelerate (e.g., by increasing fuel
or other energy provided to the engine by acceleration system 162),
decelerate (e.g., by decreasing the fuel supplied to the engine,
changing gears, and/or by applying brakes by deceleration system
160), change direction (e.g., by turning the front or rear wheels
of vehicle 100 by steering system 164), and signal such changes
(e.g., by lighting turn signals of signaling system 166). Thus, the
acceleration system 162 and deceleration system 160 may be a part
of a drivetrain that includes various components between an engine
of the vehicle and the wheels of the vehicle. Again, by controlling
these systems, computing devices 110 may also control the
drivetrain of the vehicle in order to maneuver the vehicle
autonomously.
[0041] As an example, computing device 110 may interact with
deceleration system 160 and acceleration system 162 in order to
control the speed of the vehicle. Similarly, steering system 164
may be used by computing device 110 in order to control the
direction of vehicle 100. For example, if vehicle 100 configured
for use on a road, such as a car or truck, the steering system may
include components to control the angle of wheels to turn the
vehicle. Signaling system 166 may be used by computing device 110
in order to signal the vehicle's intent to other drivers or
vehicles, for example, by lighting turn signals or brake lights
when needed.
[0042] Navigation system 168 may be used by computing device 110 in
order to determine and follow a route to a location. In this
regard, the navigation system 168 and/or data 134 may store map
data, e.g., highly detailed maps that computing devices 110 can use
to navigate or control the vehicle. As an example, these maps may
identify the shape and elevation of roadways, lane markers,
intersections, crosswalks, speed limits, traffic signal lights,
buildings, signs, real time or historical traffic information,
vegetation, or other such objects and information. The lane markers
may include features such as solid or broken double or single lane
lines, solid or broken lane lines, reflectors, etc. A given lane
may be associated with left and right lane lines or other lane
markers that define the boundary of the lane. Thus, most lanes may
be bounded by a left edge of one lane line and a right edge of
another lane line. As noted above, the map data may store known
traffic or congestion information and/or and transit schedules
(train, bus, etc.) from a particular pickup location at similar
times in the past. This information may even be updated in real
time by information received by the computing devices 110.
[0043] FIG. 2 is an example of map data 200. As shown, the map data
200 includes the shape, location, and other characteristics of road
210, road 220, road 230, road 240, and road 250. Map data 200 may
include lane markers or lane lines, such as lane line 211 for road
210. The lane lines may also define various lanes, for example lane
line 211 defines lanes 212, 214 of road 210. As alternative to lane
lines or markers, lanes may also be inferred by the width of a
road, such as for roads 220, 230, 240, 250. The map data 200 may
also include information that identifies the direction of traffic
and speed limits for each lane as well as information that allows
the computing devices 110 to determine whether the vehicle has the
right of way to complete a particular type of maneuver (i.e.
complete a turn, cross a lane of traffic or intersection,
etc.).
[0044] Map data 200 may also include relationship information
between roads 210, 220, 230, 240, and 250. For example, map data
200 may indicate that road 210 intersects road 220 at intersection
219, that road 220 intersects road 230 at intersection 229, that
roads 230, 240, and 250 intersect at intersection 239, and that
road 250 intersects road 210 at intersection 259.
[0045] Map data 200 may further include signs and markings on the
roads with various characteristics and different semantic meanings.
As shown, map data 200 includes traffic light 216 for road 210 and
pedestrian crossing 218 across road 210. Map data 200 also includes
stop sign 260. The map data 200 may additionally include other
features such as curbs, waterways, vegetation, etc.
[0046] In addition, map data 200 may include various buildings or
structures (such as points of interests) and the type of these
buildings or structures. As shown, map data 200 depicts building
270 on road 210. For example, map data 200 may include that the
type of the building 270 is an airport, train station, stadium,
school, church, hospital, apartment building, house, etc. In this
regard, the type of the building 270 may be collected from
administrative records, such as county records, or manually labeled
by a human operator after reviewing aerial images. Map data 200 may
include additional information on building 270, such as the
locations of entrances and/or exits.
[0047] Map data 200 may also store predetermined stopping areas,
such as a parking lot 280. In this regard, such areas may be
hand-selected by a human operator or learned by a computing device
over time. Map data 200 may include additional information about
the stopping areas, such as the location of entrance 282 and exit
284 of parking lot 280, and that entrance 282 connects to road 240,
while exit 284 connects to roads 230 and 250.
[0048] In some examples, map data 200 may further include zoning
information. For instance, the zoning information may be obtained
from administrative records, such as county records. As such,
information on the roads may include indication that it is within a
residential zone, a school zone, a commercial zone, etc.
[0049] The map data may further include location coordinates
(examples of which are shown in FIG. 7), such as GPS coordinates of
the roads 210, 220, 230, 240, and 250, intersections 219, 229, 239,
and 259, lane line 211, lanes 212 and 214, traffic light 216,
pedestrian crossing 218, stop sign 260, building 270 and its
entrance 272, parking lot 280 and its entrance 282 and exit
284.
[0050] Although the detailed map data is depicted herein as an
image-based map, the map data need not be entirely image based (for
example, raster). For example, the detailed map data may include
one or more roadgraphs or graph networks of information such as
roads, lanes, intersections, and the connections between these
features. Each feature may be stored as graph data and may be
associated with information such as a geographic location and
whether or not it is linked to other related features, for example,
a stop sign may be linked to a road and an intersection, etc. In
some examples, the associated data may include grid-based indices
of a roadgraph to allow for efficient lookup of certain roadgraph
features.
