U.S. patent application number 16/796349 was filed with the patent office on 2021-01-21 for system and method for improving an autonomous vehicle driving model based on driver engagement conditions.
The applicant listed for this patent is KACHE.AI. Invention is credited to Anthony Levandowski, Bernward Schwarte.
Application Number | 20210018918 16/796349 |
Document ID | / |
Family ID | 1000004658258 |
Filed Date | 2021-01-21 |
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United States Patent
Application |
20210018918 |
Kind Code |
A1 |
Levandowski; Anthony ; et
al. |
January 21, 2021 |
SYSTEM AND METHOD FOR IMPROVING AN AUTONOMOUS VEHICLE DRIVING MODEL
BASED ON DRIVER ENGAGEMENT CONDITIONS
Abstract
Systems and methods for implementing one or more autonomous
features for autonomous and semi-autonomous control of one or more
vehicles are provided. More specifically, image data may be
obtained from an image acquisition device and processed utilizing
one or more machine learning models to identify, track, and extract
one or more features of the image utilized in decision making
processes for providing steering angle and/or
acceleration/deceleration input to one or more vehicle controllers.
In some instances, techniques may be employed such that the
autonomous and semi-autonomous control of a vehicle may change
between vehicle follow and lane follow modes. In some instances, at
least a portion of the machine learning model may be updated based
on one or more conditions.
Inventors: |
Levandowski; Anthony; (San
Francisco, CA) ; Schwarte; Bernward; (San Francisco,
CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
KACHE.AI |
San Francisco |
CA |
US |
|
|
Family ID: |
1000004658258 |
Appl. No.: |
16/796349 |
Filed: |
February 20, 2020 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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16511968 |
Jul 15, 2019 |
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16796349 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G05D 2201/0213 20130101;
G06N 20/00 20190101; G05D 1/0287 20130101; G05D 1/0088
20130101 |
International
Class: |
G05D 1/00 20060101
G05D001/00; G05D 1/02 20060101 G05D001/02; G06N 20/00 20060101
G06N020/00 |
Claims
1. A method comprising: receiving an indication that an autonomous
driving mode has been disengaged by a user; determining when a
cumulative number of user disengagements of the autonomous driving
mode, including the indication that the autonomous driving mode has
been disengaged by the user, exceeds a threshold; requesting a
portion of an autonomous vehicle model based on the cumulative
number of user disengagements of the autonomous driving mode
exceeding the threshold; and receiving the requested portion of the
autonomous vehicle model.
2. The method of claim 1, wherein a user disengagement of the
autonomous driving mode includes implementing at least one course
correction or correcting a course deviation.
3. The method of claim 2, wherein a course correction includes
determining that an input associated with a manual override was
received.
4. The method of claim 3, wherein the input associated with the
manual override includes one of a manual velocity change or manual
steering angle change.
5. The method of claim 2, wherein a course deviation includes
determining that a path traveled by an autonomous vehicle is
different from a projected path traveled by the autonomous
vehicle.
6. The method of claim 2, wherein the threshold varies based on one
or more of time, location, date, or weather condition.
7. The method of claim 1, generating a second autonomous vehicle
model by applying the received requested portion of the autonomous
vehicle model to an existing autonomous vehicle model.
8. The method of claim 1, further comprising: storing a location
associated with the indication that the autonomous driving mode has
been disengaged by the user.
9. The method of claim 8, wherein the requested portion of the
autonomous vehicle model is based on the stored location.
10. The method of claim 1, providing the requested portion of the
autonomous vehicle model to an autonomous vehicle.
11. A system comprising: a memory; a processor in communication
with the memory, wherein the processor executes instructions stored
in the memory, which cause the processor to execute a method, the
method comprising: receiving an indication that an autonomous
driving mode has been disengaged by a user; determining when a
cumulative number of user disengagements of the autonomous driving
mode, including the indication that the autonomous driving mode has
been disengaged by the user, exceeds a threshold; requesting a
portion of an autonomous vehicle model based on the cumulative
number of user disengagements of the autonomous driving mode
exceeding the threshold; and receiving the requested portion of the
autonomous vehicle model.
12. The system of claim 11, wherein a user disengagement of the
autonomous driving mode includes implementing at least one course
correction or correcting a course deviation.
13. The system of claim 12, wherein a course correction includes
determining that an input associated with a manual override was
received.
14. The system of claim 13, wherein the input associated with the
manual override includes one of a manual velocity change or manual
steering angle change.
15. The system of claim 12, wherein a course deviation includes
determining that a path traveled by an autonomous vehicle is
different from a projected path traveled by the autonomous
vehicle.
16. A non-transitory computer readable medium having stored thereon
instructions, which when executed by a processor cause the
processor to execute a method, the method comprising: receiving an
indication that an autonomous driving mode has been disengaged by a
user; determining when a cumulative number of user disengagements
of the autonomous driving mode, including the indication that the
autonomous driving mode has been disengaged by the user, exceeds a
threshold; requesting a portion of an autonomous vehicle model
based on the cumulative number of user disengagements of the
autonomous driving mode exceeding the threshold; and receiving the
requested portion of the autonomous vehicle model.
17. The non-transitory computer readable medium of claim 16,
wherein a user disengagement of the autonomous driving mode
includes implementing at least one course correction or correcting
a course deviation.
18. The non-transitory computer readable medium of claim 17,
wherein a course correction includes determining that an input
associated with a manual override was received.
19. The non-transitory computer readable medium of claim 18,
wherein the input associated with the manual override includes one
of a manual velocity change or manual steering angle change.
20. The non-transitory computer readable medium of claim 17,
wherein a course deviation includes determining that a path
traveled by an autonomous vehicle is different from a projected
path traveled by the autonomous vehicle.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application is a continuation of and claims priority to
U.S. patent application Ser. No. 16/511,968, filed Jul. 15, 2019
and is a continuation of and claims priority to PCT International
Patent Application No. PCT/US2019/041720, filed Jul. 12, 2019,
which in turn claim priority to U.S. Provisional Patent Application
No. 62/697,912, filed Jul. 13, 2018, U.S. Provisional Patent
Application No. 62/697,915, filed Jul. 13, 2018, U.S. Provisional
Patent Application No. 62/697,919, filed Jul. 13, 2018, U.S.
Provisional Patent Application No. 62/697,922, filed Jul. 13, 2018,
U.S. Provisional Patent Application No. 62/697,930, filed Jul. 13,
2018, U.S. Provisional Patent Application No. 62/697,938, filed
Jul. 13, 2018, U.S. Provisional Patent Application No. 62/697,940,
filed Jul. 13, 2018, U.S. Provisional Patent Application No.
62/697,946, filed Jul. 13, 2018, U.S. Provisional Patent
Application No. 62/697,952, filed Jul. 13, 2018, U.S. Provisional
Patent Application No. 62/697,957, filed Jul. 13, 2018, U.S.
Provisional Patent Application No. 62/697,960, filed Jul. 13, 2018,
U.S. Provisional Patent Application No. 62/697,962, filed Jul. 13,
2018, U.S. Provisional Patent Application No. 62/697,965, filed
Jul. 13, 2018, U.S. Provisional Patent Application No. 62/697,969,
filed Jul. 13, 2018, and to U.S. Provisional Patent Application No.
62/697,971, filed Jul. 13, 2018, the disclosures of each of which
are hereby incorporated by reference herein in their entirety, for
all that they teach and for all purposes.
FIELD
[0002] The present disclosure is generally directed to vehicle
systems, in particular, toward autonomous vehicles.
BACKGROUND
[0003] Driving a vehicle, in particular, a semi-truck or
load-carrying vehicle requires a great deal of attention from the
driver. New vehicles and trucks are trying to alleviate some of the
burden of driving by introducing some level of autonomy with the
vehicle. Self-driving is one form of autonomy being developed.
Unfortunately, current self-driving vehicles require a great deal
of hardware and a suite of different sensors to function
effectively or safely. This additional hardware increases the cost
for the self-driving/autonomous systems and slows adoption of this
technology.
BRIEF DESCRIPTION OF THE DRAWINGS
[0004] FIG. 1 depicts a plurality of vehicles, in an environment,
in accordance with embodiments of the present disclosure;
[0005] FIG. 2A depicts a block diagram of an embodiment of a
vehicle computing environment of a vehicle in accordance with
embodiments of the present disclosure;
[0006] FIG. 2B depicts a hardware/software configuration for a
server or cloud computing function of a system, which may supply
driving models, in accordance with embodiments of the present
disclosure;
[0007] FIG. 2C depicts a computing environment that may function as
a server, user computer, or other system provided and described
herein, in accordance with embodiments of the present
disclosure;
[0008] FIG. 2D depicts an example of a computer system upon which a
server, computer, computing device, or other system or components
may be deployed or executed in accordance with embodiments of the
present disclosure;
[0009] FIG. 3A depicts one or more software structures including
one or more nodes and/or a data structure in accordance with
embodiments of the present disclosure;
[0010] FIG. 3B depicts one or more software structures including
one or more nodes and/or a data structure in accordance with
embodiments of the present disclosure;
[0011] FIG. 3C depicts one or more software structures including
one or more nodes and/or a data structure in accordance with
embodiments of the present disclosure;
[0012] FIG. 4 depicts details of an autonomous model generation
system in accordance with embodiments of the present
disclosure;
[0013] FIG. 5 depicts additional details of one or more autonomous
vehicle models that may reside at a fleet vehicle, database, and/or
be generated by an autonomous vehicle model generation system in
accordance with embodiments of the present disclosure;
[0014] FIG. 6 depicts additional details of an autonomous vehicle
model in accordance with embodiments of the present disclosure;
[0015] FIG. 7 depicts a first method for autonomously driving a
vehicle in accordance with embodiments of the present
disclosure;
[0016] FIG. 8 depicts a second method for autonomously driving a
vehicle in accordance with embodiments of the present
disclosure;
[0017] FIG. 9 depicts a third method for autonomously driving a
vehicle in accordance with embodiments of the present
disclosure;
[0018] FIG. 10 depicts a fourth method for autonomously driving a
vehicle in accordance with embodiments of the present
disclosure;
[0019] FIG. 11 depicts a fifth method for autonomously driving a
vehicle in accordance with embodiments of the present
disclosure;
[0020] FIG. 12 depicts a sixth method for autonomously driving a
vehicle in accordance with embodiments of the present
disclosure;
[0021] FIG. 13 depicts a method for determining an environmental
concern for an autonomous vehicle in accordance with embodiments of
the present disclosure;
[0022] FIG. 14 depicts a seventh method for autonomously driving a
vehicle in accordance with embodiments of the present
disclosure;
[0023] FIG. 15 depicts an eighth method for autonomously driving a
vehicle in accordance with embodiments of the present
disclosure;
[0024] FIG. 16 depicts a method for automatically calibrating one
or more image acquisition devices in accordance with embodiments of
the present disclosure;
[0025] FIG. 17 depicts a method of calibrating one or more camera
parameters over time in accordance with embodiments of the present
disclosure;
[0026] FIG. 18 depicts a method for calibrating a first image
acquisition device based on information received from a second
image acquisition device in accordance with embodiments of the
present disclosure;
[0027] FIG. 19A depicts a method utilized to adjust one or more
parameters of an autonomous vehicle based on an autonomous vehicle
model in accordance with embodiments of the present disclosure;
[0028] FIG. 19B depicts a method utilized to adjust one or more
parameters of an autonomous vehicle based on an autonomous vehicle
model in accordance with embodiments of the present disclosure;
[0029] FIG. 20 depicts one or more methods for receiving and/or
requesting a new autonomous vehicle model in accordance with
embodiments of the present disclosure;
[0030] FIG. 21 depicts an example method directed to updating one
or more autonomous vehicle models in accordance with embodiments of
the present disclosure;
[0031] FIG. 22A depicts additional details related to object
detection in accordance with embodiments of the present
disclosure;
[0032] FIG. 22B depicts additional details related to object
detection in accordance with embodiments of the present
disclosure;
[0033] FIG. 22C depicts additional details related to object
detection in accordance with embodiments of the present
disclosure;
[0034] FIG. 22D depicts additional details related to object
detection in accordance with embodiments of the present
disclosure;
[0035] FIG. 22E depicts additional details related to object
detection in accordance with embodiments of the present
disclosure;
[0036] FIG. 23A depicts additional details related to following a
vehicle in accordance with embodiments of the present
disclosure;
[0037] FIG. 23B depicts additional details related to following a
vehicle in accordance with embodiments of the present
disclosure;
[0038] FIG. 24A depicts additional details related to following a
lane in accordance with embodiments of the present disclosure;
[0039] FIG. 24B depicts additional details related to following a
lane in accordance with embodiments of the present disclosure;
[0040] FIG. 25A depicts additional details related to defining a
lane in accordance with embodiments of the present disclosure;
[0041] FIG. 25B depicts additional details related to defining a
lane in accordance with embodiments of the present disclosure;
[0042] FIG. 25C depicts additional details related to defining a
lane in accordance with embodiments of the present disclosure;
[0043] FIG. 25D depicts additional details related to defining a
lane in accordance with embodiments of the present disclosure;
[0044] FIG. 26A depicts additional details related to calibration
using an image in accordance with embodiments of the present
disclosure;
[0045] FIG. 26B depicts additional details related to calibration
using an image in accordance with embodiments of the present
disclosure;
[0046] FIG. 26C depicts additional details related to calibration
using an image in accordance with embodiments of the present
disclosure;
[0047] FIG. 27A depicts additional details of a data structure for
storing and/or communication information in accordance with
embodiments of the present disclosure;
[0048] FIG. 27B depicts additional details of a data structure for
storing and/or communication information in accordance with
embodiments of the present disclosure; and
[0049] FIG. 28 depicts an example data structure 2800 in accordance
with embodiments of the present disclosure.
DETAILED DESCRIPTION
[0050] Embodiments of the present disclosure will be described in
connection with a vehicle, and in accordance with at least some
situations or configurations, a semi-truck or freight vehicle and
associated systems.
[0051] FIG. 1 shows a plan view of a vehicle 104 in an environment
100, in accordance with embodiments of the present disclosure. The
vehicle 104 comprises a vehicle front 110, vehicle aft or rear 120,
vehicle roof 130, at least one vehicle side 160, a vehicle
undercarriage 140, and a vehicle interior 150. The vehicle 104 may
include one or more interior components inside an interior space
150, exterior components on the exterior parts of the vehicle,
systems to control the vehicle movement, vehicle speed, vehicle
acceleration, vehicle deceleration, climate control, infotainment,
other controls systems, structural components, etc.
[0052] Although shown in the form of a semi-truck or freight truck,
it should be appreciated that the vehicle 104 described herein may
include any conveyance or model of a conveyance, where the
conveyance was designed for the purpose of moving freight, such as
people, animals, cargo, and the like, or as a conveyance of people.
Vehicles may include, but are in not limited to, cars, trucks,
motorcycles, busses, automobiles, trains, trams, other railed
conveyances, boats, ships, marine conveyances, submarine
conveyances, airplanes, space craft, flying machines, human-powered
conveyances, and the like.
[0053] For the purpose of effectuating autonomous driving or other
autonomous operations, the vehicle 104 may include a number of
sensors, devices, and/or systems that are capable of assisting in
driving operations with or without human input. The sensors and
systems may include, but are in no way limited to, one or more of
cameras and/or other optical sensors (e.g., independent, stereo,
combined image, multispectral, etc.), infrared (IR) sensors,
ultraviolet (UV) sensors, radio frequency (RF) sensors, ultrasonic
sensors (e.g., transducers, transceivers, etc.), RADAR sensors
(e.g., object-detection sensors and/or systems), LIDAR systems,
odometer sensors and/or devices (e.g., encoders, etc.), orientation
sensors (e.g., accelerometers, gyroscopes, magnetometer, etc.),
navigation sensors and systems (e.g., GPS, etc.), and other
ranging, imaging, and/or object-detecting sensors. The sensors may
be disposed in an interior space 150 of the vehicle 104 and/or on
an outside of the vehicle 104. The sensors and systems may also be
disposed in one or more portions of a vehicle 104 (e.g., the frame,
a body panel, a compartment, etc.).
[0054] The vehicle sensors and systems may be selected and/or
configured to suit a level of operation associated with the vehicle
104. Among other things, the number of sensors used in a system may
be altered to increase or decrease information available to a
vehicle control system (e.g., affecting control capabilities of the
vehicle 104). Additionally or alternatively, the sensors and
systems may be part of one or more advanced driver assistance
systems (ADAS) associated with a vehicle 104. In any event, the
sensors and systems may be used to provide driving assistance at
any level of operation (e.g., from fully-manual to fully-autonomous
operations, etc.) as described herein. In some configurations, the
vehicle 104 includes only optical sensors to lower costs of
implementing the system.
[0055] The vehicle 104 may operate at a predetermined,
configurable, or static level of autonomy for vehicle driving
operations. At Level 0, or fully-manual driving operations, a
driver (e.g., a human driver) may be responsible for all the
driving control operations (e.g., steering, accelerating, braking,
etc.) associated with the vehicle. Level 0 is a no automation
level. At Level 1, the vehicle may be responsible for a limited
number of the driving operations associated with the vehicle, while
the driver is still responsible for most driving control
operations. For example, cruise control may be a Level 1 autonomous
level where the vehicle may control the throttle control and/or
braking operations. At Level 2, the vehicle may collect information
(e.g., via one or more driving assistance systems, sensors, etc.)
about an environment of the vehicle (e.g., surrounding area,
roadway, traffic, ambient conditions, etc.) and use the collected
information to control driving operations (e.g., steering,
accelerating, braking, etc.) associated with the vehicle. In Level
2, the driver may be required to perform some aspects of driving
operations not controlled by the vehicle but, like Levels 0 and 1,
a driver monitors or controls at least some of the driving
operations of the vehicle 104.
[0056] At Level 3, the vehicle 104 controls driving operations with
the driver separated from controlling the driving operations of the
vehicle 104, except when the vehicle 104 requests an operator to
act or intervene in some operation. Thus, the driver only conducts
operations when required or needed by the vehicle 104. At Level 4,
the vehicle 104 conducts driving operations even if a driver
ignores or fails to response to a request to intervene. Level 4 may
be referred to as a "High Automation" level. At Level 5, the
vehicle 104 can control all the driving operations associated with
the vehicle in all driving modes regardless of what the driver may
be doing. The vehicle 104, in Level 5, may continually monitor
traffic, vehicular, roadway, and/or other conditions in the
environment 100 while the vehicle is in operation.
[0057] The vehicle 104 may also be in communication with one or
more outside entities, for example, a computing system 122, another
vehicle 128, etc. Communication may be a wireless interconnection
112 as described herein. The wireless interconnection 112 can be
directly to the other entity, for example, the vehicle 128, or to a
communication antenna 108 (e.g., a cellular tower, a wireless
network endpoint, etc.). Then, the communication signal may be sent
through a network 116 (e.g., a local area network (LAN), a wide
area network (WAN), etc.) to the computing system 122. The
computing system 122 can be any type of computer, as described
herein, including a server or server system.
[0058] FIG. 1 generally shows a vehicle sensing environment 100 at
least partially viewed by the sensors disposed in, on, and/or about
the vehicle 104. Each sensor may include an operational detection
range R and operational detection angle .alpha.. The operational
detection range R may define the effective detection limit, or
distance, of the sensor. In some cases, this effective detection
limit may be defined as a distance from a portion of the sensor
(e.g., a lens, sensing surface, etc.) to a point in space offset
from the sensor. The effective detection limit may define a
distance, beyond which, the sensing capabilities of the sensor
deteriorate, fail to work, or are unreliable. The effective
detection limit may define a distance, within which, the sensing
capabilities of the sensor are able to provide accurate and/or
reliable detection information. The operational detection angle
.alpha. may define at least one angle of a span, between horizontal
and/or vertical limits, of a sensor. As can be appreciated, the
operational detection limit and the operational detection angle
.alpha. of a sensor together may define the effective detection
zone (e.g., the effective detection area, and/or volume, etc.) of a
sensor.
[0059] In some embodiments, the vehicle 104 may include an imaging
system to detect visual information in an environment surrounding
the vehicle 104. The visual information detected in the environment
surrounding the imaging system may be processed (e.g., via one or
more sensor and/or system processors, etc.) to generate a complete
360-degree view of an environment 100 around the vehicle 104. The
imaging system may be configured to generate changing 360-degree
views of the environment 100 in real-time, for instance, as the
vehicle 104 drives.
[0060] Sensor data and information may be collected by one or more
sensors or systems of the vehicle 104 monitoring the vehicle
sensing environment 100. This information may be processed (e.g.,
via a processor, computer-vision system, etc.) to determine targets
(e.g., objects, signs, people, markings, roadways, conditions,
etc.) inside one or more detection areas associated with the
vehicle sensing environment 100. In some cases, information from
multiple sensors may be processed to form composite sensor
detection information. For example, a first sensor and a second
sensor may correspond to a first camera and a second camera aimed
in a forward traveling direction of the vehicle 104. In this
example, images collected by the two cameras may be combined to
form stereo image information. This composite information may
increase the capabilities of a single sensor in the one or more
sensors by, for example, adding the ability to determine depth
associated with targets in the one or more detection zones. Similar
image data may be collected by rear view cameras (e.g., sensors)
aimed in a rearward traveling direction of the vehicle 104.
[0061] In some embodiments, multiple sensors may be effectively
joined to increase a sensing zone and provide increased sensing
coverage. For instance, multiple sensors disposed on the front 110
of the vehicle 104 may be joined to provide a zone of coverage that
spans across an entirety of the front 110 of the vehicle 104. These
overlapping detection zones may provide redundant sensing, enhanced
sensing, and/or provide greater detail in sensing within a
particular portion of a larger zone. Additionally or alternatively,
the sensors of the vehicle 104 may be arranged to create a complete
coverage, via one or more sensing zones around the vehicle 104. In
some areas, the sensing zones of two or more sensors may intersect
at an overlap zone. In some areas, the angle and/or detection limit
of two or more sensing zones may meet at a virtual intersection
point or plane.
[0062] The vehicle 104 may include a number of sensors disposed
proximal to the rear 120 of the vehicle 104. These sensors can
include, but are in no way limited to, an imaging sensor, camera,
IR, a radio object-detection and ranging sensors, RADAR, RF,
ultrasonic sensors, and/or other object-detection sensors. Among
other things, these sensors may detect targets near or approaching
the rear of the vehicle 104. For example, another vehicle
approaching the rear 120 of the vehicle 104 may be detected by one
or more of the ranging and imaging system (e.g., LIDAR), rear-view
cameras, and/or rear facing RADAR sensors. As described above, the
images from the rear-view cameras may be processed to generate a
stereo view (e.g., providing depth associated with an object or
environment, etc.) for targets visible to both cameras
[0063] This sensor arrangements may provide critical sensor
information to a vehicle control system in at least one of the
autonomous driving levels described above. For instance, when the
vehicle 104 is driving autonomously (e.g., Level 3, Level 4, or
Level 5) and detects other vehicles stopped in a travel path, the
sensor detection information may be sent to the vehicle control
system of the vehicle 104 to control a driving operation (e.g.,
braking, decelerating, etc.) associated with the vehicle 104 (in
this example, slowing the vehicle 104 as to avoid colliding with
the stopped other vehicles). As yet another example, the vehicle
104 may be operating and one or more of the imaging system, and/or
the side-facing sensors, may detect targets at a side 160 of the
vehicle 104. It should be appreciated that the sensors may detect a
target that is both at a side 160 and a front 110 of the vehicle
104 (e.g., disposed at a diagonal angle to a centerline of the
vehicle 104 running from the front 110 of the vehicle 104 to the
rear 120 of the vehicle). Additionally or alternatively, the
sensors may detect a target that is both, or simultaneously, at a
side 160 and a rear 120 of the vehicle 104 (e.g., disposed at a
diagonal angle to the centerline of the vehicle 104).
[0064] FIG. 2A is a block diagram of an embodiment of a vehicle
computing environment 200 of the vehicle 104 in accordance with
embodiments of the present disclosure. The computing environment
200 may include one or more vehicle sensors and systems 208,
computing system (processor) 204, interface 228, vehicle control
systems (e.g., steering system 236, braking system 240,
acceleration system 244, environmental control 248, infotainment
system 252, etc.), a communication system to the vehicle control
systems (e.g., a control area network (CAN) bus 232, a navigation
system 212, one or more data stores (e.g., user information 224,
model(s) 220, image data 216, etc.). These associated components
may be electrically and/or communicatively coupled to one another
via at least one bus or other interconnection. In some
configurations, the one or more associated components may send
and/or receive signals across a communication network to a separate
entity, for example, server 122.
[0065] The computing system 204 can include any hardware and/or
software to conduct operations, as described herein, in accordance
with embodiments of the present disclosure. The computing system
204 may be as described in conjunction with FIGS. 2C and 2D.
Interconnected to the computing system 204 may be one or more data
stores 216-224, the sensors 208, the navigation system, and/or the
interface 228, which are described hereinafter.
[0066] In accordance with embodiments of the present disclosure,
the interface 228 may comprise any type of known communication
medium or collection of communication media and may use any type of
protocols, such as SIP, TCP/IP, SNA, IPX, AppleTalk, and the like,
to transport messages between the computing system 204 and the
interface 228 and then translate those messages for transmission
onto the CAN bus 232. The interface 228 may include wired and/or
wireless communication technologies. Other examples of the
interface 228 may include, without limitation, a Local Area Network
(LAN), such as an Ethernet network, a Token-Ring network and/or the
like, a Wide Area Network (WAN), a virtual network, including
without limitation a virtual private network ("VPN"); an infra-red
network; a wireless network (e.g., a network operating under any of
the IEEE 802.9 suite of protocols, the Bluetooth.RTM. protocol
known in the art, and/or any other wireless protocol), and any
other type of packet-switched or circuit-switched network known in
the art and/or any combination of these and/or other networks. In
addition, it can be appreciated that the interface 228 need not be
limited to any one network type, and instead may be comprised of a
number of different networks and/or network types. The interface
228 may comprise a number of different communication media such as
coaxial cable, copper cable/wire, fiber-optic cable, antennas for
transmitting/receiving wireless messages, and combinations
thereof.
[0067] The vehicle 104 can include a navigation system 212 (e.g.,
global positioning system (GPS), etc.) and/or one or more sensors
208, for example, sensors for orientation, odometer, camera,
infrared (IR), and/or other optional sensors, for example LIDAR,
RADAR, ultrasonic, and/or other sensor or system. These driving
vehicle sensors and systems may be similar, if not identical, to
the sensors and systems described above.
[0068] The camera sensors may include one or more components
configured to detect image information associated with an
environment of the vehicle 104. In some embodiments, the camera
sensors may include a lens, filter, image sensor, and/or a digital
image processer. It is an aspect of the present disclosure that
multiple camera sensors may be used together to generate stereo
images providing depth measurements. Examples of the camera sensors
as described herein may include, but are not limited to, at least
one of ON Semiconductor.RTM. MT9V024 Global Shutter VGA GS CMOS
image sensors, Teledyne DALSA Falcon2 camera sensors, CMOSIS
CMV50000 high-speed CMOS image sensors, other industry-equivalent
camera sensors and/or systems, and may perform visual target and/or
obstacle detection in an environment around the vehicle 104 using
any known or future-developed standard and/or architecture.
[0069] The infrared (IR) sensors may include one or more components
configured to detect image information associated with an
environment of the vehicle 104. The IR sensors may be configured to
detect targets in low-light, dark, or poorly-lit environments. The
IR sensors may include an IR light emitting element (e.g., IR light
emitting diode (LED), etc.) and an IR photodiode. In some
embodiments, the IR photodiode may be configured to detect returned
IR light at or about the same wavelength to that emitted by the IR
light emitting element. In some embodiments, the IR sensors may
include at least one processor configured to interpret the returned
IR light and determine locational properties of targets. The IR
sensors may be configured to detect and/or measure a temperature
associated with a target (e.g., an object, pedestrian, other
vehicle, etc.). Examples of IR sensors as described herein may
include, but are not limited to, at least one of Opto Diode
lead-salt IR array sensors, Opto Diode OD-850 Near-IR LED sensors,
Opto Diode SA/SHA727 steady state IR emitters and IR detectors,
FLIR.RTM. LS microbolometer sensors, FLIR.RTM. TacFLIR 380-HD InSb
MWIR FPA and HD MWIR thermal sensors, FLIR.RTM. VOx 640.times.480
pixel detector sensors, Delphi IR sensors, other
industry-equivalent IR sensors and/or systems, and may perform IR
visual target and/or obstacle detection in an environment around
the vehicle 104 using any known or future-developed standard and/or
architecture.
[0070] An orientation sensor may include one or more sensors
configured to determine an orientation of the vehicle 104 relative
to at least one reference point. In some embodiments, the
orientation sensor may include at least one pressure transducer,
stress/strain gauge, accelerometer, gyroscope, and/or geomagnetic
sensor. Examples of the navigation sensor as described herein may
include, but are not limited to, at least one of Bosch Sensortec
BMX 160 series low-power absolute orientation sensors, Bosch
Sensortec BMX055 9-axis sensors, Bosch Sensortec BMI055 6-axis
inertial sensors, Bosch Sensortec BMI160 6-axis inertial sensors,
Bosch Sensortec BMF055 9-axis inertial sensors (accelerometer,
gyroscope, and magnetometer) with integrated Cortex M0+
microcontroller, Bosch Sensortec BMP280 absolute barometric
pressure sensors, Infineon TLV493D-A1B6 3D magnetic sensors,
Infineon TLI493D-W1B6 3D magnetic sensors, Infineon TL family of 3D
magnetic sensors, Murata Electronics SCC2000 series combined gyro
sensor and accelerometer, Murata Electronics SCC1300 series
combined gyro sensor and accelerometer, other industry-equivalent
orientation sensors and/or systems, and may perform orientation
detection and/or determination functions using any known or
future-developed standard and/or architecture.
[0071] The odometry sensor and/or system may include one or more
components that is configured to determine a change in position of
the vehicle 104 over time. In some embodiments, the odometry system
may utilize data from one or more other sensors and/or systems 208
in determining a position (e.g., distance, location, etc.) of the
vehicle 104 relative to a previously measured position for the
vehicle 104. Additionally or alternatively, the odometry sensors
may include one or more encoders, Hall speed sensors, and/or other
measurement sensors/devices configured to measure a wheel speed,
rotation, and/or number of revolutions made over time. Examples of
the odometry sensor/system as described herein may include, but are
not limited to, at least one of Infineon TLE4924/26/27/28C
high-performance speed sensors, Infineon TL4941plusC(B) single chip
differential Hall wheel-speed sensors, Infineon TL5041plusC Giant
Mangnetoresistance (GMR) effect sensors, Infineon TL family of
magnetic sensors, EPC Model 25SP Accu-CoderPro.RTM. incremental
shaft encoders, EPC Model 30M compact incremental encoders with
advanced magnetic sensing and signal processing technology, EPC
Model 925 absolute shaft encoders, EPC Model 958 absolute shaft
encoders, EPC Model MA36S/MA63S/SA36S absolute shaft encoders,
Dynapar.RTM., F18 commutating optical encoder, Dynapar.RTM. HS35R
family of phased array encoder sensors, other industry-equivalent
odometry sensors and/or systems, and may perform change in position
detection and/or determination functions using any known or
future-developed standard and/or architecture.
[0072] The vehicle 104 can include other optional sensors, which
can supplement the visual sensors described above. For example, a
LIDAR sensor/system may include one or more components configured
to measure distances to targets using laser illumination. In some
embodiments, the LIDAR sensor/system may provide 3D imaging data of
an environment around the vehicle 104. The imaging data may be
processed to generate a full 360-degree view of the environment
around the vehicle 104. The LIDAR sensor/system may include a laser
light generator configured to generate a plurality of target
illumination laser beams (e.g., laser light channels). In some
embodiments, this plurality of laser beams may be aimed at, or
directed to, a rotating reflective surface (e.g., a mirror) and
guided outwardly from the LIDAR sensor/system into a measurement
environment. The rotating reflective surface may be configured to
continually rotate 360 degrees about an axis, such that the
plurality of laser beams is directed in a full 360-degree range
around the vehicle 104. A photodiode receiver of the LIDAR
sensor/system may detect when light from the plurality of laser
beams emitted into the measurement environment returns (e.g.,
reflected echo) to the LIDAR sensor/system. The LIDAR sensor/system
may calculate, based on a time associated with the emission of
light to the detected return of light, a distance from the vehicle
104 to the illuminated target. In some embodiments, the LIDAR
sensor/system may generate over 2.0 million points per second and
have an effective operational range of at least 100 meters.
Examples of the LIDAR sensor/system as described herein may
include, but are not limited to, at least one of Velodyne.R.RTM.
LiDAR.RTM. HDL-64E 64-channel LIDAR sensors, Velodyne.RTM.
LiDAR.RTM. HDL-32E 32-channel LIDAR sensors, Velodyne.RTM.
LiDAR.RTM. PUCK.RTM. VLP-16 16-channel LIDAR sensors, Leica
Geosystems Pegasus: Two mobile sensor platform, Garmin.RTM.
LIDAR-Lite v3 measurement sensor, Quanergy M8 LiDAR sensors,
Quanergy S3 solid state LiDAR sensor, LeddarTech.RTM. LeddarVU
compact solid state fixed-beam LIDAR sensors, other
industry-equivalent LIDAR sensors and/or systems, and may perform
illuminated target and/or obstacle detection in an environment
around the vehicle 104 using any known or future-developed standard
and/or architecture.
[0073] A RADAR sensors may include one or more radio components
that are configured to detect objects/targets in an environment of
the vehicle 104. In some embodiments, the RADAR sensors may
determine a distance, position, and/or movement vector (e.g.,
angle, speed, etc.) associated with a target over time. The RADAR
sensors may include a transmitter configured to generate and emit
electromagnetic waves (e.g., radio, microwaves, etc.) and a
receiver configured to detect returned electromagnetic waves. In
some embodiments, the RADAR sensors may include at least one
processor configured to interpret the returned electromagnetic
waves and determine locational properties of targets. Examples of
the RADAR sensors as described herein may include, but are not
limited to, at least one of Infineon BASIC.RTM. RTN7735PL
transmitter and RRN7745PL/46PL receiver sensors, Autoliv ASP
Vehicle RADAR sensors, Delphi L2C0051TR 77 GHz ESR Electronically
Scanning Radar sensors, Fujitsu Ten Ltd. Automotive Compact 77 GHz
3D Electronic Scan Millimeter Wave Radar sensors, other
industry-equivalent RADAR sensors and/or systems, and may perform
radio target and/or obstacle detection in an environment around the
vehicle 104 using any known or future-developed standard and/or
architecture.
[0074] The ultrasonic sensors may include one or more components
that are configured to detect objects/targets in an environment of
the vehicle 104. In some embodiments, the ultrasonic sensors may
determine a distance, position, and/or movement vector (e.g.,
angle, speed, etc.) associated with a target over time. The
ultrasonic sensors may include an ultrasonic transmitter and
receiver, or transceiver, configured to generate and emit
ultrasound waves and interpret returned echoes of those waves. In
some embodiments, the ultrasonic sensors may include at least one
processor configured to interpret the returned ultrasonic waves and
determine locational properties of targets. Examples of the
ultrasonic sensors as described herein may include, but are not
limited to, at least one of Texas Instruments TIDA-00151 automotive
ultrasonic sensor interface IC sensors, MaxBotix.RTM. MB8450
ultrasonic proximity sensor, MaxBotix.RTM. ParkSonar.RTM.-EZ
ultrasonic proximity sensors, Murata Electronics MA40H1S-R
open-structure ultrasonic sensors, Murata Electronics MA40S4R/S
open-structure ultrasonic sensors, Murata Electronics MA58MF14-7N
waterproof ultrasonic sensors, other industry-equivalent ultrasonic
sensors and/or systems, and may perform ultrasonic target and/or
obstacle detection in an environment around the vehicle 104 using
any known or future-developed standard and/or architecture.
[0075] In some embodiments, the driving vehicle sensors may include
other sensors and/or combinations of the sensors described above.
Additionally or alternatively, one or more of the sensors described
above may include one or more processors configured to process
and/or interpret signals detected by the one or more sensors. In
some embodiments, the processing of at least some sensor
information provided by the vehicle sensors and systems 208 may be
processed by at least one sensor processor. Raw and/or processed
sensor data may be stored in a sensor data memory storage medium.
In some embodiments, the sensor data memory may store instructions
used by the sensor processor 204 for processing sensor information
provided by the sensors and systems 208. In any event, the sensor
data memory may be a disk drive, optical storage device,
solid-state storage device such as a random access memory ("RAM")
and/or a read-only memory ("ROM"), which can be programmable,
flash-updateable, and/or the like.
[0076] The navigation system 212 may include one or more sensors
having receivers and antennas that are configured to utilize a
satellite-based navigation system including a network of navigation
satellites capable of providing geolocation and time information to
at least one component of the vehicle 104. For instance, the
navigation system 212 may receive global positioning, location,
and/or navigational information from a navigation source. In some
embodiments, the navigation source may be a global navigation
satellite system (GNSS) similar, if not identical, to NAVSTAR GPS,
GLONASS, EU Galileo, and/or the BeiDou Navigation Satellite System
(BDS), etc.
[0077] Examples of the navigation sensor as described herein may
include, but are not limited to, at least one of Garmin.RTM.
GLO.RTM. family of GPS and GLONASS combination sensors, Garmin.RTM.
GPS 15x.RTM. family of sensors, Garmin.RTM. GPS 16x.RTM. family of
sensors with high-sensitivity receiver and antenna, Garmin.RTM. GPS
18x OEM family of high-sensitivity GPS sensors, Dewetron DEWE-VGPS
series of GPS sensors, GlobalSat 1-Hz series of GPS sensors, other
industry-equivalent navigation sensors and/or systems, and may
perform navigational and/or geolocation functions using any known
or future-developed standard and/or architecture.
[0078] The vehicle control systems may include separate systems for
steering 236, braking 240, acceleration 244, environmental control
248, infotainment 252, and/or other systems. There may be more or
fewer systems than those shown in FIG. 2A, as represented by
ellipses 254. The vehicle control systems 236-252 may receive
processed CAN Bus signals representing commands from the computing
system 204 and control an aspect of the vehicle 104 in response to
those commands. Further, controlling an aspect of the vehicle 104
may include presenting information via one or more display devices
associated with the vehicle, sending commands to one or more
computing devices associated with the vehicle, and/or controlling a
driving operation of the vehicle 104. The vehicle control systems
236-252 may correspond to one or more computing systems that
control driving operations of the vehicle 104 in accordance with
the Levels of driving autonomy described above. The acceleration
system 244 may operate a speed of the vehicle 104 by controlling an
output signal to the accelerator and/or braking system 240 of the
vehicle. The steering system 236 may additionally control steering
and/or other driving functions of the vehicle 104.
[0079] The vehicle control systems 236-252 may communicate, in
real-time, with the driving sensors and systems 204, 208 forming a
feedback loop. In particular, upon receiving information describing
a condition of targets in the environment surrounding the vehicle
104, the computing system 204 may autonomously make changes to a
driving operation of the vehicle 104. The computing system 204 may
then send subsequent sensor information describing any change to
the condition of the targets detected in the environment as a
result of the changes made to the driving operation. This continual
cycle of observation (e.g., via the sensors, etc.) and action
(e.g., selected control or non-control of vehicle operations, etc.)
allows the vehicle 104 to operate autonomously in the
environment.
[0080] In some embodiments, the computing system 204 may receive
control information, e.g., model(s) 220, from one or more control
sources, e.g., server 122. The control source 122 may provide
vehicle control information including models 220 that direct
autonomous driving control commands, vehicle operation override
control commands, and the like. The control source 122 may
correspond to an autonomous vehicle control system, a traffic
control system, an administrative control entity, and/or some other
controlling server. It is an aspect of the present disclosure that
the vehicle control system and/or other components of the vehicle
104 may exchange communications, for example, image data 216 saved
from camera or other visual sensors, with the control source 122
across the communication network and via the communications
subsystem.
[0081] Information associated with controlling driving operations
of the vehicle 104 may be stored in a control data memory storage
medium. The control data memory may store models, used by the
computing system 204 for controlling driving operations of the
vehicle 104, historical control information, autonomous driving
control rules, and the like in a data store 220. In some
embodiments, the control data memory may be a disk drive, optical
storage device, solid-state storage device such as a random access
memory ("RAM") and/or a read-only memory ("ROM"), which can be
programmable, flash-updateable, and/or the like.
[0082] In addition to the mechanical components described herein,
the vehicle 104 may include a number of user interface devices. The
user interface devices receive and translate human input into a
mechanical movement or electrical signal or stimulus. The human
input may be one or more of motion (e.g., body movement, body part
movement, in two-dimensional or three-dimensional space, etc.),
voice, touch, and/or physical interaction with the components of
the vehicle 104. In some embodiments, the human input may be
configured to control one or more functions of the vehicle 104
and/or systems of the vehicle 104 described herein. User interfaces
may include, but are in no way limited to, at least one graphical
user interface of a display device, steering wheel or mechanism,
transmission lever or button (e.g., including park, neutral,
reverse, and/or drive positions, etc.), throttle control pedal or
mechanism, brake control pedal or mechanism, power control switch,
communications equipment, etc.
[0083] The communications componentry 256 can include one or more
wired or wireless devices such as a transceiver(s) and/or modem
that allows communications not only between the various systems
disclosed herein but also with other devices, such as devices on a
network, and/or on a distributed network such as the Internet
and/or in the cloud and/or with other vehicle(s).