[0051] The perception system 172 also includes one or more
components for detecting objects external to the vehicle such as
other vehicles, obstacles in the roadway, traffic signals, signs,
trees, etc. For example, the perception system 172 may include one
or more LIDAR sensor(s) 180, camera sensor(s) 182, and RADAR
sensor(s) 184. The perception system 172 may include other sensors,
such as SONAR device(s), gyroscope(s), accelerometer(s), and/or any
other detection devices that record data which may be processed by
computing devices 110. The sensors of the perception system may
detect objects and their characteristics such as location,
orientation, size, shape, type (for instance, vehicle, pedestrian,
bicyclist, etc.), heading, and speed of movement, etc. The raw data
from the sensors and/or the aforementioned characteristics can be
quantified or arranged into a descriptive function, vector, and or
bounding box and sent for further processing to the computing
devices 110 periodically and continuously as it is generated by the
perception system 172. As discussed in further detail below,
computing devices 110 may use the positioning system 170 to
determine the vehicle's location and perception system 172 to
detect and respond to objects when needed to reach the location
safely.
[0052] For instance, FIG. 3 is an example external view of vehicle
100. In this example, roof-top housing 310 and dome housing 312 may
include a LIDAR sensor as well as various cameras and RADAR units.
In addition, housing 320 located at the front end of vehicle 100
and housings 330, 332 on the driver's and passenger's sides of the
vehicle may each store a LIDAR sensor. For example, housing 330 is
located in front of driver door 350. Vehicle 100 also includes
housings 340, 342 for RADAR units and/or cameras also located on
the roof of vehicle 100. Additional RADAR units and cameras (not
shown) may be located at the front and rear ends of vehicle 100
and/or on other positions along the roof or roof-top housing 310.
Vehicle 100 also includes many features of a typical passenger
vehicle such as doors 350, 352, wheels 360, 362, etc.
[0053] Once a nearby object is detected, computing devices 110
and/or perception system 172 may determine the object's type, for
example, a traffic cone, pedestrian, a vehicle (such as a passenger
car, truck, bus, etc.), bicycle, etc. Objects may be identified by
various models which may consider various characteristics of the
detected objects, such as the size of an object, the speed of the
object (bicycles do not tend to go faster than 40 miles per hour or
slower than 0.1 miles per hour), the heat coming from the bicycle
(bicycles tend to have rider that emit heat from their bodies),
etc. In addition, the object may be classified based on specific
attributes of the object, such as information contained on a
license plate, bumper sticker, or logos that appear on the
vehicle.
[0054] For instance, sensor data (examples of which are shown in
FIG. 7) collected by one or more sensors of the perception system
172 may be stored in data of computing device 110 of vehicle 100.
Referring to FIG. 2, vehicle 100 may have driven past stop sign 260
in the past, and have stored the detected values of stop sign 260
by LIDAR sensor(s) 180 in data 134 of memory 130. In this example,
the detected values may include, for example, that when vehicle 100
is at location [x_b, y_b] (which may for example correspond to
driving in road 230 towards intersection 239 and was 10m away from
reaching intersection 239), the stop sign 260 was detected to be at
location [x4, y4] and at a 30.degree. angle from a front of vehicle
100 (which may for example correspond to when vehicle 100 is 8 m
away from stop sign 260 on road 230 driving towards intersection
239). As described in detail below with respect to the example
methods, these stored sensor data may be used for performance
checks on the various systems of the vehicle 100. In other
examples, sensor data collected by the perception system of a
reference vehicle may be stored in computing device 110 of vehicle
100. In other examples, such sensor data may be stored remotely on
a server or a storage system.
[0055] Computing device 110 may further store threshold values
(some of which are shown in FIG. 8) for various components of
vehicle 100. For example, computing device 110 may store a
threshold minimum tire pressure for tires of vehicle 100. For
another example, computing device 110 may store threshold alignment
angles for tires of vehicle 10. For yet another example, computing
device 110 may store a threshold stopping distance at a particular
speed for a brake of vehicle 100.
[0056] The one or more computing devices 110 of vehicle 100 may
also receive or transfer information to and from other computing
devices, for instance using wireless network connections 156. The
wireless network connections may include, for instance,
BLUETOOTH.RTM., Bluetooth LE, LTE, cellular, near field
communications, etc. and various combinations of the foregoing.
FIGS. 4 and 5 are pictorial and functional diagrams, respectively,
of an example system 400 that includes a plurality of computing
devices 410, 420, 430, 440 and a storage system 450 connected via a
network 460. System 400 also includes vehicle 100, and vehicle 100A
which may be configured similarly to vehicle 100. Although only a
few vehicles and computing devices are depicted for simplicity, a
typical system may include significantly more.
[0057] As shown in FIG. 4, each of computing devices 410, 420, 430,
440 may include one or more processors, memory, data and
instructions. Such processors, memories, data and instructions may
be configured similarly to one or more processors 120, memory 130,
data 134, and instructions 132 of computing device 110.
[0058] The network 460, and intervening nodes, may include various
configurations and protocols including short range communication
protocols such as BLUETOOTH.RTM., Bluetooth LE, the Internet, World
Wide Web, intranets, virtual private networks, wide area networks,
local networks, private networks using communication protocols
proprietary to one or more companies, Ethernet, WiFi and HTTP, and
various combinations of the foregoing. Such communication may be
facilitated by any device capable of transmitting data to and from
other computing devices, such as modems and wireless
interfaces.
[0059] In one example, one or more computing devices 410 may
include a server having a plurality of computing devices, e.g., a
load balanced server farm, that exchange information with different
nodes of a network for the purpose of receiving, processing and
transmitting the data to and from other computing devices. For
instance, one or more computing devices 410 may include one or more
server computing devices that are capable of communicating with one
or more computing devices 110 of vehicle 100 or a similar computing
device of vehicle 100A as well as client computing devices 420,
430, 440 via the network 460. For example, vehicles 100 and 100A
may be a part of a fleet of vehicles that can be dispatched by
server computing devices to various locations. In this regard, the
vehicles of the fleet may periodically send the server computing
devices location information provided by the vehicle's respective
positioning systems and the one or more server computing devices
may track the locations of the vehicles.