[0084] The communications interface 256 and/or interface 228 can
also include inter- and intra-vehicle communications capabilities
such as hotspot and/or access point connectivity for any one or
more of the vehicle occupants and/or vehicle-to-vehicle
communications. Additionally, and while not specifically
illustrated, the communications interface 256 and/or the interface
228 can include one or more communications links (that can be wired
or wireless) and/or communications busses, including one or more of
CANbus, OBD-II, ARCINC 429, Byteflight, CAN (Controller Area
Network), D2B (Domestic Digital Bus), FlexRay, DC-BUS, IDB-1394,
IEBus, I2C, ISO 9141-1/-2, J1708, J1587, J1850, J1939, ISO 11783,
Keyword Protocol 2000, LIN (Local Interconnect Network), MOST
(Media Oriented Systems Transport), Multifunction Vehicle Bus,
SMARTwireX, SPI, VAN (Vehicle Area Network), and the like or in
general any communications protocol and/or standard(s).
[0085] The various protocols and communications can be communicated
one or more of wirelessly and/or over transmission media such as
single wire, twisted pair, fiber optic, IEEE 1394, MIL-STD-1553,
MIL-STD-1773, power-line communication, or the like. (All of the
above standards and protocols are incorporated herein by reference
in their entirety.) As discussed, the communications interface 256
enables communications between any if the inter-vehicle systems and
subsystems as well as communications with non-collocated resources,
such as those reachable over a network such as the Internet.
[0086] The communications interface 256, in addition to well-known
componentry (which has been omitted for clarity), can include
interconnected elements including one or more of, but not limited
to: one or more antennas, an interleaver/deinterleaver, an analog
front end (AFE), memory/storage/cache, controller/microprocessor,
MAC circuitry, modulator/demodulator, encoder/decoder, a plurality
of connectivity managers, GPU, accelerator, a
multiplexer/demultiplexer, transmitter, receiver and wireless radio
components such as a Wi-Fi PHY/Bluetooth.RTM. module, a Wi-Fi/BT
MAC module, transmitter and receiver. The various elements in the
communications interface 256 are connected by one or more
links/busses.
[0087] The communications interface 256 can have one more antennas,
for use in wireless communications such as multi-input multi-output
(MIMO) communications, multi-user multi-input multi-output
(MU-MIMO) communications Bluetooth.RTM., LTE, 4G, 5G, Near-Field
Communication (NFC), etc., and in general for any type of wireless
communications. The antenna(s) can include, but are not limited to
one or more of directional antennas, omnidirectional antennas,
monopoles, patch antennas, loop antennas, microstrip antennas,
dipoles, and any other antenna(s) suitable for communication
transmission/reception. In an exemplary embodiment,
transmission/reception using MIMO may require particular antenna
spacing. In another exemplary embodiment, MIMO
transmission/reception can enable spatial diversity allowing for
different channel characteristics at each of the antennas. In yet
another embodiment, MIMO transmission/reception can be used to
distribute resources to multiple users for example within the
vehicle 104 and/or in another vehicle.
[0088] Antenna(s) generally interact with the Analog Front End
(AFE), which is needed to enable the correct processing of the
received modulated signal and signal conditioning for a transmitted
signal. The AFE can be functionally located between the antenna and
a digital baseband system in order to convert the analog signal
into a digital signal for processing and vice-versa.
[0089] The communications interface 256 can also include a
controller/microprocessor and a memory/storage/cache. The
communications interface 256 can interact with the
memory/storage/cache which may store information and operations
necessary for configuring and transmitting or receiving the
information described herein. The memory/storage/cache may also be
used in connection with the execution of application programming or
instructions by the controller/microprocessor, and for temporary or
long term storage of program instructions and/or data. As examples,
the memory/storage/cache may comprise a computer-readable device,
RAM, ROM, DRAM, SDRAM, and/or other storage device(s) and
media.
[0090] The controller/microprocessor may comprise a general purpose
programmable processor or controller for executing application
programming or instructions related to the communications interface
256. Furthermore, the controller/microprocessor can perform
operations for configuring and transmitting/receiving information
as described herein. The controller/microprocessor may include
multiple processor cores, and/or implement multiple virtual
processors. Optionally, the controller/microprocessor may include
multiple physical processors. By way of example, the
controller/microprocessor may comprise a specially configured
Application Specific Integrated Circuit (ASIC) or other integrated
circuit, a digital signal processor(s), a controller, a hardwired
electronic or logic circuit, a programmable logic device or gate
array, a special purpose computer, or the like.
[0091] The communications interface 256 can further include a
transmitter and receiver which can transmit and receive signals,
respectively, to and from other devices, subsystems and/or other
destinations using the one or more antennas and/or links/busses.
Included in the communications interface 256 circuitry is the
medium access control or MAC Circuitry. MAC circuitry provides for
controlling access to the wireless medium. In an exemplary
embodiment, the MAC circuitry may be arranged to contend for the
wireless medium and configure frames or packets for communicating
over the wired/wireless medium.
[0092] The communications interface 256 can also optionally contain
a security module (not shown). This security module can contain
information regarding but not limited to, security parameters
required to connect the device to one or more other devices or
other available network(s), and can include WEP or WPA/WPA-2
(optionally+AES and/or TKIP) security access keys, network keys,
etc. The WEP security access key is a security password used by
Wi-Fi networks. Knowledge of this code can enable a wireless device
to exchange information with an access point and/or another device.
The information exchange can occur through encoded messages with
the WEP access code often being chosen by the network
administrator. WPA is an added security standard that is also used
in conjunction with network connectivity with stronger encryption
than WEP.
[0093] In some embodiments, the communications interface 256 also
includes a GPU, an accelerator, a Wi-Fi/BT/BLE PHY module and a
Wi-Fi/BT/BLE MAC module and wireless transmitter 588 and receiver
592. In some embodiments, the GPU may be a graphics processing
unit, or visual processing unit, comprising at least one circuit
and/or chip that manipulates and changes memory to accelerate the
creation of images in a frame buffer for output to at least one
display device. The GPU may include one or more of a display device
connection port, printed circuit board (PCB), a GPU chip, a
metal-oxide-semiconductor field-effect transistor (MOSFET), memory
(e.g., single data rate random-access memory (SDRAM), double data
rate random-access memory (DDR) RAM, etc., and/or combinations
thereof), a secondary processing chip (e.g., handling video out
capabilities, processing, and/or other functions in addition to the
GPU chip, etc.), a capacitor, heatsink, temperature control or
cooling fan, motherboard connection, shielding, and the like.
[0094] In accordance with one exemplary embodiment, any of the
communications discussed herein can be communicated via the
conductor(s) used for charging. One exemplary protocol usable for
these communications is Power-line communication (PLC). PLC is a
communication protocol that uses electrical wiring to
simultaneously carry both data, and Alternating Current (AC)
electric power transmission or electric power distribution. It is
also known as power-line carrier, power-line digital subscriber
line (PDSL), mains communication, power-line telecommunications, or
power-line networking (PLN). For DC environments in vehicles PLC
can be used in conjunction with CAN-bus, LIN-bus over power line
(DC-LIN) and DC-BUS.
[0095] The communications subsystem can also optionally manage one
or more identifiers, such as an IP (internet protocol) address(es),
associated with the vehicle and one or other system or subsystems
or components therein. These identifiers can be used in conjunction
with any one or more of the connectivity managers as discussed
herein.
[0096] The data stores 216-224 may be any type of hardware/software
as described in conjunction with FIGS. 2C and/or 2D that can form a
database or other system for writing, storing, and/or reading data.
The databases 216-224 can be stored in a single database as
separate partitions or portions or may be discrete data stores,
possible with separate hardware. The image data store 216 can store
one or more images generated from the image sensors described
above. The image data can include an entire frame of image data or
a portion of image data. Further, the image data may also include
one or more items of metadata associated with the image data,
including, but not limited to, a time and/or date the image was
taken, an identifier and/or location for the sensor that captured
the image, the type of sensor that captured the sensor, a reason
for capturing an image (e.g., an incident occurred that required
user interface), etc. The image data 216 may persist for some
period of time or event and may be shared with the server 122 to
improve model accuracy.
[0097] The model(s) 220 data store includes one or more models used
by the computing system 204 to autonomously operate the vehicle
104. The model(s) 220 and their functionality may be as described
hereinafter. One or more model(s) 220 may be stored in the model(s)
data store 220 and variously retrieved and implemented by the
computing system 204 based on the conditions of the environment
100.
[0098] User information 224 can be stored in a separate database.
The user information 224 can include user profiles, data about
vehicle use, or other informational data. The user profiles can
include information about a user identity, user experiences in the
vehicle, data about the user's driving style or habits, a user's
calendar or other personal information, etc. This information may
be used to customize the experience of the user with the vehicle
104.
[0099] FIG. 2B represents a hardware/software configuration for the
server 122 or cloud computing function of the system. The server
122 can be any computing system as described in conjunction with
FIGS. 2C and/or 2D. The communications system 206 may the same as
or similar to the communication interface 256, as described in
conjunction with FIG. 2A. As such, these components will not be
described further. The model(s) data store 222 may be similar to
the model(s) data store 220, but may store more models and may
provide models to different vehicles 104 based on those vehicle's
circumstances or conditions. The image test data 218 can be the
accumulation of all image data 216 from the one or more vehicles
104 provided to the server 122 to create or refine the model(s)
222. The image test data 218 is explained hereinafter.
[0100] FIG. 2C shows a computing environment 256 that may function
as the servers, user computers, or other systems provided and
described herein, in accordance with embodiments of the present
disclosure. The computing environment 256 includes one or more user
computers, or computing devices, such as a vehicle computing device
204, a communication device 266, and/or other devices, as
represented by ellipses 262. The computing devices 204, 266, 258
may include general purpose personal computers (including, merely
by way of example, personal computers, and/or laptop computers
running various versions of Microsoft Corp.'s Windows.RTM. and/or
Apple Corp.'s Macintosh.RTM. operating systems) and/or workstation
computers running any of a variety of commercially-available
UNIX.RTM. or UNIX-like operating systems. These computing devices
204, 266, 258 may also have any of a variety of applications,
including for example, database client and/or server applications,
and web browser applications. Alternatively, the computing devices
204, 266, 258 may be any other electronic device, such as a
thin-client computer, Internet-enabled mobile telephone, and/or
personal digital assistant, capable of communicating via a network
260 and/or displaying and navigating web pages or other types of
electronic documents. Although the exemplary computing environment
256 is shown with two computing devices, any number of user
computers or computing devices may be supported.
[0101] The computing environment 256 may also include one or more
servers 122, 262. In this example, server 262 is shown as a web
server and server 122 is shown as an application server. The web
server 262, which may be used to process requests for web pages or
other electronic documents from computing devices 204, 266, and
258. The web server 262 can be running an operating system
including any of those discussed above, as well as any
commercially-available server operating systems. The web server 262
can also run a variety of server applications, including SIP
(Session Initiation Protocol) servers, HTTP(s) servers, FTP
servers, CGI servers, database servers, Java servers, and the like.
In some instances, the web server 262 may publish operations
available operations as one or more web services.
[0102] The computing environment 256 may also include one or more
file and or/application servers 122, which can, in addition to an
operating system, include one or more applications accessible by a
client running on one or more of the computing devices 204, 266,
258. In at least some configurations, the application server 122
can provide models to the vehicles 104 and/or receive image data to
update the models. The server(s) 122 and/or 262 may be one or more
general purpose computers capable of executing programs or scripts
in response to the computing devices 204, 266, 258. As one example,
the server 122, 262 may execute one or more web applications. The
web application may be implemented as one or more scripts or
programs written in any programming language, such as Java.RTM., C,
C#.RTM., or C++, and/or any scripting language, such as Perl,
Python, or TCL, as well as combinations of any
programming/scripting languages. The application server(s) 122 may
also include database servers, including without limitation those
commercially available from Oracle.RTM., Microsoft.RTM.,
Sybase.RTM., IBM.RTM. and the like, which can process requests from
database clients running on a computing device 204, 262, 258.
[0103] The web pages created by the server 262 and/or 122 may be
forwarded to a computing device 204, 262, and 258 via a web (file)
server 262, 122. Similarly, the web server 262 may be able to
receive web page requests, web services invocations, and/or input
data from a computing device 204, 262, 258 (e.g., a user computer,
etc.) and can forward the web page requests and/or input data to
the web (application) server 122. In further embodiments, the
server 122 may function as a file server. Although for ease of
description, FIG. 2C illustrates a separate web server 262 and
file/application server 122, those skilled in the art will
recognize that the functions described with respect to servers 262,
122 may be performed by a single server and/or a plurality of
specialized servers, depending on implementation-specific needs and
parameters. The computer systems 204, 262, 258, web (file) server
262 and/or web (application) server 122 may function as the system,
devices, or components described in FIGS. 1-2D.
[0104] The computing environment 256 may also include a database
264. The database 264 may reside in a variety of locations. By way
of example, database 264 may reside on a storage medium local to
(and/or resident in) one or more of the computers 204, 262, 258,
262, 122. Alternatively, it may be remote from any or all of the
computers 204, 262, 258, 262, 122, and in communication (e.g., via
the network 610) with one or more of these. The database 264 may
reside in a storage-area network ("SAN") familiar to those skilled
in the art. Similarly, any necessary files for performing the
functions attributed to the computers 204, 262, 258, 266, 122 may
be stored locally on the respective computer and/or remotely, as
appropriate. The database 264 may be a relational database, such as
Oracle 20i.RTM., that is adapted to store, update, and retrieve
data in response to SQL-formatted commands. Database 264 may
represent databases and/or data stores 216, 218, 220, 222, and/or
224.
[0105] FIG. 2D illustrates one embodiment of a computer system 268
upon which the servers 122, 262, user computers 204, computing
devices, or other systems or components described above may be
deployed or executed. The computer system 268 is shown comprising
hardware elements that may be electrically coupled via a bus 270.
The hardware elements may include one or more central processing
units (CPUs) 272; one or more input devices 274 (e.g., a mouse, a
keyboard, etc.); and one or more output devices 276 (e.g., a
display device, a printer, etc.). The computer system 268 may also
include one or more storage devices 278. By way of example, storage
device(s) 278 may be disk drives, optical storage devices,
solid-state storage devices such as a random access memory ("RAM")
and/or a read-only memory ("ROM"), which can be programmable,
flash-updateable and/or the like.
[0106] The computer system 268 may additionally include a
computer-readable storage media/reader 280; a communications system
282 (e.g., a modem, a network card (wireless or wired), an
infra-red communication device, etc.); and working memory 286,
which may include RAM and ROM devices as described above. The
computer system 268 may also include a processing acceleration unit
284, which can include a digital signal processor (DSP), a
special-purpose processor, and/or the like.
[0107] The computer-readable storage media/reader 280 can further
be connected to a computer-readable storage medium, together (and,
optionally, in combination with storage device(s) 278)
comprehensively representing remote, local, fixed, and/or removable
storage devices plus storage media for temporarily and/or more
permanently containing computer-readable information. The
communications system 282 may permit data to be exchanged with a
network and/or any other computer described above with respect to
the computer environments described herein. Moreover, as disclosed
herein, the term "storage medium" may represent one or more devices
for storing data, including read only memory (ROM), random access
memory (RAM), magnetic RAM, core memory, magnetic disk storage
mediums, optical storage mediums, flash memory devices and/or other
machine readable mediums for storing information.
[0108] The computer system 268 may also comprise software elements,
shown as being currently located within a working memory 286,
including an operating system 288 and/or other code 290. It should
be appreciated that alternate embodiments of a computer system 268
may have numerous variations from that described above. For
example, customized hardware might also be used and/or particular
elements might be implemented in hardware, software (including
portable software, such as applets), or both. Further, connection
to other computing devices such as network input/output devices may
be employed.
[0109] Examples of the processors 272 as described herein may
include, but are not limited to, at least one of Qualcomm.RTM.
Snapdragon.RTM. 800 and 801, Qualcomm.RTM. Snapdragon.RTM. 620 and
615 with 4G LTE Integration and 64-bit computing, Apple.RTM. A7
processor with 64-bit architecture, Apple.RTM. M7 motion
coprocessors, Samsung.RTM. Exynos.RTM. series, the Intel.RTM.
Core.RTM. family of processors, the Intel.RTM. Xeon.RTM. family of
processors, the Intel.RTM. Atom.RTM. family of processors, the
Intel Itanium.RTM. family of processors, Intel.RTM. Core.RTM.
i5-4670K and i7-4770K 22 nm Haswell, Intel.RTM. Core.RTM. i5-3570K
22 nm Ivy Bridge, the AMD.RTM. FX.RTM. family of processors,
AMD.RTM. FX-4300, FX-6300, and FX-8350 32 nm Vishera, AMD.RTM.
Kaveri processors, Texas Instruments.RTM. Jacinto C6000.RTM.
automotive infotainment processors, Texas Instruments.RTM.
OMAP.RTM. automotive-grade mobile processors, ARM.RTM.
Cortex.RTM.-M processors, ARM.RTM.. Cortex-A and ARM926EJ-S.RTM.
processors, other industry-equivalent processors, and may perform
computational functions using any known or future-developed
standard, instruction set, libraries, and/or architecture.
[0110] An embodiment of software structure 300, 308, 336 that may
be stored within the working memory 286 of the vehicle 104 may be
as shown in FIGS. 3A and/or 3B and/or 3C. In at least some
configurations, the software structures 300, 308, 336 are comprised
of one or more software nodes 304. In some examples, the one or
more software nodes 304 may include, but are not limited to one or
more robot operating system (ROS) nodes 304. The nodes 304 depicted
in FIGS. 3A, 3B, and 3C may be a middleware that allows for
software development, which provides services designed for
heterogeneous computer cluster used for hardware abstraction,
low-level device control, implementation of commonly used
functionality, message-passing between processes, and package
management. Node based processes are represented in a graph
architecture where processing takes place in nodes that may
receive, post and multiplex sensor, control, state, planning,
actuator and other messages. In some examples, a node may post or
otherwise provide information, such as an image, processed image,
extracted features, a message indicating that an image, processed
image, extracted features are available, a location to such
information, etc. to a common, private, or otherwise shared
communication pathway; one or more other nodes may then receive,
and/or retrieve information associated with the posted information.
The nodes can be used to implement the CNN processes described
hereinafter.
[0111] The software structure 300, 308, 336 can include an
arrangement of nodes 304A-304M as a matrix, as shown in FIG. 3A.
The matrix of nodes 304A-304E allows exchange of information,
commands, etc. between various nodes until an output may be
generated. In other configurations, the nodes 304A-304G may be
arranged as a hierarchy, where a parent process, e.g., 304A,
completes a process with a result that is returned to a child
process, e.g., 304B and/or 304C. The processes are completed
node-by-node until an output is generated. The output can be a
command sent through the interface 228 and CAN Bus 232 to control a
system 236-252 of the vehicle 104. In some examples, the nodes may
be arranged as a combination of a matrix and a hierarchical
organizational structure and may operate in a parallel manner.
[0112] FIG. 3C depicts additional details of an example message 320
that may be communicated from a node 304 to another node 304. For
example, a first node 304A may perform a process and generate an
output, status, or otherwise to be communicated to another node,
for example node 304B and 304C. Thus, the first node 304A may post
a message to a common information pathway, such as a shared bus,
where the message 320 may include a node identification 324, data
328, and a timestamp 332. The node identification 324 may include a
unique identifier specific to the node 304A or may include a unique
identifier specific to a type of information (for example,
processed image information, lane markings, distance etc.) relevant
to one or more other nodes 304. Accordingly, the one or more other
nodes, for example 304B and 304C, may subscribe or otherwise be
configured to identify the node identification 304A and then
receive the message posted by the first node 304A. Accordingly, the
nodes 304B and 304C may operate on, retrieve, or otherwise process
information in the data portion 328 of the message 320. In some
instances, the timestamp 332 may be relevant to a receiving node in
that the receiving node may determine whether such information is
relevant or desired for a given period of time. For example, node
304B may require processed image information once a second while
node 304C may require processed image information once every five
milliseconds.
[0113] Further depicted in FIG. 3C is an example of node structure
336 for processing one or more images and generating an output(s)
to control a vehicle, such as vehicle 104. More specifically, a
first node 304A may be configured to acquire an image or otherwise
is associated with a sensor 208 to acquire data, such as an image,
and make such data available to one or more nodes in the node
structure 336. In at least one example, the first node 304A may
post a message, such as message 320, indicating that image data is
available. In another example, the first node 304A may include the
image data in the message. Accordingly, second and third nodes 304B
and 304C, having subscribed to the first node 304A and/or having
subscribed to a specified data type or category of information, may
receive the message provided by the first node 304A and proceed to
obtain, by retrieving for instance, image information. Accordingly,
the second node 304B may correspond to an object identifier or
object detector configured to identify/detect and then output
information associated with one or more objects (such as a location
of the object and object information (size, distance, category,
type etc.) from the image provided by the first node 304A. The
object detector may detect and classify all objects in an image,
such as different types of vehicles, different road signs,
inanimate and moving objects, buildings, etc. Each identified
object may be associated with an object ID, where the object ID may
be made accessible to one or more nodes. In accordance with at
least one example, a fourth node 304D may be configured to track
one or more objects provided by node 304B. Thus, the fourth node
304D may provide access to one or more objects, one or more object
tracks, locations of objects, etc. For example, an object, such as
a vehicle, detected in a first image may be tracked via second,
third, and fourth images, where the images may correspond to or
otherwise be associated with one or more frames of video. Based on
a type of enacted autonomous driving mode (e.g., following a
vehicle, following a line, or following a GPS track), a heading may
be calculated at a sixth node 304F. In some examples, a heading for
each mode may be computed in parallel; that is, the sixth node 304F
may include a plurality of nodes, where one or more of the
plurality of nodes are utilized to determine a heading.
[0114] In accordance with at least one example, the third node 304C
may correspond to a lane detector and may be configured to detect
or find one or more lane markers from the image made available by
the first node 304A; the third node 304C may output or otherwise
make accessible lane marker information. The fifth node 304E may
utilize the lane marker information to determine a heading for the
vehicle 104, where the heading from the fifth node 304E and the
sixth node 304F may be provided to or otherwise made accessible to
the seventh node 304G. The seventh node 304G may determine a path,
or otherwise select a path based on a follow mode, for the vehicle
based on the headings determined at the fifth and sixth nodes 304E
and 304F; in some instances, an external input provided by an
eighth node 304H may also be utilized when determining a path for
the vehicle 104 to follow. The path determined at the seventh node
304G may be made accessible to a controller to generate or
otherwise determine a steering angle correction to be made to the
vehicle at a ninth node 304I, where the steering angle correction
may be accessed by a tenth node 304J which may actuate a steering
angle change.
[0115] In accordance with some examples of the present disclosure,
one or more of the lane detection information from the third node
304C, object identification information from the second node 304B,
tracking information from the fourth node 304D, heading information
from the sixth node 304F, path information from the seventh node
304G, and additional sensor information provided form an eleventh
node 304K may be accessed by a twelfth node 304L to determine
acceleration and/or braking for the vehicle 104. The determined
acceleration and/or braking may be accessed by the thirteenth node
304M which may then actuate a change in throttle position or an
amount of braking applied to the vehicle 104. While the software
structure 336 provides a general overview of how one or more nodes
may be utilized to control one or more autonomous aspects of a
vehicle, it should be appreciated that additional nodes and/or
fewer nodes may be utilized. Moreover, the outputs and/or
determinations of each node should not be considered limiting;
rather each node may provide additional or fewer outputs or
determinations than what has been described.
[0116] Accordingly, the second node 304B may correspond to an
object tracker configured to identify and then output one or more
objects (such as a location of the object and object information
(size, distance, category, type etc.) from the image provided by
the first node 304A.
[0117] FIG. 4 depicts an autonomous model generation system 400 in
accordance with embodiments of the present disclosure. In
embodiments, the autonomous model generation system 400, may
include an autonomous vehicle model generation system 416, which
may receive one or more images from one or more fleet vehicles
404A-B and/or fleet vehicle 408, process the one or more images,
and generate an autonomous model. In some embodiments, the
generated autonomous model may be specific to the fleet vehicle
404A-B and/or fleet vehicle 408, a user, and/or one or more
environmental parameters, such as location, weather, time of day,
etc.
[0118] In accordance with embodiments of the present disclosure,
the autonomous vehicle model generation system 416 may include one
or more interfaces 420 including interfaces interface 424A-D, for
example, for receiving one or more images from a fleet vehicle 404
and/or 408. In some embodiments, the autonomous vehicle model
generation system 416 may be a portal that provides functionalities
for a point of access on the web or Internet. That is, as will be
described below, an autonomous vehicle model generation system 416
may provide centralized management capabilities for autonomous
vehicle model generation and distribution. Accordingly, user of a
fleet vehicle 404 and/or 408, an autonomous vehicle model manager,
and/or an autonomous vehicle model custodian may interact with one
or more of the interfaces 424A-424D, to provide and receive images,
updated models, new models and the like.
[0119] In accordance with embodiments of the present disclosure,
the autonomous vehicle model generation system 416 may receive one
or more images, for example images 412, from one or more of a fleet
vehicle 404A-B and/or a fleet vehicle 408. The images may be
captured by an image capture device, such as the previously
described camera for instance, and communicated to the autonomous
vehicle model generation system 416 via the communication network
407. The autonomous vehicle model generation system 416 may then
utilize the model manager 428 to obtain information about a current
autonomous model, for example autonomous vehicle model 452,
residing at the specified fleet vehicle 404A-B. The information
about the current autonomous model residing at the specified fleet
vehicle 404A-B may be an autonomous model itself, an identifier of
the autonomous model and/or data indicating one or more parameters
of the autonomous model and/or vehicle. In accordance with
embodiments of the present disclosure, the model manager 428 may
perform image processing, such as pre-processing on the one or more
received images from the fleet vehicle 404A.
[0120] In accordance with embodiments of the present disclosure,
the pre-processed and/or imaged processed data and/or information
may then be provided to the model generation module 436 which may
generate one or more autonomous vehicle models. The model manager
428 may access model information in the database 444, or otherwise,
such that a series of images may be utilized during the model
generation process. As one example, a convolutional neural network
may be utilized to create the autonomous vehicle model. The
autonomous vehicle model may then undergo a model verification
process at the model verification module 440 and be provided to the
model exchange 442 for distribution and/or storage to the model
verification module 440 and/or fleet vehicle 404A-B and/or fleet
vehicle 408. In accordance with embodiments of the present
disclosure, an autonomous vehicle model 452 may be distributed to
the fleet vehicle 404A-B and/or fleet vehicle 408; that is, a same
autonomous vehicle model 452 may be provided to each of the fleet
vehicle 404A-B and/or fleet vehicle 408. In addition, or
alternatively, a different autonomous vehicle model may be provided
to the fleet vehicle 404A, fleet vehicle 404B, and/or fleet vehicle
408. The autonomous vehicle model may be communicated to the fleet
vehicle utilizing the communication 448 and/or the communication
network 407.
[0121] As an example of an autonomous vehicle model that may be
generated by the model manager 428 and/or reside at a fleet vehicle
404 and/or 408, the autonomous vehicle model 452 may include one or
more image processing portions 456, convolutional neural network
portions 460, and/or the vehicle adjustment portions 464.
Accordingly, and as one example in accordance with embodiments of
the present disclosure, the autonomous vehicle model 452 may be
implemented at the model verification module 440 and/or the fleet
vehicle. Thus, the autonomous vehicle model 452 may receive one or
more images 412, process the images at the image processing portion
456, perform feature extraction and classification at the
convolutional neural network 460, and adjust one or more of a
steering angle and/or velocity utilizing the steering angle adjust
468 and velocity adjust 472 of the vehicle adjust portion 464.
[0122] In accordance with embodiments of the present disclosure,
the autonomous vehicle model generation system 416 may generate
autonomous vehicle models based on information specific to a
vehicle, information specific to a user, information specific to a
location, information specific to a time of day, information
specific to a time of year, information specific to a change in one
of the previously mentioned specific information types, and/or
combinations thereof. As one non-limiting example, the autonomous
vehicle model generation system 416 may generate an autonomous
vehicle model specific to a location, where the generated
autonomous vehicle model for the specific location may provide
classifications having a higher confidence than an autonomous
vehicle model suited to another different location. In accordance
with embodiments of the present disclosure, a generated autonomous
vehicle model may be an entire autonomous vehicle model, a portion
of a model, and/or a single node of the model.
[0123] FIG. 5 depicts additional details of the one or more
autonomous vehicle models that may reside at the fleet vehicle,
database 444, and/or be generated by the autonomous vehicle model
generation system 416. The model may include a machine learning
algorithm 530 which takes data, trains a model on the data, and
uses the trained model to make predictions on new data. At each
step, the model 530 makes predictions and gets feedback about how
accurate its generated predictions were. This feedback, which is
provided in terms of an error according to some measure (for
example distance from the correct solution), is used to correct the
errors made in a prediction. As depicted in FIG. 5, the model 530
may receive an image 512 and perform a convolution on the image
512. The primary purpose of convolution is to extract features from
the input image 512 while preserving the spatial relationship
between pixels by learning image features using small squares of
input data. For example, one or more filters, kernels, and/or
feature detectors 516 may be applied to the image 512; the image
512 may be a preprocessed image in some embodiments. For example,
the image 512 may correspond to an image that is a subsampled
representation of an original image. Moreover, the image 512 may be
an image from a series of images, such as video. The resulting
element 524 may be a single element of the entire convolved
feature, activation map, or feature map 522. As can be appreciated,
an autonomous vehicle model may include specific filters, or
feature detectors, 516, and utilize a varying number of strides to
obtain the feature map 522. Moreover, the feature detectors 516,
the number of features detectors, and other model parameters may be
specific to a fleet vehicle, user, time of day, location etc. In
addition, the resulting feature map 522 may be subsampled,
subjected to non-linear processing such as ReLU, and pooled
resulting in a rectified feature map 532 for example. In accordance
with embodiments of the present disclosure, the process of
subsampling and pooling may be performed multiple times.
[0124] As shown in FIG. 5, two sets of convolution, ReLU &
Pooling layers are depicted where the second convolution layer
performs convolution on the output of the first pooling layer using
six filters to produce a total of six feature maps. ReLU is then
applied individually on all of these six feature maps. A max
pooling operation may then be applied separately on each of the six
rectified feature maps. Together these layers extract the useful
features from the images, introduce non-linearity, and reduce
feature dimension while aiming to make the features somewhat
equivariant to scale and translation. The output of the 2nd Pooling
Layer acts as an input to the Fully Connected Layer 522.
[0125] The Fully Connected layer 522 may be a traditional Multi
Layer Perceptron that uses a classifier in the output layer, such
as a softmax activation function or a support vector machine (SVM).
The term "Fully Connected" implies that every neuron in the
previous layer is connected to every neuron on the next layer.
However, in some embodiments, the layer 522 may not be fully
connected. The output from the convolutional and pooling layers
represent high-level features of the input image. The purpose of
the Fully Connected layer is to use these features for classifying
the input image into various classes based on the training dataset.
As discussed above, the Convolution+Pooling layers act as Feature
Extractors from the input image while Fully Connected layer acts as
a classifier.
[0126] In accordance with embodiments of the present disclosure,
the CNN 522 and 556 may be trained in accordance with a defined
training process. Backpropagation may be used to generate gradients
of error with respect to weights within the network and such
gradients may be filtered to minimize the output error. A training
set of images may be used to train the autonomous vehicle model to
optimize all weights and parameters of the CNN to classify not only
features within images, but also other inputs, such as steering
angle, acceleration, and the like. Accordingly, when a new (unseen)
image and or other unseen inputs are provided to the autonomous
vehicle model, the autonomous vehicle model would go through
forward propagation and output 564a probability for each
classification for the image and an associated action based on the
classification in the image at 560.
[0127] As depicted in FIG. 6, additional details of the autonomous
vehicle model 452 are depicted. That is, the autonomous vehicle
model 452 may include one or more model identifiers 604, one or
more inputs 608, one or more outputs 612, and/or one or more
weightings 616. As previously discussed, each of the autonomous
vehicle models may be comprised of one or more node nodes. In some
examples, each node may be, or otherwise include a robot operating
system (ROS) node. Each of the nodes may be addressable in such
manner such that each node may be replaced, updated, modified,
changed, deleted, and/or added and such that one or more nodes may
communicate with one another.
[0128] FIG. 7 shows a method for autonomously driving a vehicle in
accordance with embodiments of the present disclosure. A general
order for the steps of the method 700 is shown in FIG. 7.
Generally, the method 700 starts with a start operation 704 and
ends with an end operation 732. The method 700 can include more or
fewer steps or can arrange the order of the steps differently than
those shown in FIG. 7. The method 700 can be executed as a set of
computer-executable instructions executed by a computer system and
encoded or stored on a computer readable medium. Further, the
method 700 can be performed by gates or circuits associated with a
processor, an application specific integrated circuit (ASIC), a
field programmable gate array (FPGA), a system-on-chip (SOC), or
other hardware device. Hereinafter, the method 700 shall be
explained with reference to the systems, components, devices,
modules, software, signals, data structures, interfaces, methods,
etc. described in conjunction with FIGS. 1-6 and 8-28.
[0129] The processor 204 can determine one or more conditions of
the driving environment, in step 708. The image processing node
executed by the processor 204 can receive sensor data, which can
include one or more images from the image sensors 208. The sensor
data can be an image, other sensor data, and/or image and other
sensor data associated with the driving environment of the vehicle.
The image processing node, executed by the processor 204, which
implements a machine learning (ML) model in a convolutional neural
network (CNN), may process the image. Within the image, different
edges, the position of those edges, and other information can be
determined. Conditions can include an edge or other information,
such as movement of an edge over time, location of other sensed
data, etc.
[0130] From the conditions, an object detection node, executed by
the processor 204, may identify and determine one or more objects
within the image and within the driving environment, in step 712.
For example, as shown in FIG. 22A, one or more objects 2204-2220
may exist in the image 2200 provided by the image sensor. The ML
model deployed by the object detection node can determine what
items are objects (items of interest in the image 2200). The shape
of the object can inform the processor 204 as to what type of
object is being sensed. Thus, the object detection node can
determine that object 2204 is a stop sign, objects 2208 and 2220
are trees, and objects 2212 and 2216 are vehicles. This information
may then be stored by the processor 204 for determining actions
required for driving in the environment 100.
[0131] Detecting an object may be a multipart process. After rough
objects are identified, the object detection node can automatically
draw a box over the object(s) in the image. For example, the object
detection node may draw boxes 2206, 2210, 2214, 2218, and 2222
around objects 2204-2220. The image 2200 with only the boxes
showing the location of the objects may be as shown in FIG. 22B.
The operation shown in FIG. 22B may actually occur before
identifying the objects in each box 2206-2222, which may be as
shown in FIG. 22A. The ML model may then identify the object in
each of the boxes, including identifying object 2216 in box 2218 as
a second vehicle.
[0132] As shown in FIG. 22A, the boxes 2206-2220 can mimic the size
of the object both in height and width in the two-dimensional image
2200. Thus, the boxes 2206-2220 delineate the extents (e.g., the
dimensions) of the object within the image. As shown in FIG. 22C,
the box 2218 for object 2216 can provide the width 2228 and/or
height 2232 of the second vehicle in the image 2200, which may be
measured in pixels in the image 2200. Further, the object detection
node can determine a center 2236 of the box, and thus, the center
of the object 2216 in the image 2200. The location of the center
2236, in the image 2200, may be stored.
[0133] The object detection node executed by the processor 204 can
then determine if one of the objects, in view in the image 2200 is
a second or another vehicle 2212, 2216, in step 716. For example,
the processor 204 can determine if vehicle 124 is being seen by the
sensors 208 of vehicle 104. As shown in FIG. 22A, the object
detection node can determine that object 2216 is a second vehicle
in the same lane 2224 as the present vehicle. If a second vehicle
2216 is in view in the image and may be followed (e.g., is
traveling in the same lane 2224 in the same direction), the process
700 proceeds YES to step 724. However, if no second vehicle is in
view or the objects are not vehicles, the process 700 proceeds NO
to step 720.
[0134] In step 720, the processor changes the autonomous driving
mode to "lane follow," to follow a lane 2224 viewed in the driving
environment. In the lane follow mode, the processor 204 follows
road features, e.g., the marked lanes, curbs, tire tracks, etc.
Lane following may be as described hereinafter in conjunction with
FIGS. 12 and 25A-25D. Thus, the processor 204 can still control the
operation of the vehicle 104 autonomously when there is no vehicle
to follow.
[0135] The processor 204, in contrast, can change the autonomous
state to follow automatically an identified second vehicle 124, in
step 724. Thus, a particular distance from the vehicle 124 and
alignment to that vehicle 124 may be maintained through controls
sent through the CAN bus 232 to the control systems 236-254. If the
vehicle 124 moves, e.g., changes lanes, the vehicle 104 may then
change lanes to continue to follow the vehicle 124. In this way,
the needed computational effort by the processor 204 is greatly
reduced.
[0136] The control node can follow the second vehicle 2216, as
shown in FIGS. 22A-22E by maintaining the location of the center
2236 of the box 2218 in each image provided to the processor. For
example, as shown in FIG. 22D, the second vehicle 2216 may have
moved in the driving environment. The movement of the second
vehicle 2216 in the driving environment can appear as a shifting of
a first location of the box 2218a in a first image to a second
location of the box 2218b in a subsequent image. The vehicle follow
node can witness the change as a change in position of the center
2236a from a first location to a second location of the center
2236b along vector 2240. To adjust to the movement, the control
node can issue a steering change at a determined steering angle and
a velocity change (either by braking or by accelerating) to the
vehicle controls 236-254 that will reverse the vector 2240. Thus,
as shown in FIG. 22E, a reverse vector 2244 is applied by changing
steering and/or speed. This control change can then move the
location of the center 2236c, and thus, box 2218c, back to the same
position as center 2236a. Thus, to follow the second vehicle, the
control object maintains the center 2236 of the box 2218 in a same
position in subsequent images.
[0137] Further, the ability to drive autonomously is simplified as
only a single object need be tracked for driving in the environment
100. Also, the second vehicle may also be blocking the view of the
lanes, and thus, the vehicle may need to follow the second vehicle
to continue autonomous driving.
[0138] However, the processor 204 can still monitor the images from
the sensors 208 to determine a change in environment 100, in step
728. For example, the processor 204 can monitor the environment 100
to determine if the vehicle 124 deviates from the desired route
necessitating a change to another vehicle to follow. Further, the
processor 204 can continue to follow the vehicle 124 while trying
to identify possible threats or obstacles for the vehicle 104. In
this way, more processing is spent on safety then on simply driving
the vehicle 104.
[0139] It should be noted that the center 2236 may not be
maintained in the same exact position in all driving situations.
For example, when cornering or turning, maintain the center 2236 in
the same position may cause the vehicle 104 to drift out of the
lane or take the corner to sharply, which may be dangerous. Thus,
the control node can determine when the vehicle is turning. And
when the vehicle is turning, the position used to follow the center
2236 of the box 2218 may be adjusted by at least one pixel in the
image to ensure the vehicle 104 turns properly and safely.
[0140] In some configurations, the second vehicle 2216 may be in a
caravan with the vehicle 104. A caravan is a joint trip taken by
two or more vehicles that know each other and are associated. The
association may be by belonging to a same group, organization,
company, etc. The caravan can include a communication link
established between the vehicles. For example, the communication
link 112B may be established and/or maintained between the first
vehicle 104 and the second vehicle 128. When determining objects,
the object detection node may identify the second vehicle 2216 as
part of the caravan. The identification of the second vehicle may
be by identifying indicia (e.g., a license plate, an image of the
vehicle, a bumper sticker, bar code, QR code, another marking, a
dent or disfigurement, and/or other indicia) on the second vehicle
2216. The indicia may be received and known to the object detection
node.
[0141] FIG. 8 shows a method for autonomously driving a vehicle in
accordance with embodiments of the present disclosure. A general
order for the steps of the method 800 is shown in FIG. 8.
Generally, the method 800 starts with a start operation 804 and
ends with an end operation 828. The method 800 can include more or
fewer steps or can arrange the order of the steps differently than
those shown in FIG. 8. The method 800 can be executed as a set of
computer-executable instructions executed by a computer system and
encoded or stored on a computer readable medium. Further, the
method 800 can be performed by gates or circuits associated with a
processor, an ASIC, a FPGA, a SOC, or other hardware device.
Hereinafter, the method 800 shall be explained with reference to
the systems, components, devices, modules, software, signals, data
structures, interfaces, methods, etc. described in conjunction with
FIGS. 1-7 and 9-28.
[0142] The control node, executed by the processor 204, can enter
the autonomous state to follow an identified second vehicle 124, in
step 808. Thus, a particular distance from the second vehicle 124
(e.g., as determined by a radar or other sensor) and alignment
(e.g., as determined by the images as described in conjunction with
FIGS. 22A-22E) to that vehicle 124 may be maintained through
controls sent through the CAN bus 232 to the control systems
236-254. The processor 204 follows the vehicle 124 along the route
by monitoring that vehicle 124 and maintaining an orientation to
that vehicle 124.