[0060] As mentioned above, rather than saving sensor data detecting
various traffic features on computing device 110, such sensor data
may additionally or alternatively be stored on server computing
device 410. Likewise, threshold values for components of vehicle
100 may likewise be stored on server computing device 410.
[0061] In addition, server computing devices 410 may use network
460 to transmit and present information to a user, such as user
422, 432, 442 on a display, such as displays 424, 434, 444 of
computing devices 420, 430, 440. In this regard, computing devices
420, 430, 440 may be considered client computing devices.
[0062] As shown in FIG. 5, each client computing device 420, 430,
440 may be a personal computing device intended for use by a user
422, 432, 442, and have all of the components normally used in
connection with a personal computing device including a one or more
processors (e.g., a central processing unit (CPU)), memory (e.g.,
RAM and internal hard drives) storing data and instructions, a
display such as displays 424, 434, 444 (e.g., a monitor having a
screen, a touch-screen, a projector, a television, or other device
that is operable to display information), and user input devices
426, 436, 446 (e.g., a mouse, keyboard, touchscreen or microphone).
A user, such as user 422, 432, 442, may send information, such as
pickup or drop-off requests, to server computing devices 410, using
user input devices 426, 436, 446 of computing devices 420, 430,
440. The client computing devices may also include a camera for
recording video streams, speakers, a network interface device, and
all of the components used for connecting these elements to one
another.
[0063] Although the client computing devices 420, 430, and 440 may
each comprise a full-sized personal computing device, they may
alternatively comprise mobile computing devices capable of
wirelessly exchanging data with a server over a network such as the
Internet. By way of example only, client computing device 420 may
be a mobile phone or a device such as a wireless-enabled PDA, a
tablet PC, a wearable computing device or system, or a netbook that
is capable of obtaining information via the Internet or other
networks. In another example, client computing device 430 may be a
wearable computing system, shown as a wrist watch in FIG. 4. As an
example the user may input information using a small keyboard, a
keypad, microphone, using visual signals with a camera, or a touch
screen.
[0064] In some examples, client computing device 440 may be remote
operator work station used by an administrator to provide remote
operator services to users such as users 422 and 432. For example,
a remote operator 442 may use the remote operator work station 440
to communicate via a telephone call or audio connection with users
through their respective client computing devices and/or vehicles
100 or 100A in order to ensure the safe operation of vehicles 100
and 100A and the safety of the users as described in further detail
below. Although only a single remote operator work station 440 is
shown in FIGS. 4 and 5, any number of such work stations may be
included in a typical system.
[0065] Storage system 450 may store various types of information as
described in more detail below. This information may be retrieved
or otherwise accessed by a server computing device, such as one or
more server computing devices 410, in order to perform some or all
of the features described herein. For example, the information may
include user account information such as credentials (e.g., a
username and password as in the case of a traditional single-factor
authentication as well as other types of credentials typically used
in multi-factor authentications such as random identifiers,
biometrics, etc.) that can be used to identify a user to the one or
more server computing devices. The user account information may
also include personal information such as the user's name, contact
information, identifying information of the user's client computing
device (or devices if multiple devices are used with the same user
account), as well as age information, health information, and user
history information about how long it has taken the user to enter
or exit vehicles in the past as discussed below.
[0066] The storage system 450 may also store routing data for
generating and evaluating routes between locations. For example,
the routing information may be used to estimate how long it would
take a vehicle at a first location to reach a second location. In
this regard, the routing information may include map data, not
necessarily as particular as the detailed map data 200 described
above, but including roads, as well as information about those road
such as direction (one way, two way, etc.), orientation (North,
South, etc.), speed limits, as well as traffic information
identifying expected traffic conditions, etc.
[0067] As mentioned above, rather than saving sensor data detecting
various traffic features on computing device 110 or server
computing device 410, such sensor data may additionally or
alternatively be stored on storage system 450. Likewise, threshold
values for components of vehicle 100 may likewise be stored on
storage system 450.
[0068] The storage system 450 may also store information which can
be provided to client computing devices for display to a user. For
instance, the storage system 450 may store predetermined distance
information for determining an area at which a vehicle is likely to
stop for a given pickup or drop-off location. The storage system
450 may also store graphics, icons, and other items which may be
displayed to a user as discussed below.
[0069] As with memory 130, storage system 450 can be of any type of
computerized storage capable of storing information accessible by
the server computing devices 410, such as a hard-drive, memory
card, ROM, RAM, DVD, CD-ROM, write-capable, and read-only memories.
In addition, storage system 450 may include a distributed storage
system where data is stored on a plurality of different storage
devices which may be physically located at the same or different
geographic locations. Storage system 450 may be connected to the
computing devices via the network 460 as shown in FIG. 4 and/or may
be directly connected to or incorporated into any of the computing
devices 110, 410, 420, 430, 440, etc.
EXAMPLE METHODS
[0070] In addition to the systems described above and illustrated
in the figures, various operations will now be described. It should
be understood that the following operations do not have to be
performed in the precise order described below. Rather, various
steps can be handled in a different order or simultaneously, and
steps may also be added or omitted
[0071] FIG. 6 illustrates an example situation 600 for performing a
plurality of performance checks on vehicle 100. Various features in
FIG. 6 may generally correspond to the shape, location, and other
characteristics of features shown in map data 200 of FIG. 2, and
labeled as such. Additional features in FIG. 6, including various
road users and other objects, are described in detail below.
Although these examples are useful for demonstration purposes, they
should not be considered limiting.
[0072] As shown in FIG. 6, vehicle 100 is currently parked curbside
in lane 212 of road 210, to ensure safe operation on the road,
vehicle 100 may need to perform performance checks on its systems.