[0143] Thus, the processor 204 can detect a change of position of
the vehicle 124, in step 812. In processing the image data through
the object detection nodes, a change in position of the vehicle 124
may be determined by a shift in the position of the vehicle 124
object in one image to a subsequent image, as described in
previously in conjunction with FIGS. 22D and 22E. Then, if the
vehicle 124 moves, e.g., changes lanes, the vehicle 104 may then
determine whether the vehicle 124 remains in view, in step 816. The
vehicle 104 can continue along a route regardless of a vehicle 124
staying on the same route. Thus, in some situations, the vehicle
124 may exit the route and disappear from the image generated by
the sensors 208. The processor 204 can determine that the vehicle
124 has deviated from the route and no longer is present in the
images of the sensor 208. If the vehicle 124 no longer is visible
in the images, the process 800 may proceed NO to step 820. When the
vehicle 124 remains in the image, the process 800 can proceed YES
to step 824.
[0144] In step 820, the processor 204 can change the driving mode.
The driving mode may be switched to the lane follow mode, as
described in conjunction with FIG. 7. In other situations, the
processor 204 can change the mode by selecting a new third vehicle
to follow while still in the follow vehicle mode.
[0145] Should the vehicle 124 remain visible, the processor 204 can
decide how to follow the vehicle and change position of the vehicle
104 to continue to follow vehicle 124, in step 824. In some
situations, the positional change of the vehicle 124 may be small
or inconsequential, e.g., lane drift. In these situations, the
processor 204 can decide not to follow or ignore the change of
precise position of the second vehicle. In other situations, the
vehicle 124 may change lanes, accelerate, decelerate, etc. The
processor 204 may decide to imitate these changes by issuing
commands through the CAN bus 232 to the control systems 236-254.
Thus, the processor 204 maintains the vehicle 104 in a relation
with vehicle 124 and changes automatically the position of the
first vehicle to match the change in the position of the second
vehicle as described previously in conjunction with FIGS. 22D,
22E.
[0146] FIG. 9 shows a method for autonomously driving a vehicle in
accordance with embodiments of the present disclosure. A general
order for the steps of the method 900 is shown in FIG. 9.
Generally, the method 900 starts with a start operation 904 and
ends with an end operation 928. The method 900 can include more or
fewer steps or can arrange the order of the steps differently than
those shown in FIG. 9. The method 900 can be executed as a set of
computer-executable instructions executed by a computer system and
encoded or stored on a computer readable medium. Further, the
method 900 can be performed by gates or circuits associated with a
processor, an ASIC, a FPGA, a SOC, or other hardware device.
Hereinafter, the method 900 shall be explained with reference to
the systems, components, devices, modules, software, signals, data
structures, interfaces, methods, etc. described in conjunction with
FIGS. 1-8 and 10-28.
[0147] The processor 204 can enter the autonomous state to follow
an identified vehicle 124, in step 908. Thus, an association but
not necessarily a particular distance or alignment to that vehicle
124 may be maintained through controls sent through the CAN bus 232
to the control systems 236-254. The processor 204 follows the
vehicle 124 along the route by monitoring that vehicle 124 and
maintaining an association with that vehicle 124. The association
is a relationship with the vehicle 124, in that, the vehicle 104
follows vehicle 124 but allows some changes in where the vehicle
124 is compared to vehicle 104.
[0148] Thus, the processor 204 can detect a change of position of
the vehicle 124, in step 912. In processing the image data through
the ROS nodes 304, a change in position of the vehicle 124 may be
determined by a shift in the position of the vehicle 124 object in
one image to a subsequent image, as described previously in
conjunction with FIGS. 22A-22E. Then, if the vehicle 124 moves,
e.g., changes lanes, the vehicle 104 may then determine whether the
vehicle 124 remains in view, in step 916. The vehicle 104 can
continue along a route regardless of a vehicle 124 staying on the
same route. Thus, in some situations, the vehicle 124 may exit the
route and disappear from the image generated by the sensors 208.
The processor 204 can determine that the vehicle 124 has deviated
from the route and no longer is present in the images of the sensor
208. If the vehicle 124 is no longer is visible in the images, the
process 900 may proceed NO to step 920. When the vehicle 124
remains in the image, the process 900 can proceed YES to step
924.
[0149] In step 920, the processor 204 can change the driving mode.
The driving mode may be switched to the lane follow mode, as
described in conjunction with FIG. 7. In other situations, the
processor 204 can change the mode by selecting a new vehicle to
follow while still in the follow vehicle mode.
[0150] Should the vehicle 124 remain visible, the processor 204 can
decide how to follow the vehicle 124 and change or not change the
position of the vehicle 104 to continue to follow vehicle 124, in
step 924. In some situations, the positional change of the vehicle
124 may be small or inconsequential, e.g., lane drift. In these
situations, the processor 204 can decide not to follow the change
of position precisely. In other situations, the vehicle 124 may
change lanes, accelerate, decelerate, etc. For example, as shown in
FIGS. 23A and 23B, the vehicle 104 may be proceeding in driving
environment 2300 along lane 2304. The vehicle 104 may be following
vehicle 2308 presently. Then, the vehicle 2308 may change lanes
from lane 2304 to lane 2312, as shown in FIG. 23B by vector
2316.
[0151] The processor 204 may decide to still not imitate these
changes, but allow variation in the position of the vehicle 2308
compared to the vehicle 104. In some driving situations, mimicking
the change in position may be dangerous. If someone is in lane 2312
to the side of vehicle 104, it may not be possible for vehicle 104
to change lanes similar to vehicle 2308.
[0152] If the change in position is not mimicked, the relationship
in distance and orientation between the vehicle 104 and the second
vehicle 2308 may change. For example, vehicle 124 may move in front
of vehicle 2308 in the other lane. If the vehicle 2308 begins to
move away from vehicle 104, then the processor 204 may attempt to
keep some distal relationship with vehicle 124 by issuing commands
through the CAN bus 232 to the control systems 236-254. However, if
the vehicle 124 remains relatively proximal to vehicle 104, the
processor 204 may not make any changes in the position or driving
settings and stay in the same lane 2304 and/or at the same speed.
Thus, the processor 204 maintains the vehicle 104 in a follow mode
with the second vehicle 2308 but not necessarily in parrot every
movement of vehicle 2308.
[0153] FIG. 10 shows a method for autonomously driving a vehicle in
accordance with embodiments of the present disclosure. A general
order for the steps of the method 1000 is shown in FIG. 10.
Generally, the method 1000 starts with a start operation 1004 and
ends with an end operation 1028. The method 1000 can include more
or fewer steps or can arrange the order of the steps differently
than those shown in FIG. 10. The method 1000 can be executed as a
set of computer-executable instructions executed by a computer
system and encoded or stored on a computer readable medium.
Further, the method 1000 can be performed by gates or circuits
associated with a processor, an ASIC, a FPGA, a SOC, or other
hardware device. Hereinafter, the method 1000 shall be explained
with reference to the systems, components, devices, modules,
software, signals, data structures, interfaces, methods, etc.
described in conjunction with FIGS. 1-9 and 11-28.
[0154] The processor 204 can receive an indication of a vehicle 124
that may be available to follow, in step 1008. The processor 204
can receive a signal for another entity, e.g., the server 122,
which identifies a vehicle 124, in a caravan, having particular
characteristics that may be traveling along the same route, at
least for a portion of the route. The signal sent to the processor
204 can include identifying information for the vehicle 124, for
example, the vehicle 104 may receive data structure 2702 from a
data store 2700, as shown in FIG. 27A. The data structure can
include a vehicle identifier (ID), visual indicia 2708, and or
breadcrumbs 2712. There may be more or fewer fields in data
structure 2702, as represented by ellipses 2736. Each caravan or
association of vehicles can have a data structure 2702, as
represented by ellipses 2740.
[0155] The vehicle ID can be automatically generated and represent
any type of identifier including one or more of, but not limited
to, a numeric ID, a license plate number, an alphanumeric ID, a
globally unique ID (GUID), etc. The visual indicia can be any
visual information, which can be provided as one or more of, but is
not limited to, an image of the vehicle, a vehicle type, a vehicle
make and/or model, a vehicle color, a license plate number, a
bumper sticker, an image of damage to the vehicle, etc. The
breadcrumb 2712 is an electronic beacon or waypoint of the second
vehicle that allows the first vehicle to follow the route of the
second vehicle even if the second vehicle is out of sight. Thus,
the breadcrumb 2712 can include one or more of, but is not limited
to, a Global Positioning Satellite (GPS) coordinates for the second
vehicle at some moment in time, images seen by the second vehicle
at a moment in time, etc. The second vehicle 2408 and the first
vehicle may maintain a communication link 112B, while caravanning,
to continue to exchange information, for example, data structure
2702.
[0156] The image processing node, executed by the processor 204 and
implemented in the convolutional neural network as an ML model, may
process the images of the driving environment 100 to locate the
identified vehicle 2408. Within the image, different edges, the
position of those edges, and other information can be determined.
Conditions can include an edge or other information, such as
movement of an edge over time, location of other sensed data, etc.
The vehicle 2408 may be identified and followed as described
previously in conjunction with FIGS. 22A-22E.
[0157] From the conditions, a next ML model associated with an
object detection node may identify one or more objects within the
image. The shape of the object can inform the processor 204 as to
what type of object is being sensed. This information may then be
stored by the processor 204 for determining actions required for
driving in the environment 100. After identifying vehicles within
the image, an object detection node can compare the object to the
information provided by data structure 2702.
[0158] The processor 204 can then determine if the identified
vehicle is in view in the image, in step 1012. For example, the
processor 204 can determine if vehicle 2408 is being seen by the
sensors 208 of vehicle 104. If a vehicle 2408 is in view, as shown
in FIG. 24A, the process 1000 proceeds YES to step 1016. However,
if the identified vehicle is not in view, the process 1000 proceeds
NO to step 1024. For example, another vehicle 2412 may enter lane
2404 between vehicle 104 and vehicle 2408, as shown in FIGS. 24A
and 24B. Thus, as shown in FIG. 24B, vehicle 2408 is obscured and
not in view of vehicle 104.
[0159] In step 1024, the control node changes the autonomous
driving mode to "lane follow" or "vehicle follow" but using a
different vehicle, e.g., vehicle 2412, other than the vehicle 2408
identified, as described in conjunction with FIG. 7 and FIGS. 24A
and 24B. Then, for some period of time, the processor 204 may
continue to search for the identified vehicle 2308 while
autonomously driving in a different mode. The period of time may be
predetermined, established by a user, and/or established by a third
party, for example, the server 122, the vehicle manufacturer, etc.
Thus, the method 1000 can return to step 1012 for successive
iterations of step 1012 until the period of time expires or another
event cause the process to proceed to the end operation 1028.
[0160] The control node, in contrast, can maintain the autonomous
state to follow the second vehicle 2408, in step 1016. Thus,
control node can establish a spatial relationship with the
identified and discovered vehicle 2408. The control node, thus, can
establish a particular distance from the vehicle 2408 and alignment
to that vehicle 2408 that may be effectuated and maintained through
controls sent through the CAN bus 232 to the control systems
236-254.
[0161] The processor 204 may then follow the vehicle 2408, in step
1020. If the vehicle 2408 moves, e.g., changes lanes, the vehicle
104 may then change lanes to continue to follow the vehicle 124. In
this way, the needed computational effort by the processor 204 is
greatly reduced. Further, the ability to drive autonomously is
simplified as only a single object need be tracked to for driving
in the environment 100 and can drive autonomously when another
vehicle obscures the lane lines.
[0162] FIG. 11 shows a method for autonomously driving a vehicle in
accordance with embodiments of the present disclosure. A general
order for the steps of the method 1100 is shown in FIG. 11.
Generally, the method 1100 starts with a start operation 1104 and
ends with an end operation 1132. The method 1100 can include more
or fewer steps or can arrange the order of the steps differently
than those shown in FIG. 11. The method 1100 can be executed as a
set of computer-executable instructions executed by a computer
system and encoded or stored on a computer readable medium.
Further, the method 1100 can be performed by gates or circuits
associated with a processor, an ASIC, a FPGA, a SOC, or other
hardware device. Hereinafter, the method 1100 shall be explained
with reference to the systems, components, devices, modules,
software, signals, data structures, interfaces, methods, etc.
described in conjunction with FIGS. 1-10 and 12-28.
[0163] The control node can enter the autonomous state to follow an
identified vehicle 124. For example, as shown in FIGS. 24A and 24B,
an association with a vehicle 2408 may be maintained through
controls sent through the CAN bus 232 to the control systems
236-254. The control node follows the vehicle 2408 along the route
by monitoring that vehicle 2408 and maintaining an association with
that vehicle 2408. The association is a relationship with the
vehicle 2408, in that, the vehicle 104 follows vehicle 2408 but
allows some changes in where the vehicle 2408 is compared to
vehicle 104.
[0164] The control node can then detect a change of position of the
vehicle 2408. In processing the image data through the image
processing node, a change in position of the vehicle 2408 may be
determined by a shift in the position of the vehicle 2408 object in
one image to a subsequent image, as described previously in
conjunction with FIGS. 22A-22E. Then, if the vehicle 2408 moves,
e.g., changes lanes, the vehicle 104 may then determine whether the
vehicle 2408 remains in view. The vehicle 104 can continue along a
route regardless of a vehicle 2408 staying on the same route. Thus,
in some situations, the vehicle 2408 may exit the route, and the
processor 204 may then lose a view of the vehicle 2408 from the
image(s) generated by the sensors 208, in step 1108. For example,
the vehicle 2408 may exit a road, may change lanes and become
obscured by another vehicle, may accelerate away from the vehicle
104, may decelerate and end up behind vehicle 104 (making following
physically impossible), another vehicle may position itself between
the vehicle 104 and vehicle 2408 (as shown in FIGS. 24A and 24B,
etc.
[0165] The processor 204 can determine that the vehicle 2408 has
deviated from the route (e.g., is no longer in lane 2404 driving in
a same direction) and/or is no longer is present in the images of
the sensor 208. If the vehicle 2408 no longer is visible in the
images, the control node can then determine to attempt to follow a
third vehicle 2412 in the view of the sensors 208, in step 1112.
The control node can then determine if a third vehicle is in view
in the image and located the third vehicle to follow in subsequent
images, in step 1116, as described previously in conjunction with
FIGS. 22A-22E. For example, the control node can determine if
vehicle 2412 is being seen by the sensors 208 of vehicle 104. If a
third vehicle 2412 is in view, the process 1100 proceeds YES to
step 1124. However, if no vehicle is in view, the process 1100
proceeds NO to step 1120. In step 1120, the control node can change
the driving mode. The driving mode may be switched to the lane
follow mode, as described in conjunction with FIG. 7. Thus, the
vehicle 104 can maintain a same route while changing to another
autonomous driving mode, e.g., the lane following mode.
[0166] The control node, in contrast, can change the autonomous
state to follow the third vehicle 2412, in step 1124. The control
node can decide how to follow the third vehicle 2412 and change or
not change the position of the vehicle 104 to follow vehicle 2412,
in step 1128. In some situations, the positional change of the
vehicle 2412 may be small or inconsequential, e.g., lane drift. In
these situations, the control node can decide not to follow the
change of position precisely. In other situations, the vehicle 2412
may change lanes, accelerate, decelerate, etc. The control node may
decide to still not imitate these changes, but allow variation in
the position of the vehicle 2412 to the vehicle 104. If the vehicle
2412 begins to move away from vehicle 104, then the control node
may attempt to keep some distal relationship with vehicle 2412 by
issuing commands through the CAN bus 232 to the control systems
236-254. However, if the vehicle 2412 remains relatively proximal
to vehicle 104 and maintains a same route as the first vehicle 104,
the control node may not make any changes in the position or
driving settings and stay in the same lane and/or at the same speed
and continue to follow the third vehicle 2412. Thus, the control
node maintains the vehicle 104 in a follow mode with vehicle
2412.
[0167] FIG. 12 shows a method for autonomously driving a vehicle in
accordance with embodiments of the present disclosure. A general
order for the steps of the method 1200 is shown in FIG. 12.
Generally, the method 1200 starts with a start operation 1204 and
ends with an end operation 1232. The method 1200 can include more
or fewer steps or can arrange the order of the steps differently
than those shown in FIG. 12. The method 1200 can be executed as a
set of computer-executable instructions executed by a computer
system and encoded or stored on a computer readable medium.
Further, the method 1200 can be performed by gates or circuits
associated with a processor, an ASIC, a FPGA, a SOC, or other
hardware device. Hereinafter, the method 1200 shall be explained
with reference to the systems, components, devices, modules,
software, signals, data structures, interfaces, methods, etc.
described in conjunction with FIGS. 1-11 and 13-28.
[0168] In step 1208, the control node enters the autonomous driving
mode to "lane follow." In the lane follow mode, the control node
follows road features, e.g., the marked lanes, curbs, tire tracks,
etc. Thus, the control node can still control the operation of the
vehicle 104 autonomously but need not have a vehicle to follow.
[0169] The lane identification node executed by the lane
identification node can identify the edges of the lanes or other
road feature in the image(s). Thus, as explained above, the lane or
other road object feature may be identified. This road feature may
then followed by directing the path of vehicle 104 to interleave or
slot between the lanes or other road features. In this way, the
lane identification node keeps the vehicle 104 traveling on the
road and within the lane.
[0170] The lane detection node can execute a ML model, associated
with a CNN, to analyze the image for an edge that can define a
lane. The edge can be a contrast in color or light between two
adjacent areas define by a line or other straight delineation. An
example of how the lane is defined may be as shown in FIGS. 25A
through 25D. In an image 2500 of the driving environment, a lane
2504 may be defined by at least one lane line 2506a, 2506b. The ML
model can recognize edge that continue onto or to within proximity
with a vanishing point 2510. Once a lane line 2506 is recognized,
the lane detection node can locate and define two or more points
2512a, 2512b, 2512c, 2512d, etc. that define a location of the lane
line 2506, and as a result, the lane 2504 itself.
[0171] The points 2512 can be positioned at predetermined intervals
along a vertical axis 2516 of the image. The intervals between
successive points may be equidistant or at random or varying
distances. Each point can be defined by a data structure. The data
structure can include a point identifier ID, a location (some
number of horizontal and vertical pixels, in the pixel grid, from a
known and predetermined location in the image, e.g., the bottom
right corner, the center of the image, the vanishing point 2510) of
the point, and/or the lane to which the point is associated (e.g.,
left lane, right lane, middle lane, etc.). The point ID can be any
type of identifier, for example a GUID, a numeric ID, a name, an
alphanumeric ID, etc.
[0172] However, there are difficulties with determining the edges
of the lane. In step 1216, the lane identification node can
determine if there is a portion of the edge defining the lane that
should be filtered. For example, the pixilation of the lane edge
can be jagged or have sections that do not conform to a straight or
continuous line. Other problems are also possible, for example, the
lane markers on the road surface may not be in a uniform or
continuous line. In other words, the paint or markers that indicate
the lane line 2506 can be wavy or non-uniform. If the vehicle were
to strictly follow these inaccurate or non-uniform lane edges, the
vehicle 104 could shift from side-to-side or "shake" between the
lane edges. To prevent these issues, the lane detection node can
filter out these errors or inconsistencies.
[0173] To identify areas to filter, the lane identification node
can analyze the procession of the edge. If the edge makes a change
compared to some set benchmark, e.g., more than 10.degree., that
edge section may be marked. If the edge returns to a more uniform
line, that end of the section may also be marked. Thus, the portion
deviating from a normalized or best fit line can be marked.
[0174] Alternatively or additionally, each of the points 2512 can
be checked against a predetermined envelop, as shown in FIG. 25B. A
point 2512a may be analyzed against previous points in previous
images. Thus, the location of point 2512a can be compared to a
previous location of the same or associated point in a previous
image. If the location and the previous location deviate by more
than a predetermined threshold 2520a, that point may be filtered
out of the set of points that defined the location of the lane line
2506a. The current location can be checked against more than one
previous location in more than one previous image. In other words,
the current location can be checked against a moving average of the
location or an average location. Further, depending on the location
of a point 2512 along the vertical access 2516, the threshold 2520
may be different. For example, due to the proximity of the lane
line 2506 to the vehicle 104, the lane lines 2506 in the image may
move more lower in the image that nearer the vanishing point 2510.
Thus, the predetermined threshold 2520a may be greater for points
2512a, 2512b lower in the image 2500 than the predetermined
threshold 2520b for points 2512d higher in the image 2500.
[0175] If there is a portion of the edge 2506 or points 2512 that
needs to be filtered, the process 1200 can proceed YES to step
1224. If there is no portion of the edge 2506 or points 2512 that
needs to be filtered, the process 1200 can proceed NO to step 1220.
In step 1220, the lane identification node processes the unfiltered
edges and follows those edges to maintain the vehicle 104 within
the lane by issuing commands through the CAN bus 232 to the control
systems 236-254 to follow the lane. To follow the lane lines 2506a,
2506b, the control node can automatically steer the vehicle 104
between the two or more points 2512 defining a first lane 2506a and
the two or more points 2512 defining a second lane 2506b. Thus, the
control node attempts to steer alone a line 2524 that bisects the
points 2512, as shown in FIG. 25C.