In this regard, vehicle 100 may be scheduled to perform the
performance checks on its systems on a regular basis, such as every
day or week, every predetermined number of kilometers traveled or
numbers of trips completed, or some frequency mandated by law. For
example, during a previous day, vehicle 100 might have completed a
number of trips, and upon completing these trips, vehicle 100 has
parked roadside by the curb in lane 212. On the current day, before
going on more trips, vehicle 100 may first perform a plurality of
performance checks on its various systems. In one instance, some
types of performance checks may be performed at higher frequency
than other performance checks. In another instance, the frequency
of the performance checks may depend on the type of vehicle.
[0073] In order to perform the plurality of performance checks on
the vehicle, a test route may be determined. In this regard,
computing device 110 may determine the test route based on a
location of the vehicle, map data, and the types of performance
checks to be performed. For instance, computing device 110 may
determine that vehicle 100 is currently parked by the curb in lane
212 near intersection 219, and determine, based on map data 200, a
test route nearby so that vehicle 100 does not need to drive to a
designated depot or testing center just to perform these tests.
This ensures that the performance checks are performed as soon as
possible, instead of risking operating the vehicle 100 on a long
drive to the designated testing center, and ensures a more
efficient use of resources, including fuel.
[0074] Computing device 110 may determine the test route further
based on the types of performance checks that need to be performed
in order to complete the plurality of performance checks. For
instance, a list of required performance checks, including the type
of each required performance check and frequency for each required
performance check, may be stored on computing device 110.
Additionally or alternatively, the list of required performance
checks may be stored on server computing device 410 and/or storage
system 450 accessible by computing device 110. For example, the
list of required performance checks may include items such as
"perform a sensor check using a stored traffic light detection at
least once per 24 hours," "perform a map check using a stop sign
stored in map data at least once per month," "perform a component
check on all four tires at least once per week," etc. In one
aspect, computing device 110 may select a plurality of segments,
such as a plurality of road segments, using map data and stored
sensor data, where each of the plurality of road segments is
selected for performing one or more of the required performance
checks. Computing device 110 may then connect the plurality of road
segments, and connect the location of the vehicle to one of the
plurality of road segments to determine a test route in order to
allow the vehicle to perform the plurality of performance
checks.
[0075] For instance, for a sensor check, computing device 110 may
select a segment for a test route so that sensor data can be
collected to compare with stored values of previous detections of
traffic features or stationary objects. For example, as shown in
FIG. 6, computing device 110 may determine that traffic light 216
and pedestrian crossing 218 nearby vehicle 100 are stored as
previously detected traffic features near vehicle 100. As such, a
sensor check on one or more detection systems may be performed by
comparing newly detected values to these stored values. Therefore,
computing device 110 may determine that segment 610 beginning at
the current location of vehicle 100 and a right turn from lane 212
to road 220 may be used for performing the sensor check.
[0076] For a map check, computing device 110 may select a segment
for a test route so that sensor data can be collected to compare
with stored locations and/or orientations of traffic features or
stationary objects in map data. For example, as shown in FIG. 6,
computing device 110 may determine that stop sign 260 is stored in
map data 200 as a traffic feature. As such, a map check on the map
data 200 stored in navigation system may be performed by comparing
newly detected location and orientation of the stop sign 260 with
the stored location and/or orientation of the stop sign 260 in map
data 200. Therefore, computing device 110 may determine that
segment 640 including a portion of road 230 near stop sign 260 may
be used for performing the map check.
[0077] For a component check, computing device 110 may select a
segment for a test route where a particular vehicle maneuver may be
performed. For example, as shown in FIG. 6, computing device 110
may determine that a left turn may be performed at intersection
229. As such, a component check on the brake, wheel alignment, and
left turn signal may be performed at intersection 229 while vehicle
100 performs the left turn. Therefore, computing device 110 may
determine that segment 620 including a portion of road 220 and a
left turn at intersection 229 to road 230 can be used for
performing the component check.
[0078] In some instances, computing device 110 may determine that
more than one segment is needed for performing a particular type of
performance check. In this regard, a human operator may manually a
list of items for a test route. Alternatively or additionally, a
list of items required for a test route may be stored on computing
devices 110, and/or stored on server computing device 410 and/or
storage system 450 accessible by computing device 110. For example,
the list of items required for a test route may include items such
as "five or more traffic lights on the test route," "one multipoint
turn on the test route," etc. For example, as shown in FIG. 6,
computing device 110 may determine that a component check for the
reverse signal requires maneuvers such as back-in or parallel
parking or multi-point turns. As such, computing device may
determine that segment 650 including parking lot 280 may be used
for performing the component check on the reverse signal.
[0079] In other instances, the segments of test routes selected for
each type of performance check may be the same or have overlapping
portions, or in other words, a given segment may be used for
performing multiple types of performance checks. For example, in
addition for sensor check, segment 610 may also be used for map
check, since the location and/or orientation of traffic features
such as traffic light 216 and pedestrian crossing 218 are stored in
map data 200. For another example, segment 610 may also be used for
a component check on the brake, wheel alignment, and right turn
signal.
[0080] Computing device 110 may select additional segments of test
route connecting the various segments selected for the particular
types of performance checks. For example, computing device 110 may
determine that segment 630 may be needed to connect segment 620 and
segment 640. As such, an example test route may include segments
610, 620, 630, 640, and 650.
[0081] Further, where applicable, computing device 110 may store
the corresponding or associated performance checks to be performed
using sensor data from each segment of the test route. For example,
computing device 110 may associate segment 610 with a sensor check,
a map check using traffic light 216, pedestrian crossing 218, and a
component check on wheel alignment and right-turn signal. For
another example, computing device 110 may associate segment 620
with a component check on left-turn signal and wheel alignment. For
still another example, computing device 110 may associate segment
640 with a sensor check and a map check using stop sign 260. For
yet another example, computing device 110 may not associate any
check with segment 630.
[0082] Computing device 110 may determine the test route further
based on additional requirements for test routes. One example
requirement may be that one or more segments of the test route must
have below a threshold traffic volume. In this regard, computing
device 110 may receive historical or real-time traffic data from a
database. Another example requirement may be that one or more
segments of the route must have a speed limit below or above a
threshold speed limit. In this regard, computing device 110 may
determine speed limits of various roads based on map data 200. Yet
another example requirement may be that one or more segments of the
route must not be performed in certain areas, such as a school
zone. In this regard, computing device 110 may determine zoning
information based on map data 200. Still another example
requirement may be that particular types of maneuvers must be
performed in a parking lot. For example, as shown in FIG. 6,
although multi-point turn maneuvers may also be performed on road
230, parking lot 280 may be chosen based on a requirement that
multi-point turns be made in parking lots. Additional example
requirements may be that the test route must include multiple
distinct traffic lights, one or more cul-de-sacs for performing
multipoint turns, and a stored traffic feature in a designated
depot or testing center for the vehicle 100.
[0083] The test route need not be a closed loop. For example, as
shown in FIG. 6, computing device 110 may determine to go on a next
segment at the end of segment 650, instead of returning to the
beginning of the test route. In other examples, the test route may
be a closed loop, for example, computing device 110 may determine
an additional segment connecting the end of segment 650 back to the
beginning of segment 610. In examples where the test route is a
closed loop, the plurality of performance checks may be repeated in
order to collect more sets of sensor data, which for example may be
averaged to obtain more accurate results.
[0084] The test route may be stored so that the test route can be
used again by the vehicle at a later time to perform the
aforementioned checks. For instance, the test route described above
may be stored, and if vehicle 100 happens to be around the area
when the plurality of performance checks need to be performed
again, computing device 110 may simply use the stored test route,
instead of determining a new test route. For another instance,
based on the performance checks need to be performed again,
computing device 110 may use some but not all the segments of the
stored test route.
[0085] Once the test route is determined, computing device 110 may
control the vehicle 100 to drive along the test route. While doing
so, the perception system 172 and/or computing devices 110 may
collect data while on the test route including sensor data and
component data in order to perform the aforementioned performance
checks. For example, FIG. 7 shows example sensor data 700 collected
by various sensors in the perception system 172 while vehicle 100
drives along the test route shown in FIG. 6. For another example,
FIG. 8 shows example components data 800 collected from various
components while vehicle 100 drives along the test route shown in
FIG. 6.
[0086] Referring to FIGS. 6 and 7, the collected sensor data may
include data on permanent traffic features or stationary objects,
such as traffic light 216, pedestrian crossing 218, building 270,
stop sign 260, and parking lot 280. As shown in FIG. 7, the sensor
data may include information such as the detected location and
orientation of each traffic feature or object, as well as the
location of the vehicle 100 when the sensor data on that feature or
object is taken. For example, while on the test route, when vehicle
100 is at location [x_a, y_a], LIDAR sensor(s) 180 of vehicle 100
may detect traffic light 216 at location [x1, y1] and at an angle
25.degree. from vehicle 100, and building 270 at location [x3, y3]
and at an angle 10.degree. from vehicle 100. For another example,
while still on the test route, when vehicle 100 is at location
[x_b, y_b], LIDAR sensor(s) 180 of vehicle 100 may detect stop sign
260 at location [x4, y4] and at an angle 25.degree. from vehicle
100, and parking lot 280 at location [x5, y5] and at an angle
25.degree. from vehicle 100. Locations of vehicle 100 during the
test route may be determined by navigation system 168. Although not
shown, the LIDAR data may further include details such as the size
and shape of these features or objects.
[0087] The stored sensor data may include information such as the
previously detected location and orientation of each traffic
feature or object. In this regard, the stored sensor data for a
traffic feature or object may include previous detections of the
traffic feature or object by sensors of the vehicle 100 in the
past. The stored sensor data for a traffic feature or object may
additionally or alternatively include previous detections of the
traffic feature or object made by sensors of other vehicles.
Further, the stored sensor data may include the location of the
vehicle taking the sensor data when the traffic feature or object
was detected. The stored sensor data may be stored on computing
device 110. Additionally or alternatively, the stored sensor data
may be stored on server computing device 410 and/or storage system
450 accessible by computer device 110.
[0088] Although not shown, stored sensor data and collected sensor
data may include same type of sensor data taken by multiple
sensors, such as by different LIDAR sensors mounted at different
locations in or on vehicle 100. Further, stored sensor data and
collected sensor data may include different types of sensor data,
such as camera data. Each type of sensor data may include similar
information as LIDAR data, such as detected location and
orientation of each traffic feature or object, and the location of
the vehicle 100 when the sensor data on that feature or object is
taken. In addition, each type of sensor data may include further
details such as the size, shape, color of these features or
objects.
[0089] Although not shown, the collected sensor data may further
include data on temporary or moving traffic features and/or
objects, such as vehicle 100A, vehicle 100B, traffic cone 670 and
pedestrian 680. For example, while at location [x_c, y_c] of the
test route, LIDAR sensor(s) 180 of vehicle 100 may detect vehicle
100A at location [x6, y6] at a 15.degree. angle from the front of
the vehicle 100. At or around the same time, camera sensor(s) 182
and RADAR sensor 184 may also each detect vehicle 100A at location
[x6, y6] at a 15.degree. angle. For example, the camera data may
further include the color of vehicle 100A, and RADAR data may
further include speed of vehicle 100A.
[0090] Referring to FIGS. 6 and 8, component data may be collected
on various components of vehicle 100. For example as shown in FIG.
8, tire pressures may be collected for all four tires of vehicle
100. For another example, wheel alignment data may be collected on
all four wheels of vehicle 100. The wheel alignment data may
include camber angle, caster angle, and toe angle for each wheel.