[0176] In step 1224, the lane identification node smooths the edge
by filtering the undesired edge portion 2506 or points 2512. All
edge information from a first marker to a second marker,
established as explained above, indicating the deviating edge
section may be erased or ignored for future processing. Thus, a
section or portion of the lane edge is missing for the lane line
2506b following the filtering operation, as shown in FIG. 25D. As
can be seen, a significant portion of the lane line 2506b is
missing.
[0177] The lane identification node may then normalize the edge, in
step 1228. To normalize the edge, the lane identification node can
generate a replacement portion of the edge that corresponds to a
best fit line along the edge before and after the filtered edge
portion. In this way, the lane identification node maintains a
continuous edge of the lane for the vehicle 104 to follow in
subsequent processing.
[0178] In other configurations, normalization 1228 can change how
the control node steers the vehicle 104. Rather than bisect the
points 2512 as explained in conjunction with FIG. 25C, which is not
possible with a set of the points 2512 missing, the control node
steers from a single lane line 2506a. In other words, the control
node automatically steers to maintain a path 2528 of the vehicle
104 a predetermined number of pixels from the two or more points
2512 defining a first lane 2506a. In this way, the vehicle 104
maintains its course even without two lane lines 2506.
[0179] FIG. 13 shows a method for determining an environmental
concern for an autonomous vehicle in accordance with embodiments of
the present disclosure. A general order for the steps of the method
1300 is shown in FIG. 13. Generally, the method 1300 starts with a
start operation 1304 and ends with an end operation 1332. The
method 1300 can include more or fewer steps or can arrange the
order of the steps differently than those shown in FIG. 13. The
method 1300 can be executed as a set of computer-executable
instructions executed by a computer system and encoded or stored on
a computer readable medium. Further, the method 1300 can be
performed by gates or circuits associated with a processor, an
ASIC, a FPGA, a SOC, or other hardware device. Hereinafter, the
method 1300 shall be explained with reference to the systems,
components, devices, modules, software, signals, data structures,
interfaces, methods, etc. described in conjunction with FIGS. 1-12
and 14-28.
[0180] The processor 204 can determine one or more conditions of
the environment 100 while the vehicle 104 is not moving, in step
1308. The processor 204 can receive one or more images from the
image sensors 208. The nodes 304 deploy a ML model executed by the
processor 204 and associated with a CNN may process the image.
Within the image, different edges, the position of those edges, and
other information can be determined. Conditions can include an edge
or other information, such as movement of an edge over time,
location of other sensed data, etc.
[0181] From the conditions, a next node may identify one or more
objects within the image. The shape of the object can inform the
processor 204 as to what type of object is being sensed. This
information may then be stored by the processor 204 for determining
actions required for driving in the environment 100. The stored
objects can include images of business signs, street signs, words,
disposed waste, other people, etc. Other sensor data may also be
processed, such as the time, GPS data indicating location, and
other types of sensor data.
[0182] The processor 204 can then compare the sensed information to
one or more parameters, in step 1312. A parameter can be a legal
requirement, a business requirement, a law, a code, or other type
of necessity. For example, the GPS sensor data can be compared to a
location prohibited by a restraining order. In another example, an
image of a cocktail glass and a time can be compared to a business
requirement that discourages drinking during business hours. Other
such comparisons are possible. The processor 204 then determines if
a parameter has been violated, in step 1316. If a violation has
occurred, the process 1300 proceeds YES to step 1324. However, if
no violation has occurred, the process 1300 proceeds NO to step
1320. In step 1320, the processor 204 continues to monitor the
environment.
[0183] The processor 204 can determine a response to the violation,
in step 1324. The response to a violation may be stored in user
information data store 224. Thus, a violation of a restraining
order may require the police be contacted by the processor 204
sending a message through the communication interface 256. A
violation of the drinking policy may require the prohibition of the
vehicle 104 from starting, the calling of a cab through the
communications interface 256, and/or the sending of an incident
report to a superior through the communications interface 256. In
step 1328, the response is executed by the processor 204.
[0184] FIG. 14 shows a method for autonomously driving a vehicle in
accordance with embodiments of the present disclosure. A general
order for the steps of the method 1400 is shown in FIG. 14.
Generally, the method 1400 starts with a start operation 1404 and
ends with an end operation 1432. The method 1400 can include more
or fewer steps or can arrange the order of the steps differently
than those shown in FIG. 14. The method 1400 can be executed as a
set of computer-executable instructions executed by a computer
system and encoded or stored on a computer readable medium.
Further, the method 1400 can be performed by gates or circuits
associated with a processor, an ASIC, a FPGA, a SOC, or other
hardware device. Hereinafter, the method 1400 shall be explained
with reference to the systems, components, devices, modules,
software, signals, data structures, interfaces, methods, etc.
described in conjunction with FIGS. 1-13 and 15-28.
[0185] The processor 204 can enter the autonomous state to follow
an identified vehicle 124. Thus, an association but not necessarily
a particular distance or alignment to that vehicle 124 may be
maintained through controls sent through the CAN bus 232 to the
control systems 236-254. The processor 204 follows the vehicle 124
along the route by monitoring that vehicle 124 and maintaining an
association with that vehicle 124. The association is a
relationship with the vehicle 124, in that, the vehicle 104 follows
vehicle 124 but allows some changes in where the vehicle 124 is
compared to vehicle 104.
[0186] Thus, the processor 204 can detect a change of position of
the vehicle 124, in step 1408. In processing the image data through
the nodes 304, a change in position of the vehicle 124 may be
determined by a shift in the position of the vehicle 124 object in
one image to a subsequent image. Then, if the vehicle 124 moves,
e.g., changes lanes, the vehicle 104 may then determine whether the
vehicle 124 remains in view. The vehicle 104 can continue along a
route regardless of a vehicle 124 staying on the same route. Thus,
in some situations, the vehicle 124 may deviate from the route and
disappear from the image generated by the sensors 208. The
processor 204 can determine that the vehicle 124 has deviated from
the route and no longer is present in the images of the sensor 208.
These changes in position of the vehicle 124 over time may be
stored, with metadata explaining what, when, where, and why the
information was stored, as image data 216, in step 1436.
[0187] From the stored data, the processor 204 can determine if the
vehicle 124 is driving erratically. For example, if a number of
adjustments per hour exceeds a predetermined benchmark, the
processor 204 can determine the driving of the vehicle 124 is
erratic. If a number of lane changes or other positional change is
more than a predetermined threshold, the processor 204 can
determine the vehicle 124 is behaving erratically. If the processor
204 determines that the vehicle is behaving erratically, the
process 1400 may proceed Yes to step 1424. However, if the
processor 204 determines that the vehicle is not behaving
erratically, the process 1400 may proceed NO to step 1420, wherein
the processor 204 continues to follow the vehicle 124. In some
examples, a parameter change, such as but not limited to lane
changes, acceleration, deceleration, velocity, etc. may be utilized
to determine the condition of the followed vehicle. In some
situations, a condition of the followed vehicle may be determined
to be erratic, not-safe, safe, not-erratic, etc. In some
situations, the processor 204 may coordinate a message to be sent,
where the message may be sent to the followed vehicle, a different
reporting agency, or otherwise, using the network 260 for
example.
[0188] In step 1424, the processor 204 can report the behavior and
the vehicle 124 to the authorities or to another entity. Thus, the
processor 204 can send an automated message to a third party
through the communications interface 256. Further, the processor
204 may also start following a second vehicle 128 as described
above.
[0189] FIG. 15 shows a method for autonomously driving a vehicle in
accordance with embodiments of the present disclosure. A general
order for the steps of the method 1500 is shown in FIG. 15.
Generally, the method 1500 starts with a start operation 1504 and
ends with an end operation 1532. The method 1500 can include more
or fewer steps or can arrange the order of the steps differently
than those shown in FIG. 15. The method 1500 can be executed as a
set of computer-executable instructions executed by a computer
system and encoded or stored on a computer readable medium.
Further, the method 1500 can be performed by gates or circuits
associated with a processor, an ASIC, a FPGA, a SOC, or other
hardware device. Hereinafter, the method 1500 shall be explained
with reference to the systems, components, devices, modules,
software, signals, data structures, interfaces, methods, etc.
described in conjunction with FIGS. 1-14 and 16-28.
[0190] The processor 204 can enter the autonomous state to follow
an identified vehicle 124. Thus, an association but not necessarily
a particular distance or alignment to that vehicle 124 may be
maintained through controls sent through the CAN bus 232 to the
control systems 236-254. The processor 204 follows the vehicle 124
along the route by monitoring that vehicle 124 and maintaining an
association with that vehicle 124. The association is a
relationship with the vehicle 124, in that, the vehicle 104 follows
vehicle 124 but allows some changes in where the vehicle 124 is
compared to vehicle 104.
[0191] Thus, the processor 204 can detect a change of position of
the vehicle 124, in step 1508. In processing the image data through
the nodes 304, a change in position of the vehicle 124 may be
determined by a shift in the position of the vehicle 124 object in
one image to a subsequent image. Then, if the vehicle 124 moves,
e.g., changes lanes, the vehicle 104 may then determine whether the
vehicle 124 remains in view. The vehicle 104 can continue along a
route regardless of a vehicle 124 staying on the same route. Thus,
in some situations, the vehicle 124 may deviate from the route and
disappear from the image generated by the sensors 208. The
processor 204 can determine that the vehicle 124 has deviated from
the route and no longer is present in the images of the sensor 208.
These changes in position of the vehicle 124 over time may be
stored, with metadata explaining what, when, where, and why the
information was stored, as image data 216, in step 1536.
[0192] From the stored data, the processor 204 can determine if the
vehicle 104 is driving erratically. For example, if a number of
adjustments per hour exceeds a predetermined benchmark, the
processor 204 can determine the driving of the vehicle 104 is
erratic. If a number of lane changes or other positional change is
more than a predetermined threshold, the processor 204 can
determine the vehicle 104 is behaving erratically. If the processor
204 determines that the vehicle 104 is behaving erratically, the
process 1500 may proceed Yes to step 1524. However, if the
processor 204 determines that the vehicle 104 is not behaving
erratically, the process 1500 may proceed NO to step 1520, wherein
the processor 204 continues to follow the vehicle 124.
[0193] In step 1524, the processor 204 can determine problem with
the control of the vehicle and, in step 1528, execute a response
for the problem. For example, the processor can determine a
necessity for and execute the reporting of the behavior and driver
of the vehicle 104 to the authorities or to another entity. Thus,
the processor 204 can send an automated message to a third party
through the communications interface 256. Further, the processor
204 may also start following a second vehicle 128 as described
above. In still other situations, the processor 204 can indicate a
problem with something in the autonomous drive system based on the
erratic behavior. In this situations, the processor 204 may
instruct the driving systems to pull over the vehicle 104 and wait
for repairs or service.
[0194] As provided above, one or more calibration parameters may be
obtained over time; such calibration parameters allow for the
calibration of the image acquisition device in an efficient and
ongoing manner. Thus, rather than having an autonomous vehicle have
to relocate to a calibration facility, such calibration can be
performed dynamically on the fly and/or over an ongoing basis.
[0195] FIG. 16 depicts a method for automatically calibrating one
or more image acquisition devices in accordance with embodiments of
the present disclosure. A general order for the steps of the method
1600 is shown in FIG. 16. Generally, the method 1600 starts with a
start operation 1604 and ends with an end operation 1632. The
method 1600 can include more or fewer steps or can arrange the
order of the steps differently than those shown in FIG. 16. The
method 1600 can be executed as a set of computer-executable
instructions executed by a computer system and encoded or stored on
a computer readable medium. Further, the method 1600 can be
performed by gates or circuits associated with a processor, an
ASIC, a FPGA, a SOC, or other hardware device. Hereinafter, the
method 1600 shall be explained with reference to the systems,
components, devices, modules, software, signals, data structures,
interfaces, methods, etc. described in conjunction with FIGS. 1-15
and 17-28.
[0196] The method may be initiated at step 1604 and flow to step
1608 where the system may determine if existing calibration
parameters are already available for the image acquisition device.
For example, the system processor may access the database, and
retrieve previously stored image acquisition device calibration
parameters at step 1508. If, at step 1608, the system determines
that existing calibration parameters are not available, the method
may proceed to step 1612, where an image, or scene, from the image
acquisition device may be obtained. Accordingly, at step 1616, the
method may extract one or more features from the image, as
previously described, and may further identify parallel lines
associated with the extracted features. For example, if an image of
a scene includes a polygon shape such as a stop sign, the system
can identify parallel lines from edges of the polygon. Extending
such parallel lines from the extracted feature can determine a
point of intersection in the horizon. Based on multiple points of
intersection in the horizon, one or more vanishing points for the
scene may be determined. A vanishing point is a point on the image
plane of a perspective drawing where the two-dimensional
perspective projections of mutually parallel lines in
three-dimensional space appear to converge. The system may then use
the vanishing points and the parallel lines determine one or more
calibration parameters, such as pose, height, field of view, angle,
etc. Accordingly, one or more flows of a model may be modified
based on the new calibration parameters such that a higher
statistical degree of camera accuracy, and therefore model
accuracy, can be obtained.
[0197] A calibration may be as shown in FIGS. 26A-26C. First
referring to FIG. 26C, an image 2600 is provided having one or more
objects 2636a, 2636b, 2648a-2648c. These objects 2636, 2648 can
represent buildings and lane lines or lane markers, respectively,
in the image 2600 that can help determined the vanishing point 2632
in the image 2600. The building 2636 can have one or more edges
2640a, 2640b, 2640c, 2640d that help determine the vanishing point.
The edges 2640 can be projected from the building 2636 to a horizon
as lines 2644a, 2644b, 2644c, 2644d to determine a common point
2632, which represents the vanishing point. The angle of the edge
2640 indicates the projection of the line 2644 and thus, the angle
of the edge 2640 indicates the location for the vanishing point
2632. The lane marker 2648 can also end or terminate at the
vanishing point 2632. There may be a small discrepancy as to where
the edge 2640 project to and/or where the lane markers 2648 end. An
average of the points may provide the vanishing point 2632, which
can be represented by a pixel coordinate in the horizontal and
vertical direction. These pitch and yaw calibration parameters can
be stored by the vehicle and applied to subsequent images to
compensate for the center of the camera not being perfectly aligned
with the image of the driving environment.
[0198] Based on the vanishing point 2632, the image acquisition
devise can determine calibration parameters. The calibration
parameters can include at least the pitch 2652 of the image
acquisition devise and the yaw 2656 of the image acquisition
devise. The pitch parameter 2652 represent the number of pixels, in
a vertical direction, the center 2660 of the image is from a
horizontal line that represents the vertical position of the
vanishing point 2632. The yaw parameter 2656 represent the number
of pixels, in a horizontal direction, the center 2660 of the image
is from a vertical line that represents the horizontal position of
the vanishing point 2632.
[0199] In accordance with embodiments of the present disclosure,
and as previously described, the new camera parameters may be
utilized to modify an existing flow and/or node of an autonomous
vehicle model, such as the autonomous vehicle model 452. That is,
one or more of nodes and/or flows associated with the image
processing, the feature extraction, and/or the classification steps
as previously described may be modified. Accordingly, in as much
that the calibration parameters may affect the image preprocessing
step, such parameters may also affect an accuracy associated with
feature extraction, feature classification, and/or the ultimate
adjustment that is made by the model. Accordingly, one or more of
the camera calibration parameters may be utilized as an input to
one or more levels, flows, and/or nodes of the convolutional neural
network.
[0200] FIG. 17 depicts a method of calibrating one or more camera
parameters over time using classified feature information obtained
from an image of the image acquisition device in accordance with
embodiments of the present disclosure. That is, at step 1704, the
flow may be initiated and proceed to step 1708, where a scene, or
image, may be obtained from an image acquisition device, as
previously described. Image information may then be obtained
utilizing a feature extraction and classification process as
previously described. The feature extraction and classification
process may then identify one or more features in the scene or
image at step 1712. At step 1716, parameters associated with the
identified and extracted feature, such as an object, may be
obtained. For example, if the autonomous vehicle model identifies a
stop sign and/or traffic light, size information from the stop sign
and/or traffic light (such as the aperture openings, size of LED
etc. . . . ) may be obtained. Such object parameter information may
be used as ongoing calibration data for the camera. For example,
the known feature information may be retrieved from a database at
step 1720 and may be utilized to the scale or otherwise update one
or more calibration parameters associated with the object
identified and/or extracted from the image provided by the image
acquisition device. At step 1724, the new calibration parameters
associated with the image acquisition device and vehicle may then
be stored in the database, such as database 444.
[0201] FIG. 18 depicts a method 1800 for calibrating a first image
acquisition device based on information received from a second
image acquisition device in accordance with embodiments of the
present disclosure. As depicted in FIG. 18, the system may initiate
method 1800 at step 1804, where the method may proceed to step
18008 where an image from a first image acquisition device is
obtained. The method then proceeds to step 1812 where one or more
objects are detected, via feature extraction and classification, as
explained previously in conjunction with FIGS. 22A-22E.
[0202] At step 1816, information from a second image acquisition
device may be obtained; that is, the second image acquisition
device may obtain a second image. The second image acquisition
device may be associated the same vehicle or a different vehicle.
For example, an image may be obtained from a second vehicle where
the second image includes scene information that is the same as or
similar to a scene obtained from the first image acquisition
device. The information obtained from the second image acquisition
device may be obtained for a same location as the image obtained
from the first image acquisition device. For example, the second
vehicle can send a data structure 2714 as described in conjunction
with FIG. 27B.
[0203] The vehicle 104 may receive data structure 2714 from a data
store 2700, as shown in FIG. 27B, and/or from a second image
acquisition devise associated with a second vehicle. The data
structure 2714 can include a vehicle identifier (ID) 2716, a GPS
coordinate 2720, object information 2724, and/or a second image
2728. There may be more or fewer fields in data structure 2714, as
represented by ellipses 2744. Each association of vehicles can have
a data structure 2714, as represented by ellipses 2748.
[0204] The vehicle ID can be automatically generated and represent
any type of identifier including one or more of, but not limited
to, a numeric ID, a license plate number, an alphanumeric ID, a
globally unique ID (GUID), etc. Thus, GPS information 2720 can
include a location of the second vehicle at a time when the image
was acquired. In this way, the first vehicle can obtain a same
image from a same location. Object information 2724 may be as
defined in conjunction with FIG. 28A. Images 2728 can be one or
more images taken by the second image acquisition devise for the
first image acquisition devise.
[0205] FIG. 28 illustrates an example data structure 2800 in
accordance with examples of the present disclosure. The data
structure 2800 may include one or more of an Object ID 2806, Height
2808, Width 2810, Center 2812, and Location of Center 2814 and may
store information associated with one or more objects as explained
in conjunction with FIGS. 22A-22E, objects 2604, 2608, 2612, 2616,
2620, and 2606, 2610, 2614, 2618, and 2626 as shown in FIGS. 26A
and 26B. The processor 204 may access one or more elements of the
data structure 2800 to store and/or retrieve object information,
such as a height, width, and center of the box as well as well as a
location of the center of the box within the image as described
herein. Additional fields may be included as indicated by the
ellipses 2820; additional objects may be stored in the data
structure 2800 as indicated by the ellipses 2816. Data 2804 for an
object having an Object ID of A45FA is further illustrated.
[0206] As explained in conjunction with FIGS. 22A-22E, objects
2604, 2608, 2612, 2616, and 2620 may be located within the image
2600, as shown in FIGS. 26A and 26B. Thus, at least one of the
features in the second image comprises the objects 2604, 2608,
2612, 2616, 2620 and at least one of the parameters is the location
of those objects the 2604, 2608, 2612, 2616, and 2620. Boxes 2606,
2610, 2614, 2618, 2622 may be drawn around the objects 2604, 2608,
2612, 2616, 2620. The center of the boxes may be determined for
each box as previously described in conjunction with FIGS. 22A-22E.
As shown in FIG. 26B, the location, enumerated by a number of
pixels (e.g., as a vector length and angle or as a number of pixels
in a horizontal direction 2664 and vertical direction 2668) from a
vanishing point 2632. In this way, the calibration node at the
first vehicle can use the calibration information for the second
image acquisition device and the location of the second vehicle
when the image was acquired to make a comparison to a similar image
made by the first image acquisition devise of the first vehicle.
The comparison can reveal where the vanishing point 2632 should be
and how to determine the pitch and yaw of the camera as explained
in conjunction with FIG. 26C.