For still another example, data on brake of vehicle 100 may be
collected. For instance, stopping distance at a specific speed such
as 100 km/hr may be measured. For yet another example,
responsiveness of various lights, such as the turn and reverse
signals, as well as night light, can be turned on and off.
[0091] During or after the vehicle completes the test route, the
plurality of performance checks may be performed by analyzing the
collected data. Computing devices 110 may perform the plurality of
performance checks by analyzing the collected data in real time
while vehicle 100 navigates through the test route, or store the
collected data in memory 130 so that computing device 110 may
perform the checks after completing the test route. Additionally or
alternatively, the collected data may be uploaded to server
computing device 410 or storage system 450 so that server computing
device 410 may perform the plurality of performance checks. Having
computing device 110 perform the checks may provide greater
efficiency, since uploading collected data to server computing
device 410 or storage system 450 may be time consuming.
[0092] For a sensor check, detected characteristics of a traffic
feature or object collected during the test route may be compared
with previously detected or stored characteristics of the traffic
feature or object. A sensor may satisfy the sensor check when the
characteristics collected during the test route match the
previously detected or stored characteristics by the same sensor,
and not satisfy the sensor check when the characteristics collected
during the test route do not match the previously detected or
stored characteristics. For instance, referring to FIG. 7,
collected LIDAR data from LIDAR sensor(s) 180 may be compared to
stored LIDAR values from a previous detection by LIDAR sensor(s)
180. For example, computing device 110 may determine that detected
location for each of traffic light 216, building 270, stop sign 260
and parking lot 280 are identical to stored LIDAR values, but
detected orientation of each is offset by a 5.degree. angle. As
such, computing device 110 may determine that LIDAR sensor(s) 180
does not satisfy the sensor check.
[0093] FIG. 9 shows an example situation 900 illustrating an
example sensor check. Various features in FIG. 9 may generally
correspond to the shape, location, and other characteristics of
features shown in map data 200 of FIG. 2, and labeled as such.
Additional features in FIG. 9, including various road users and
other objects, are described in detail below. Although these
examples are useful for demonstration purposes, they should not be
considered limiting.
[0094] As shown in FIG. 9, while vehicle 100 is at location [x_b,
y_b], LIDAR sensor(s) 180 detects stop sign 260 at a location [x4,
y4] and orientation of 25.degree. angle with respect to a front
right corner of vehicle 100. However, the stored LIDAR values for
the stop sign 260 include location [x4, y4] and orientation of
30.degree. angle with respect to a front right corner of vehicle
100. This may be due to a movement of the LIDAR sensor(s) 180 from
its previous position when the stored LIDAR values were taken. For
example, a pedestrian might have accidentally touched the LIDAR
sensor(s) 180 when passing by vehicle 100 while vehicle 100 was
parked curbside in lane 212. As such, this rotation causes a
-5.degree. angle offset for all detections made by LIDAR sensor(s)
180.
[0095] Additionally or alternatively, computing device 110 may
compare the location and/or orientation of traffic features and/or
objects detected in the collected sensor data with the location
and/or orientation of traffic features and/or objects stored in map
data 200. For example as shown in FIG. 7, computing device 110 may
compare location [x1, y1] for traffic light 216 detected in
collected LIDAR data with location [x1, y1] stored in map data 200,
compare location [x3, y3] for building 270 detected in collected
LIDAR data with location [x3, y3] stored in map data 200, compare
location [x4, y4] for stop sign 260 detected in collected LIDAR
data with location [x4, y4] stored in map data 200, compare
location [x5, y5] for parking lot 280 detected in collected LIDAR
data with location [x5, y5] stored in map data 200, and conclude
that LIDAR sensor(s) 180 pass the sensor check. In this regard,
computing device 110 may compare collected sensor data with map
data 200 for some or all traffic features and/or objects detected
during the test route.
[0096] In some instances, computing device 110 may determine that a
sensor may still pass a sensor test if the differences between the
stored and collected sensor data are within a predetermined range.
For instance, computing device 110 may determine that LIDAR
sensor(s) 180 may still pass the sensor test if the difference in
stored and detected orientation for a detected object is within a
10.degree. range.
[0097] Computing device 110 may determine one or more corrections
for one or more sensors that fails the sensor test. For example,
for LIDAR sensor(s) 180, computing device 110 may determine a
+5.degree. correction for all orientation values detected by LIDAR
sensor(s) 180. For instance, computing device 110 may add 5.degree.
to the detected 25.degree. angle for stop sign 260.
[0098] Another sensor check may include comparing collected sensor
data from various sensors of a same type for a detected object. For
instance, if LIDAR sensor(s) 180 include multiple sensors have
overlapping fields of views, computing device 110 may compare the
LIDAR point cloud for traffic light 216 collected by a first sensor
with the LIDAR point cloud for traffic light 216 collected by a
second sensor. Such sensor error of the second sensor may be caused
by any of a number of factors, such as damage by another road user,
or due to environmental factors such as extreme temperature or
humidity. Computing device 110 may determine that, if the two LIDAR
point clouds match substantially, such as by 90% or some other
threshold, then both the first and second sensors pass the sensor
check.
[0099] Still another sensor check may include determining a
resolution or field of view captured by a sensor. For example, if
collected LIDAR data for LIDAR sensor(s) 180 has a smaller field of
view than the stored LIDAR data, computing device 110 may further
determine that LIDAR sensor(s) 180 has failed the sensor check. In
some instances, computing device 110 may determine that LIDAR
sensor(s) 180 may still pass the sensor test if the difference
between field of view of the collected LIDAR data during test route
and field of view of the stored LIDAR data is within a
predetermined threshold difference. For another example, if
collected camera data for camera sensor(s) 182 has a lower
resolution than the stored camera data, computing device 110 may
further determine that camera sensor(s) 182 has failed the sensor
check. In some instances, computing device 110 may determine that
camera sensor(s) 182 may still pass the sensor test if the
difference between resolution of the collected camera data during
test route and resolution of stored camera data is within a
predetermined threshold difference. Such changes in resolution or
field of view may be caused by any of a number of factors, such as
damage by another road user, or due to environmental factors such
as extreme temperature or humidity.
[0100] Yet another sensor check may include determining whether a
sensor produces unreasonable sensor data. For example, computing
device 110 may determine that camera data produced by camera
sensor(s) 182 are all green, and conclude that camera sensor(s) 182
fail the sensor check. For another example, computing device 110
may determine that LIDAR sensor(s) 180 produces empty point clouds,
and conclude that LIDAR sensor(s) 180 fail the sensor check. Such
changes in resolution or field of view may be caused by any of a
number of factors, such as damage by another road user, or due to
environmental factors such as extreme temperature or humidity.
[0101] For another instance, for a map check, a location or
orientation of a detected traffic feature may be compared with the
location and/or orientation of a previously detected or stored
traffic feature stored in map data of the vehicle. The map data may
satisfy the map check when the location and/or orientation of
detected traffic features during the test route match the location
and/or orientation of traffic feature stored in the map data. For
instance, referring to FIG. 7, locations of traffic features
detected by LIDAR sensor(s) 180 may be compared to locations stored
in map data 200. For example, computing device 110 may determine
that detected location by LIDAR sensor(s) 180 for each of traffic
light 216, building 270, stop sign 260 and parking lot 280 are
identical to locations stored in map data 200, but that pedestrian
crossing 218 is not detected by LIDAR sensor(s) 180.
[0102] When a difference between the map data 200 and collected
sensor data on a traffic feature is detected, computing device 110
may further determine whether the difference was due to an error in
the map data 200 or an error in the collected sensor data. For
example, computing device 110 may determine that, since pedestrian
crossing is not a 3D structure and that the field of view of LIDAR
sensor(s) 180 does not include ground level, LIDAR sensor(s) 180
cannot detect pedestrian crossing 218, and therefore the difference
does not indicate an error in map data 200. In such cases,
computing device 110 may further confirm by comparing the location
stored in map data 200 with collected sensor data from another
sensor, such as camera sensor(s) 182. For example, computing device
110 may determine that the location for pedestrian crossing 218 in
map data 200 matches the location detected by camera sensor(s)
182.
[0103] In some instances, computing device 110 device may determine
that an update needs to be made for map data 200. Referring to FIG.
9, which shows the example situation 900 further illustrating an
example map check. As shown, while at location [x_b, y_b], LIDAR
sensor(s) 180 of vehicle 100 detects a no-enter sign 910 near exit
284 of parking lot 280. However, map data 200 does not include data
on a no-enter sign at this location. As such, computing device 110
may determine to update map data 200 with the detected location of
no-enter sign 910.
[0104] In addition or alternatively, computing device 110 may
determine that, even if some error exists, the map data may still
pass a map test if a threshold number or percentage of traffic
features stored in the map data have locations matching the
detected locations from the collected sensor data. For instance,
computing device 110 may determine that map data 200 may still pass
the map test if at least five or at least 80% of the stored
features have locations matching the collected sensor data. For
example, since locations for traffic light 216, pedestrian crossing
218, building 270, stop sign 260, and parking lot 280 match that of
collected LIDAR data, even though location for the no-enter sign
910 was missing, computing device 110 may still determine that map
data 200 may pass the map test.
[0105] For another instance, for a component check, the one or more
measurements related to a component of the vehicle may be compared
with predetermined requirements. The component may satisfy the
component check when the one or more measurements satisfy
predetermined requirements. For example, the predetermined
requirements may be stored in computing device 110, or
alternatively or additionally stored on server computing device 410
and/or storage system 450.
[0106] In some instances, a component may satisfy a component check
if a measurement meets a predetermined threshold value. For
example, referring to FIG. 8, a predetermined minimum threshold of
35 psi may be stored for tires of vehicle 100. As shown, since the
front left tire, the rear left tire, and the rear right tire each
meets the predetermined minimum threshold, these tires satisfy the
component check. However, since front right tire has a pressure of
only 20 psi, the front right tire fails the component check.
[0107] Additionally of alternatively, a component may satisfy a
component check if a measurement is within a predetermined range of
values. For example, referring to FIG. 8, predetermined alignment
angles may be set for the tires of vehicle 100, which include
camber, caster, and toe angles. Since each of the tires of vehicle
100 have alignment angles within these predetermined ranges, each
of the tires of vehicle 100 passes the component check.
[0108] Additionally of alternatively, a component may satisfy a
component check if a measurement indicates that the component has a
predetermined level of responsiveness. For example, referring to
FIG. 8, the predetermined level of responsiveness may be set as a
binary (responsive or not) for each of the left turn, right turn,
reverse, and brake signal lights, as well as the headlight. As
shown, since left turn, right turn, reverse, and brake signal
lights are all responsive, computing device 110 may determine that
they each pass the component check. However, since headlight is
unresponsive, computing device 110 may determine that the headlight
fails the component check.
[0109] For another example, again referring to FIG. 8, the
predetermined level or responsiveness may be set as a predetermined
level of delay. As shown, for the brake, a predetermined stopping
distance at a specific speed, such as 100 km/hr, may be set for
vehicle 100. As such, since the measured stopping distance for
vehicle 100 is 19 m, which is below the predetermined stopping
distance of 20 m, computing device 110 may determine that the brake
passes the component check.
[0110] Once the plurality of performance checks are completed,
computing devices 110 may select an operation mode for vehicle 100.
Modes for operation may include, for example, task designations
(passenger or non-passenger tasks). Modes of operation may further
include various limits, such as limits on speeds, distance,
geographic area, or environmental conditions (such as weather,
day/night). Modes for operation may also include an inactive mode
where the vehicle is pulled over or parked after completing the
plurality of performance checks.