[0207] At step 1820, calibration information, such as one or more
previously described parameters, may be retrieved and/or obtained
for the second image and/or for the second image acquisition
device. Based on the calibration information associated with the
second image and/or the second image acquisition device, as
described above, one or more calibration parameters associated with
the first image acquisition device may be modified. As one example,
if an image including a first scene is obtained from the first
image acquisition device, calibration parameters from a second
image acquisition device for an image of the first scene may be
obtained and may be utilized to update and/or modify the
calibration parameters of the first image acquisition device. The
method 1800 may end at step 1828. However, the calibration
processes above may occur periodically to ensure the image
acquisition devise remains calibrated.
[0208] FIG. 19A depicts a method utilized to adjust one or more
parameters of an autonomous vehicle based on an autonomous vehicle
model in accordance with embodiments of the present disclosure. As
depicted in FIG. 19A, the method 1900 may begin at step 1904 where
the method proceeds to receive an image from an image acquisition
device at step 1908. At step 1912, the image may be preprocessed,
as previously described, and one or more features may be extracted
and classified at step 1916. At step 1920, features may be
classified from the first image using the autonomous vehicle model
and any adjustment may be recommended using the same autonomous
vehicle model. At step 1928, the autonomous vehicle model may
provide the recommendation to the steering angle adjust 468 and/or
the velocity adjust 472 where a vehicle adjustment may be made. The
method 1900 may then end at step 1932.
[0209] In accordance with embodiments of the present disclosure, a
statistical accuracy of the model may be monitored over time. For
example, as a result of the output prediction at 560 and/or the
output of the vehicle adjust at 464, the number of and type of user
engagements and/or disengagements may be monitored and correlated
with an output accuracy of the autonomous vehicle model. Such
correlating may occur at step 1936. In accordance with embodiments
of the present disclosure, the statistical accuracy of the model
from step 1936 may be utilized to determine if a new model 452 may
need to be obtained, generated, and/or requested. That is, at step
1938, the method 1940 may retrieve a previous, or prior,
statistical accuracy that was recorded by the method 1900 at step
1936. The statistical accuracy and/or method may vary over time but
may be maintained in such a manner that the statistical accuracy
does not drop below a certain threshold. For example, if the
statistical accuracy of the model based on vehicle adjust output
drops below being 94% accurate at step 1944, a request for a new
model, or an updated node, may be made at step 1948 if such model
is available at step 1952, the new model may be retrieved and/or
provided by the autonomous vehicle model generation system 416
automatically. Statistically accuracy may include a measure of time
for which user intervention was not needed vs a measure of time for
which user intervention was needed. Alternatively, or in addition,
statistically accuracy may include a measure of an output, such as
a steering adjustment, throttle adjustment, and/or braking
adjustment compared to a correct output, where a correct output may
be from another vehicle, another system, a post processing
technique (e.g., image data is processed at a location other than
the autonomous vehicle and such processing may provide different
steering angle adjustments, throttle adjustments, braking
adjustments, than a model executing at the autonomous vehicle in
real-time. In some instances, a statistical accuracy may be based
on based on at least one of a quantity of course corrections or a
quantity of course deviations, where a course correction includes
determining that an input associated with a manual override was
received. In some instances, a course deviation includes
determining that a path traveled by an autonomous vehicle is
different from a projected path traveled by the autonomous vehicle.
For example, an actual path, due in part to user
engagement/disengagement for example, may alter an actual path
traveled by the autonomous vehicle from a projected path. The
method 1940 may then end at step 1956.
[0210] FIG. 20 depicts one or more methods for receiving and/or
requesting a new model in accordance with embodiments of the
present disclosure. The method 2000 may begin at step 2004 and may
proceed to step 2008, where in a first instance, model input
parameters may be evaluated to determine if the input parameters
comply with model requirements. For example, an input for a
location may be provided to a model, for example, autonomous
vehicle model 452. A specific autonomous vehicle model 452 may be
valid for a specified location. If the input location is outside
the valid location, for example at steps 2012 and 2016, a new model
may be requested and/or received. Similarly, such input parameters
may apply to various times of day, various real-time weather
conditions, various times of year, and/or various changes in a
user/vehicle.
[0211] In accordance with examples of the present disclosure, an
autonomous vehicle model 452 may be restricted to or otherwise
limited to working within a geographic area or region. For example,
an autonomous vehicle model 452 may only be approved, certified, or
otherwise designated to operate in a specific region, such as but
not limited to a mountainous region, a flat region, a hilly region,
a dry region, a wet region, a snowy region, a city, an urban area,
a rural area, a state, a territory, and/or a country. Accordingly,
an input location may be received, for example, from a GPS receiver
or otherwise, providing a location of an autonomous vehicle. If the
autonomous vehicle approaches a boundary, such as a geofenced
boundary, or otherwise a distance between the vehicle and/or the
boundary is less than a threshold, the autonomous vehicle model 452
may be requested and/or otherwise received at the vehicle. In some
instances, the autonomous vehicle model 452 may be pushed to the
autonomous vehicle. The new autonomous vehicle model 452 may be
associated with the approaching region such that the autonomous
vehicle includes a geographically relevant autonomous vehicle
model.
[0212] In accordance with some examples, one or more portions of an
autonomous vehicle model may be associated with a period of time
for which it is valid. For example, an autonomous vehicle model may
be provided for a period of time for which it is most accurate,
such as for a specific season (e.g., fall, winter, spring, summer),
for a specific weather event (e.g., snow, thunderstorm, rain, hail,
sand, etc.), or for another atmospheric condition, such as
temperature changes, cloudiness, lighting condition, etc.).
[0213] In accordance with some examples, an autonomous vehicle
model 452 or a portion of the autonomous vehicle model 452 may be
updated. For example, a specific node (for example 304C) associated
with identifying one or more lane markers may be updated at a first
time, while a specific node (for example, 304I/304J) associated
with controlling a steering angle of a vehicle may be updated at a
second time. Alternatively, or in addition, both of the previously
mentioned nodes may be updated at a same time. In some instances,
the update may include a parameter, such as a single threshold; in
other instances, an entire node may be replaced with a node
included in the update.
[0214] In addition, a number of course deviations and/or
corrections may be utilized to determine if a new autonomous
vehicle model 452 is needed and/or should be updated. In accordance
with embodiments of the present disclosure, a first course of
action, such as providing a steering adjustment, may be determined
at step 2024. If an actual steering adjustment is different from
the suggested steering adjustment, for example at step 2028, a new
autonomous vehicle model may be requested and/or received at step
2020. The method may end at step 2040.
[0215] Similarly, a number of driver engagements and/or
disengagements may be monitored over time. As a number of driver
engagements, that is a number of times a driver corrects a course
of action (for example, provides a steering adjustment), changes a
parameter or setting, and/or intervenes in some manner is tracked
and if such number of interventions exceeds a threshold, a new
model may be requested and/or received. Importantly, the threshold
may vary in accordance with or otherwise based on time, location,
date, weather, and/or other input parameters as previously
described.
[0216] FIG. 21 depicts an example method 2100 in accordance with
embodiments of the present disclosure. The method 2100 may be
directed to updating one or more autonomous vehicle models as
previously described. Alternatively, or in addition, the method
2100 may be directed to updating a portion of one or more
autonomous vehicle models as previously described. That is, an
entirety of an autonomous vehicle model 452 may be modified,
updated, changed, and/or replaced in some instances, while in other
instances, one or more nodes 304 of a model 452 may be updated,
changed, modified, or deleted. Alternatively, or in addition, one
or neurons, layer of neurons, and/or pooling and subsampling may be
modified, deleted, added, or otherwise changed. As provided in FIG.
21, an indication that a change to an autonomous vehicle model 452
may be received at step 2108. At step 2112, one or more nodes,
layers, neurons, nodes, etc. . . . may be determined to which the
update received at step 2108 has been received. At step 2116, the
updated mode and/or node may be received. At step 2120, the model
and/or node may be replaced and verified at step 2124. The method
may then end at step 2128.
[0217] The processor 204 may also continually monitor the rate of
speed, rate of deceleration, and/or the rate of acceleration. Thus,
based on sensor data from an accelerometer, the acceleration system
244, deceleration system 240, the image sensors, etc., the
processor 204 can compare a rate of speed, acceleration, and/or
deceleration to a predetermined threshold. IF the rate meets and/or
crosses the threshold, the processor can take a predetermined
action. For example, if the deceleration crosses a threshold, the
processor 204 may automatically engage the hazard lights to warn of
other vehicles approaching from behind of the increased
deceleration. In other embodiments, the action may occur from
measuring a change in rate of another vehicle being sensed by the
image sensors.
[0218] The processor 204 can also monitor situations while the
vehicle 104 is stopped but still in transit. For example, the
processor 204 can receive image data associated with an
intersection. When a traffic signal/light changes, e.g., goes from
red to green, the processor 204 may start a timer. If the driver
does not move the vehicle during a predetermined period of time,
e.g., 2 seconds, the processor 204 can send a signal to the audio
system of the vehicle 104 to create an audible alarm for the user.
The period of time and the type and volume of the alarm may be
configured by the user. Thus, the user can prevent situations where
other people may honk at the user for stalling at a green
light.
[0219] In some examples, the processor 204 may determine that an
issue or condition exists with another object based on received
image data. For example, at an intersection, a light may change
from red to green; however, a vehicle in front of the autonomous
vehicle may not move. Thus, the processor may determine that an
issue exists with another vehicle in proximity to the autonomous
vehicle having the processor.
[0220] In still other situations, the processor 204 can monitor
other conditions in the driving environment. For example, the
processor 204 may monitor the location, speed, direction, and other
characteristics of the lanes and other vehicles to the side and
behind the vehicle 104. If a situation arises that may result in an
accident, the processor 204 can send an audible alarm or usurp
driver control to keep the vehicle and the occupants in the vehicle
safe. For example, the processor 204 can identify which lane the
vehicle is in. The processor 204 can also continuously monitor and
identify open lanes to the right or left of the vehicle 104. Should
a another vehicle be approaching fast from the rear and present an
imminent threat, the processor 204 can send an audible alert to the
user to "Merge left immediately," or provide a similar
announcement. In other situations, if the user does not respond to
the alert or without giving an alert, the processor 204 can control
the vehicle 104 to move automatically into the open lane or
area.
[0221] Any of the steps, functions, and operations discussed herein
can be performed continuously and automatically.
[0222] The exemplary systems and methods of this disclosure have
been described in relation to vehicle systems and vehicles.
However, to avoid unnecessarily obscuring the present disclosure,
the preceding description omits a number of known structures and
devices. This omission is not to be construed as a limitation of
the scope of the claimed disclosure. Specific details are set forth
to provide an understanding of the present disclosure. It should,
however, be appreciated that the present disclosure may be
practiced in a variety of ways beyond the specific detail set forth
herein.
[0223] Furthermore, while the exemplary embodiments illustrated
herein show the various components of the system collocated,
certain components of the system can be located remotely, at
distant portions of a distributed network, such as a LAN and/or the
Internet, or within a dedicated system. Thus, it should be
appreciated, that the components of the system can be combined into
one or more devices, such as a server, communication device, or
collocated on a particular node of a distributed network, such as
an analog and/or digital telecommunications network, a
packet-switched network, or a circuit-switched network. It will be
appreciated from the preceding description, and for reasons of
computational efficiency, that the components of the system can be
arranged at any location within a distributed network of components
without affecting the operation of the system. For example, the
various components can be located in a switch such as a PBX and
media server, gateway, in one or more communications devices, at
one or more users' premises, or some combination thereof.
Similarly, one or more functional portions of the system could be
distributed between a telecommunications device(s) and an
associated computing device.
[0224] Furthermore, it should be appreciated that the various links
connecting the elements can be wired or wireless links, or any
combination thereof, or any other known or later developed
element(s) that is capable of supplying and/or communicating data
to and from the connected elements. These wired or wireless links
can also be secure links and may be capable of communicating
encrypted information. Transmission media used as links, for
example, can be any suitable carrier for electrical signals,
including coaxial cables, copper wire, and fiber optics, and may
take the form of acoustic or light waves, such as those generated
during radio-wave and infra-red data communications.
[0225] While the flowcharts have been discussed and illustrated in
relation to a particular sequence of events, it should be
appreciated that changes, additions, and omissions to this sequence
can occur without materially affecting the operation of the
disclosed embodiments, configuration, and aspects.
[0226] A number of variations and modifications of the disclosure
can be used. It would be possible to provide for some features of
the disclosure without providing others.
[0227] In yet another embodiment, the systems and methods of this
disclosure can be implemented in conjunction with a special purpose
computer, a programmed microprocessor or microcontroller and
peripheral integrated circuit element(s), an ASIC or other
integrated circuit, a digital signal processor, a hard-wired
electronic or logic circuit such as discrete element circuit, a
programmable logic device or gate array such as PLD, PLA, FPGA,
PAL, special purpose computer, any comparable means, or the like.
In general, any device(s) or means capable of implementing the
methodology illustrated herein can be used to implement the various
aspects of this disclosure. Exemplary hardware that can be used for
the present disclosure includes computers, handheld devices,
telephones (e.g., cellular, Internet enabled, digital, analog,
hybrids, and others), and other hardware known in the art. Some of
these devices include processors (e.g., a single or multiple
microprocessors), memory, nonvolatile storage, input devices, and
output devices. Furthermore, alternative software implementations
including, but not limited to, distributed processing or
component/object distributed processing, parallel processing, or
virtual machine processing can also be constructed to implement the
methods described herein.
[0228] In yet another embodiment, the disclosed methods may be
readily implemented in conjunction with software using object or
object-oriented software development environments that provide
portable source code that can be used on a variety of computer or
workstation platforms. Alternatively, the disclosed system may be
implemented partially or fully in hardware using standard logic
circuits or VLSI design. Whether software or hardware is used to
implement the systems in accordance with this disclosure is
dependent on the speed and/or efficiency requirements of the
system, the particular function, and the particular software or
hardware systems or microprocessor or microcomputer systems being
utilized.
[0229] In yet another embodiment, the disclosed methods may be
partially implemented in software that can be stored on a storage
medium, executed on programmed general-purpose computer with the
cooperation of a controller and memory, a special purpose computer,
a microprocessor, or the like. In these instances, the systems and
methods of this disclosure can be implemented as a program embedded
on a personal computer such as an applet, JAVA.RTM. or CGI script,
as a resource residing on a server or computer workstation, as a
routine embedded in a dedicated measurement system, system
component, or the like. The system can also be implemented by
physically incorporating the system and/or method into a software
and/or hardware system.
[0230] Although the present disclosure describes components and
functions implemented in the embodiments with reference to
particular standards and protocols, the disclosure is not limited
to such standards and protocols. Other similar standards and
protocols not mentioned herein are in existence and are considered
to be included in the present disclosure. Moreover, the standards
and protocols mentioned herein and other similar standards and
protocols not mentioned herein are periodically superseded by
faster or more effective equivalents having essentially the same
functions. Such replacement standards and protocols having the same
functions are considered equivalents included in the present
disclosure.
[0231] The present disclosure, in various embodiments,
configurations, and aspects, includes components, methods,
processes, systems and/or apparatus substantially as depicted and
described herein, including various embodiments, subcombinations,
and subsets thereof. Those of skill in the art will understand how
to make and use the systems and methods disclosed herein after
understanding the present disclosure. The present disclosure, in
various embodiments, configurations, and aspects, includes
providing devices and processes in the absence of items not
depicted and/or described herein or in various embodiments,
configurations, or aspects hereof, including in the absence of such
items as may have been used in previous devices or processes, e.g.,
for improving performance, achieving ease, and/or reducing cost of
implementation.
[0232] In accordance with at least one example of the present
disclosure, a method is provided, where the method includes:
receiving sensor data associated with a driving environment of a
vehicle; determining an object within the driving environment;
determining if the object is a second vehicle; if the object is a
second vehicle, changing an autonomous driving mode to follow the
second vehicle automatically; and if the object is not a second
vehicle, changing the autonomous driving mode to follow a lane
viewed in the driving environment. At least one aspect of the above
method includes where the sensor data is an image of the driving
environment. At least one aspect of the above method includes where
determining the object comprises automatically drawing a box over
the object in the image. At least one aspect of the above method
includes where determining the object comprises executing a machine
learning model that identifies the object in the box as a vehicle.
At least one aspect of the above method includes where the box
delineates the extents of the object within the image. At least one
aspect of the above method includes where a center of the box is
determined. At least one aspect of the above method includes where
to follow the second vehicle, a position of the center of the box
is maintained in subsequent images. At least one aspect of the
above method includes where the second vehicle is in a caravan with
the vehicle. At least one aspect of the above method includes where
determining if the object is a second vehicle comprises identifying
the second vehicle as part of the caravan. At least one aspect of
the above method includes where the second vehicle is identified by
indicia on the vehicle. In accordance with at least one example of
the present disclosure, a system is provided, where the system
includes a memory and a processor in communication with the memory,
wherein the processor executes instructions stored in the memory,
which cause the processor to execute the above method, at least one
aspect of the above method, or combinations thereof. In accordance
with at least one example of the present disclosure, a
non-transitory computer readable medium is provided, where the
non-transitory computer readable medium includes instructions
stored thereon, which when executed by a processor cause the
processor to execute the above method, at least one aspect of the
above method, or combinations thereof.
[0233] In accordance with at least one example of the present
disclosure, a method is provided, where the method includes:
entering, by a first vehicle, an autonomous driving mode that
follows a second vehicle; determining a change in a second position
of the second vehicle while the first vehicle is following the
second vehicle; determining if the change in the second position
requires adjusting a first position of the first vehicle; if the
change in the second position requires adjusting the first position
of the vehicle, changing automatically the first position of the
first vehicle to match the change in the second position; and if
the change in the second position does not require adjusting the
first position of the vehicle, ignoring the change in the second
position. At least one aspect of the above method includes where
the second position is determined by sensor data of a driving
environment associated with the first vehicle, and wherein the
sensor data is at least an image of the driving environment. At
least one aspect of the above method includes where the second
position is determined by drawing a box over the second vehicle in
the image. At least one aspect of the above method includes where
the box delineates extents of the second vehicle within the image.
At least one aspect of the above method includes where a center of
the box is determined. At least one aspect of the above method
includes where to follow the second vehicle, the first vehicle
automatically adjusts a steering angle to maintain a position of
the center of the box in subsequent images. At least one aspect of
the above method includes where, when turning, the position of the
center of the box in a subsequent image is adjusted by at least one
pixel. At least one aspect of the above method includes where the
second vehicle is in a caravan with the first vehicle. At least one
aspect of the above method includes where determining if an object
in the image is a second vehicle comprises identifying the second
vehicle as part of the caravan. At least one aspect of the above
method includes where the second vehicle is identified by indicia
on the vehicle. In accordance with at least one example of the
present disclosure, a system is provided, where the system includes
a memory and a processor in communication with the memory, wherein
the processor executes instructions stored in the memory, which
cause the processor to execute the above method, at least one
aspect of the above method, or combinations thereof. In accordance
with at least one example of the present disclosure, a
non-transitory computer readable medium is provided, where the
non-transitory computer readable medium includes instructions
stored thereon, which when executed by a processor cause the
processor to execute the above method, at least one aspect of the
above method, or combinations thereof.
[0234] In accordance with at least one example of the present
disclosure, a method is provided, where the method includes:
entering, by a first vehicle, an autonomous driving mode that
follows a second vehicle; determining a change in a second position
of the second vehicle while the first vehicle is following the
second vehicle; continuing to follow the second vehicle; and
maintaining an association with the second vehicle without matching
the change to the second position. At least one aspect of the above
method includes where the second position is determined by sensor
data of a driving environment associated with the first vehicle,
and wherein the sensor data is at least an image of the driving
environment. At least one aspect of the above method includes where
the second position is determined by drawing a box over the second
vehicle in the image. At least one aspect of the above method
includes where the box delineates extents of the second vehicle
within the image. At least one aspect of the above method includes
where a center of the box is determined. At least one aspect of the
above method includes where to follow the second vehicle, the first
vehicle automatically maintains a steering angle to maintain a
direction of the first vehicle while a visual location of the
second vehicle changes position in subsequent images. At least one
aspect of the above method includes where the second vehicle
changes lanes and the first vehicle maintains a first position in
another lane. At least one aspect of the above method includes
where the second vehicle is in a caravan with the vehicle. At least
one aspect of the above method includes where determining if an
object in the image is a second vehicle comprises identifying the
second vehicle as part of the caravan. At least one aspect of the
above method includes where the second vehicle is identified by
indicia on the vehicle. In accordance with at least one example of
the present disclosure, a system is provided, where the system
includes a memory and a processor in communication with the memory,
wherein the processor executes instructions stored in the memory,
which cause the processor to execute the above method, at least one
aspect of the above method, or combinations thereof. In accordance
with at least one example of the present disclosure, a
non-transitory computer readable medium is provided, where the
non-transitory computer readable medium includes instructions
stored thereon, which when executed by a processor cause the
processor to execute the above method, at least one aspect of the
above method, or combinations thereof.