[0111] Computing device 110 may determine an operation mode based
on results from the plurality of performance checks. For example,
an operation mode may only be selected if a threshold number of
percentage of performance checks are passed. For another example,
an operation mode may only be selected if a specific set of
performance checks are passed, such as a set of performance checks
specific to driving at night or during poor visibility, which may
include performance checks such as the sensor checks described
above, and component checks involving signal lights and headlight,
etc.
[0112] Computing device 110 may determine that one or more
operation modes cannot be selected based on specific failures. For
example, computing device 110 may determine that, if stopping
distance at 100 km/hr for vehicle 100 is above 20 m, modes of
operation involving driving at a speed of 100 km/hr or greater
cannot be selected. For another example, computing device 110 may
determine that, if less than 80% of the sensors in the perception
system 172 fail the sensor test, operation mode involving driving
at night or certain weather conditions cannot be selected. For
still another example, computing device 110 may determine that, if
one or more tires has a tire pressure below 35 psi, modes of
operation involving passenger tasks cannot be selected.
[0113] Computing device 110 may select an operation mode further
based on other factors, such as traffic law requirements and the
type of vehicle. For example, traffic law may require a vehicle to
have operating turn signals. As such, computing device 110 may
select the inactive mode if any of the turn signals is
unresponsive. For another example, computing device 110 may select
an operation mode with a limit on distance only for compact
vehicles with below normal tire pressures, and select an inactive
operation mode for trucks with below normal tire pressures.
[0114] Once an operation mode is selected, computing device 110 may
operate vehicle 100 in the selected operation mode. For example,
operating in the selected operation mode may include operating
according to limits of the mode of operation, such as limits on
speed, distance, geographic area, environmental condition. For
another example, operating in the selected mode may include whether
to determining whether to accept passenger or non-passenger
tasks.
[0115] Operating in the selected operation mode may further include
using the determined corrections for one or more sensors. For
example, as described with respect to FIGS. 7 and 9, when operating
vehicle 100, computing device 110 may apply a correction of
+5.degree. to sensor data detected by LIDAR sensor(s) 180.
[0116] Operating in the selected operation mode may further include
using the updated map data. For example, as described with respect
to FIGS. 7 and 9, when operating vehicle 100, computing device 110
may use updated map data 200 including the no-enter sign 910.
[0117] Operation modes may also be selected for a plurality of
vehicles by a remote system, such as a fleet management system. For
example, server computing device 410 may manage a fleet of vehicles
including vehicle 100, 100A, 100B. In this regard, sensor data and
component data collected by various vehicles in the fleet, such as
vehicle 100, 100A, 100B may be uploaded to server computing device
410. Server computing device 410 may compare the collected sensor
data from each vehicle to stored sensor values from previous
detections. Server computing device 410 may also compare the
collected components data with stored predetermined requirements.
In some instances, the plurality of performance checks may be
performed by computing device of each vehicle, and only the results
(pass/fail) are uploaded to server computing device 410. Sever
computing device 410 may then designate modes of operations for
subsets of vehicles of the plurality of vehicles based on the
plurality of performance checks as described above, such as based
on passing a threshold number or percentage of performance checks,
particular sets of performance checks, other factors such as type
of vehicle or traffic law, etc. For another example, server
computing device 410 may designate modes of operations further
based on a planned distribution or demand for the vehicles in the
fleet.
[0118] FIG. 10 shows an example flow diagram 1000 of an example
method for performing a plurality of performance checks. The
example method may be performed by one or more processors, such as
one or more processors 120 of computing device 110. For example,
processors 120 of computing device 110 may receive data and make
various determinations as shown in flow diagram 1000, and control
the vehicle 100 based on these determinations.
[0119] Referring to FIG. 10, in block 1010, a plurality of
performance checks are identified, including a first check for a
detection system of a plurality of detection systems of the vehicle
and a second check for map data. In block 1020, a plurality of road
segments are selected based on a location of the vehicle and the
plurality of performance checks, wherein each of the plurality of
road segments is selected for performing one or more of the
plurality of performance checks. In block 1030, a test route is
determined for the vehicle by connecting the plurality of road
segments and by connecting the location of the vehicle to one of
the plurality of road segments. For example, a plurality of road
segments and a test route may be determined as described in
relation to FIG. 6. In block 1040, the vehicle is controlled along
the test route in an autonomous driving mode. In block 1050, while
controlling the vehicle, sensor data are received from the
plurality of detection systems of a vehicle. For example, sensor
data collected on a test route may be received by computing device
110 as described in relation to FIG. 7.
[0120] In block 1060, the plurality of performance checks are
performed based on the received sensor data. For example, one or
more sensor checks may be performed by comparing the collected
sensor data with stored previous sensor data. For another example,
one or more map checks may be performed by comparing the collected
sensor data with map data. In block 1070, an operation mode is
selected from a plurality of operation modes for the vehicle based
on results of the plurality of performance checks. For example, the
driving mode may be selected based on the results meeting a
threshold number or percentage of performance checks. In block
1080, the vehicle is operated in the selected operation mode. For
example, operating in the selected operation mode may include using
corrections to sensor data or updates to map data.
[0121] Unless otherwise stated, the foregoing alternative examples
are not mutually exclusive, but may be implemented in various
combinations to achieve unique advantages. As these and other
variations and combinations of the features discussed above can be
utilized without departing from the subject matter defined by the
claims, the foregoing description of the embodiments should be
taken by way of illustration rather than by way of limitation of
the subject matter defined by the claims. In addition, the
provision of the examples described herein, as well as clauses
phrased as "such as," "including" and the like, should not be
interpreted as limiting the subject matter of the claims to the
specific examples; rather, the examples are intended to illustrate
only one of many possible embodiments. Further, the same reference
numbers in different drawings can identify the same or similar
elements.
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