[0235] In accordance with at least one example of the present
disclosure, a method is provided, where the method includes:
receiving an indication of a second vehicle to follow, by a first
vehicle, while in an autonomous driving mode, wherein the
indication provides identifying information associated with the
second vehicle; determining if the second vehicle is visible in an
image from a sensor of the first vehicle; if the second vehicle is
visible in the image from a sensor of the first vehicle,
automatically associating the first vehicle with the second vehicle
to cause the first vehicle to follow the second vehicle
automatically; and if the second vehicle is not visible in the
image from a sensor of the first vehicle, continuing to search for
the second vehicle while autonomously driving in a different mode.
At least one aspect of the above method includes where the second
vehicle is in a caravan with the vehicle. At least one aspect of
the above method includes where the first vehicle determines if an
object in the image is the second vehicle. At least one aspect of
the above method includes where determining if an object in the
image is the second vehicle comprises identifying the second
vehicle as part of the caravan. At least one aspect of the above
method includes where the second vehicle is identified by indicia
on the vehicle. At least one aspect of the above method includes
where the second position is determined by drawing a box over the
second vehicle in the image. At least one aspect of the above
method includes where the box delineates extents of the second
vehicle within the image, wherein a center of the box is
determined, and wherein to follow the second vehicle, a position of
the center of the box is maintained in subsequent images. At least
one aspect of the above method includes where when the second
vehicle is not visible in the image from a sensor of the first
vehicle, the second vehicle provides breadcrumbs to the first
vehicle to follow. At least one aspect of the above method includes
where the breadcrumbs are Global Positioning Satellite (GPS)
coordinates for the second vehicle at some moment in time. At least
one aspect of the above method includes where the first vehicle and
the second vehicle maintain a communication link when in the
caravan. In accordance with at least one example of the present
disclosure, a system is provided, where the system includes a
memory and a processor in communication with the memory, wherein
the processor executes instructions stored in the memory, which
cause the processor to execute the above method, at least one
aspect of the above method, or combinations thereof. In accordance
with at least one example of the present disclosure, a
non-transitory computer readable medium is provided, where the
non-transitory computer readable medium includes instructions
stored thereon, which when executed by a processor cause the
processor to execute the above method, at least one aspect of the
above method, or combinations thereof.
[0236] In accordance with at least one example of the present
disclosure, a method is provided, where the method includes:
following a second vehicle, by a first vehicle, while in an
autonomous driving mode; determining that the second vehicle is no
longer visible in an image from a sensor of the first vehicle;
locating a third vehicle to follow in the image; and changing the
autonomous mode to follow the third vehicle. At least one aspect of
the above method includes where the second vehicle deviates from a
route of the first vehicle. At least one aspect of the above method
includes where determining that the second vehicle is no longer
visible comprises: determining an object within the image; and
determining if the object is the second vehicle. At least one
aspect of the above method includes where locating a third vehicle
to follow in the image comprises automatically drawing a box over
the third vehicle in the image. At least one aspect of the above
method includes where the box delineates extents of the third
vehicle within the image. At least one aspect of the above method
includes where a center of the box is determined. At least one
aspect of the above method includes where to follow the third
vehicle, a position of the center of the box is maintained in
subsequent images. At least one aspect of the above method includes
where the third vehicle maintains a same route as the first
vehicle. At least one aspect of the above method includes where
when a third vehicle is no longer visible in the image, maintaining
the same route while changing to second autonomous driving mode. At
least one aspect of the above method includes where the second
autonomous driving mode is a lane follow mode. In accordance with
at least one example of the present disclosure, a system is
provided, where the system includes a memory and a processor in
communication with the memory, wherein the processor executes
instructions stored in the memory, which cause the processor to
execute the above method, at least one aspect of the above method,
or combinations thereof. In accordance with at least one example of
the present disclosure, a non-transitory computer readable medium
is provided, where the non-transitory computer readable medium
includes instructions stored thereon, which when executed by a
processor cause the processor to execute the above method, at least
one aspect of the above method, or combinations thereof.
[0237] In accordance with at least one example of the present
disclosure, a method is provided, where the method includes:
receiving image data from an image sensor of a first vehicle, while
in an autonomous driving mode; determining if a portion of the
image data includes an edge defining a lane; determining if a
second portion of the edge should be filtered; and filtering the
second portion from the image data to smooth the edge of the lane.
At least one aspect of the above method includes where determining
if a portion of the image data includes an edge defining a lane
comprises applying a machine learning (ML) model to analyze the
image for the edge. At least one aspect of the above method
includes where the ML model provide a set of two or more points on
the lane that define a location of the lane in the image. At least
one aspect of the above method includes where determining if a
second portion of the edge should be filtered comprises comparing
the location of a point to a previous location of an associated
point in a previous image. At least one aspect of the above method
includes where the location and the previous location deviate by
more than a predetermined threshold, filtering out the point as
defining the location of the lane. At least one aspect of the above
method includes where the two or more points are positioned at
predetermined intervals along a vertical axis of the image. At
least one aspect of the above method includes where the
predetermined threshold is greater for points lower in the image
than points higher in the image. At least one aspect of the above
method includes where the first vehicle automatically steers
between the two or more points defining a first lane and the two or
more points defining a second lane. At least one aspect of the
above method includes where the first vehicle automatically steers
to maintain a path of the vehicle a predetermined number of pixels
from the two or more points defining a first lane. In accordance
with at least one example of the present disclosure, a system is
provided, where the system includes a memory and a processor in
communication with the memory, wherein the processor executes
instructions stored in the memory, which cause the processor to
execute the above method, at least one aspect of the above method,
or combinations thereof. In accordance with at least one example of
the present disclosure, a non-transitory computer readable medium
is provided, where the non-transitory computer readable medium
includes instructions stored thereon, which when executed by a
processor cause the processor to execute the above method, at least
one aspect of the above method, or combinations thereof.
[0238] In accordance with at least one example of the present
disclosure, a method is provided, where the method includes:
determining if one or more calibration parameters are available for
an image acquisition device; if the one or more calibration
parameters are not available for the image acquisition device:
obtaining an image from the image acquisition device; determining a
vanishing point for the image; modifying at least one of the one or
more calibration parameters based on the determined vanishing point
for the image. At least one aspect of the above method includes
where the one or more calibration parameters include pitch and yaw
of the image acquisition device. At least one aspect of the above
method includes where the pitch is a first number of pixels, in a
vertical direction, a center of an image is from the vanishing
point. At least one aspect of the above method includes where the
yaw is a second number of pixels, in a horizontal direction, the
center of an image is from the vanishing point. At least one aspect
of the above method includes where the vanishing point is
determined from an object in the image. At least one aspect of the
above method includes where an edge of the object helps determine
the vanishing point. At least one aspect of the above method
includes where an angle of the edge indicates a location for the
vanishing point. At least one aspect of the above method includes
where the object is a lane marker. At least one aspect of the above
method includes where the object is a building. At least one aspect
of the above method includes where the calibration parameters are
stored and applied to subsequent images. In accordance with at
least one example of the present disclosure, a system is provided,
where the system includes a memory and a processor in communication
with the memory, wherein the processor executes instructions stored
in the memory, which cause the processor to execute the above
method, at least one aspect of the above method, or combinations
thereof. In accordance with at least one example of the present
disclosure, a non-transitory computer readable medium is provided,
where the non-transitory computer readable medium includes
instructions stored thereon, which when executed by a processor
cause the processor to execute the above method, at least one
aspect of the above method, or combinations thereof.
[0239] In accordance with at least one example of the present
disclosure, a method is provided, where the method includes:
receiving an image from a first image acquisition device;
identifying one or more features in the image; obtaining one or
more parameters associated with the identified one or more
features; obtaining calibration information for one or more of the
identified one or more features from a second image acquisition
device; and calibrating the first image acquisition device based
the one or more parameters and the calibration information from the
second image acquisition device. At least one aspect of the above
method includes where one or more features in the image comprises
an object. At least one aspect of the above method includes where
the one or more parameters associated with the identified one or
more features comprises a location of the object in the image. At
least one aspect of the above method includes where the location is
a number of pixels from a vanishing point in the image. At least
one aspect of the above method includes where the first image
acquisition device is associated with a first vehicle. At least one
aspect of the above method includes where the second image
acquisition device is associated with a second vehicle. At least
one aspect of the above method includes where the second vehicle
also sends a location of the second vehicle when the calibration
information for the second image acquisition device was obtained.
At least one aspect of the above method includes where calibrating
the first image acquisition device occurs periodically. At least
one aspect of the above method includes where the calibration
information includes pitch and yaw of the first image acquisition
device. At least one aspect of the above method includes where the
pitch is a first number of pixels, in a vertical direction, a
center of an image is from the vanishing point, and wherein the yaw
is a second number of pixels, in a horizontal direction, the center
of an image is from the vanishing point. In accordance with at
least one example of the present disclosure, a system is provided,
where the system includes a memory and a processor in communication
with the memory, wherein the processor executes instructions stored
in the memory, which cause the processor to execute the above
method, at least one aspect of the above method, or combinations
thereof. In accordance with at least one example of the present
disclosure, a non-transitory computer readable medium is provided,
where the non-transitory computer readable medium includes
instructions stored thereon, which when executed by a processor
cause the processor to execute the above method, at least one
aspect of the above method, or combinations thereof.
[0240] In accordance with at least one example of the present
disclosure, a method is provided, where the method includes:
receiving a first autonomous vehicle model; determining that at
least one of a location or a time associated with the first
autonomous vehicle model has changed; and updating the first
autonomous vehicle model to a second autonomous vehicle model based
on the change in the at least one of the location or the time. At
least one aspect of the above method includes: receiving an update
to apply to the first autonomous vehicle model; and updating the
autonomous vehicle model to the second autonomous vehicle model
based on the received update. At least one aspect of the above
method includes where the update includes one or more autonomous
vehicle model parameters for a portion of the first autonomous
vehicle model. At least one aspect of the above method includes
where the first autonomous vehicle model is associated with a first
geographical region and the second autonomous vehicle model is
associated with a second geographical region that is different from
the first geographical region. At least one aspect of the above
method includes: determining an approval status of the first
autonomous vehicle model in the first geographical region; and
based on the approval status of the first autonomous vehicle model
in the first geographical region, updating the first autonomous
vehicle model to the second autonomous vehicle model. At least one
aspect of the above method includes where the first autonomous
vehicle model includes an autonomous driving mode that is not
included in the second autonomous vehicle model. At least one
aspect of the above method includes: determining that a distance
between an autonomous vehicle and the second geographical region is
less than threshold; and updating the first autonomous vehicle
model to the second autonomous vehicle model based on the distance.
At least one aspect of the above method includes: determining that
a period of time associated with the first autonomous vehicle model
has expired; and updating the first autonomous vehicle model to the
second autonomous vehicle model based on the expiration of the
period of time. At least one aspect of the above method includes
where the period of time is based on at least one of a season, a
weather event, or an atmospheric condition. At least one aspect of
the above method includes where the first autonomous vehicle model
is received at an autonomous vehicle. In accordance with at least
one example of the present disclosure, a system is provided, where
the system includes a memory and a processor in communication with
the memory, wherein the processor executes instructions stored in
the memory, which cause the processor to execute the above method,
at least one aspect of the above method, or combinations thereof.
In accordance with at least one example of the present disclosure,
a non-transitory computer readable medium is provided, where the
non-transitory computer readable medium includes instructions
stored thereon, which when executed by a processor cause the
processor to execute the above method, at least one aspect of the
above method, or combinations thereof.
[0241] In accordance with at least one example of the present
disclosure, a method is provided, where the method includes:
receiving a first autonomous vehicle model; detecting a condition
associated with a performance of the first autonomous vehicle
model; determining that the first autonomous vehicle model should
be updated based on the detected condition; receiving at least one
update for the first autonomous vehicle model; applying the at
least one update; and generating a second autonomous vehicle model
based on the at least one update. At least one aspect of the above
method includes where detecting a condition associated with the
performance of the first autonomous vehicle model includes
determining that at least one of a quantity of course corrections
or a quantity of course deviations exceeded a threshold within a
period of time. At least one aspect of the above method includes
where a course correction includes determining that an input
associated with a manual override was received. At least one aspect
of the above method includes where the input associated with the
manual override includes one of a manual velocity change or manual
steering angle change. At least one aspect of the above method
includes where a course deviation includes determining that a path
traveled by an autonomous vehicle is different from a projected
path traveled by the autonomous vehicle. At least one aspect of the
above method includes where the threshold varies based on one or
more of time, location, date, or weather condition. At least one
aspect of the above method includes where the at least one update
includes one or more model parameters for a portion of the first
autonomous vehicle model. At least one aspect of the above method
includes: recording a location associated with one or more course
corrections and/or course deviations. At least one aspect of the
above method includes where the second autonomous vehicle model is
received at an autonomous vehicle. At least one aspect of the above
method includes where the second autonomous vehicle model is
generated at an autonomous vehicle. In accordance with at least one
example of the present disclosure, a system is provided, where the
system includes a memory and a processor in communication with the
memory, wherein the processor executes instructions stored in the
memory, which cause the processor to execute the above method, at
least one aspect of the above method, or combinations thereof. In
accordance with at least one example of the present disclosure, a
non-transitory computer readable medium is provided, where the
non-transitory computer readable medium includes instructions
stored thereon, which when executed by a processor cause the
processor to execute the above method, at least one aspect of the
above method, or combinations thereof.
[0242] In accordance with at least one example of the present
disclosure, a method is provided, where the method includes:
receiving a statistical accuracy associated with a first autonomous
vehicle model; determining that the statistical accuracy associated
with the first autonomous vehicle model indicates that the first
autonomous vehicle model is statistically incorrect; and updating
the first autonomous vehicle model to a second autonomous vehicle
model based on the determination that the first autonomous vehicle
model is statistically incorrect. At least one aspect of the above
method includes: receiving at least one update for the first
autonomous vehicle model; applying the at least one update to the
first autonomous vehicle model; and generating the second
autonomous vehicle model based on the at least one update. At least
one aspect of the above method includes where the at least one
update includes one or more model parameters for a portion of the
first autonomous vehicle model. At least one aspect of the above
method includes where the statistical accuracy associated with the
first autonomous vehicle model is based on at least one of a
quantity of course corrections or a quantity of course deviations.
At least one aspect of the above method includes where a course
correction includes determining that an input associated with a
manual override was received. At least one aspect of the above
method includes where a course deviation includes determining that
a path traveled by an autonomous vehicle is different from a
projected path traveled by the autonomous vehicle. At least one
aspect of the above method includes where determining that the
statistical accuracy associated with the first autonomous vehicle
model indicates that the first autonomous vehicle model is
statistically incorrect includes determining that the statistical
accuracy associated with the first autonomous vehicle model is less
than a threshold. At least one aspect of the above method includes:
providing the second autonomous vehicle model to an autonomous
vehicle. At least one aspect of the above method includes:
providing the second autonomous vehicle model to a second
autonomous vehicle. At least one aspect of the above method
includes where the second autonomous vehicle model is generated at
an autonomous vehicle. In accordance with at least one example of
the present disclosure, a system is provided, where the system
includes a memory and a processor in communication with the memory,
wherein the processor executes instructions stored in the memory,
which cause the processor to execute the above method, at least one
aspect of the above method, or combinations thereof. In accordance
with at least one example of the present disclosure, a
non-transitory computer readable medium is provided, where the
non-transitory computer readable medium includes instructions
stored thereon, which when executed by a processor cause the
processor to execute the above method, at least one aspect of the
above method, or combinations thereof.
[0243] In accordance with at least one example of the present
disclosure, a method is provided, where the method includes:
receiving an update associated with a first autonomous vehicle
model; determining one or more nodes of the first autonomous
vehicle model that are to be updated based on the received update;
and applying the update to the determined one or more nodes of the
first autonomous vehicle model to generate a second autonomous
vehicle model. At least one aspect of the above method includes
where applying the update to the determined one or more nodes of
the first autonomous vehicle model includes replacing the
determined one or more nodes of the first autonomous vehicle model.
At least one aspect of the above method includes where applying the
update to the determined one or more nodes of the first autonomous
vehicle model includes changing a parameter of the determined one
or more nodes of the first autonomous vehicle model. At least one
aspect of the above method includes where a first node of the one
or more nodes is associated with at least one of changing a
steering angle and/or changing a velocity of an autonomous vehicle.
At least one aspect of the above method includes where a second
node of the one or more nodes is associated with at least one of
receiving an image, processing the image, identifying one or more
objects in the image, and/or tracking the identified one or more
objects in the image. At least one aspect of the above method
includes where a second node of the one or more nodes is associated
with at least one of determining a path for an autonomous vehicle
to follow and/or determining an operating mode for an autonomous
vehicle. At least one aspect of the above method includes where
determining one or more nodes of the first autonomous vehicle model
that are to be updated is based on at least one of a location or a
time associated with the one or more nodes. At least one aspect of
the above method includes: providing the second autonomous vehicle
model to an autonomous vehicle. At least one aspect of the above
method includes: providing the second autonomous vehicle model to a
second autonomous vehicle. At least one aspect of the above method
includes where the second autonomous vehicle model is generated at
an autonomous vehicle. In accordance with at least one example of
the present disclosure, a system is provided, where the system
includes a memory and a processor in communication with the memory,
wherein the processor executes instructions stored in the memory,
which cause the processor to execute the above method, at least one
aspect of the above method, or combinations thereof. In accordance
with at least one example of the present disclosure, a
non-transitory computer readable medium is provided, where the
non-transitory computer readable medium includes instructions
stored thereon, which when executed by a processor cause the
processor to execute the above method, at least one aspect of the
above method, or combinations thereof.
[0244] In accordance with at least one example of the present
disclosure, a method is provided, where the method includes:
receiving an indication that an autonomous driving mode has been
disengaged by a user; determining when a cumulative number of user
disengagements of the autonomous driving mode, including the
indication that the autonomous driving mode has been disengaged by
the user, exceeds a threshold; requesting a portion of an
autonomous vehicle model based on the cumulative number of user
disengagements of the autonomous driving mode exceeding the
threshold; and receiving the requested portion of the autonomous
vehicle model. At least one aspect of the above method includes
where a user disengagement of the autonomous driving mode includes
implementing at least one course correction or correcting a course
deviation. At least one aspect of the above method includes where a
course correction includes determining that an input associated
with a manual override was received. At least one aspect of the
above method includes where the input associated with the manual
override includes one of a manual velocity change or manual
steering angle change. At least one aspect of the above method
includes where a course deviation includes determining that a path
traveled by an autonomous vehicle is different from a projected
path traveled by the autonomous vehicle. At least one aspect of the
above method includes where the threshold varies based on one or
more of time, location, date, or weather condition. At least one
aspect of the above method includes: generating a second autonomous
vehicle model by applying the received requested portion of the
autonomous vehicle model to an existing autonomous vehicle model.
At least one aspect of the above method includes: storing a
location associated with the indication that the autonomous driving
mode has been disengaged by the user. At least one aspect of the
above method includes where the requested portion of the autonomous
vehicle model is based on the stored location. At least one aspect
of the above method includes: providing the requested portion of
the autonomous vehicle model to an autonomous vehicle. In
accordance with at least one example of the present disclosure, a
system is provided, where the system includes a memory and a
processor in communication with the memory, wherein the processor
executes instructions stored in the memory, which cause the
processor to execute the above method, at least one aspect of the
above method, or combinations thereof. In accordance with at least
one example of the present disclosure, a non-transitory computer
readable medium is provided, where the non-transitory computer
readable medium includes instructions stored thereon, which when
executed by a processor cause the processor to execute the above
method, at least one aspect of the above method, or combinations
thereof.
[0245] In accordance with at least one example of the present
disclosure, a method is provided, where the method includes:
receiving image data from a sensor of a first vehicle; identifying
a first object based on the image data; determining that the first
object has changed; and determining that an issue exists when a
parameter corresponding to a change of the first object does not
change. At least one aspect of the above method includes:
identifying a second object based on the image data; and
determining that the issue exists with the second object when a
parameter associated with the second object does not change based
on the change of the first object. At least one aspect of the above
method includes where the first object is a traffic light and the
second object is a second vehicle. At least one aspect of the above
method includes: determining that an issue exists with the second
vehicle when the traffic light changes from red to green and a
distances between the first vehicle and the second vehicle does not
increase. At least one aspect of the above method includes:
determining that an issue exists with the second vehicle when the
traffic light changes from red to green and an acceleration of the
second vehicle does not change. At least one aspect of the above
method includes where the parameter corresponding to a change of
the first object is an acceleration of the first vehicle. At least
one aspect of the above method includes where the first vehicle is
static. At least one aspect of the above method includes: receiving
sensor data from a second sensor of the first vehicle; and
determining that the issue exists when the sensor data from the
second sensor exceeds a threshold. At least one aspect of the above
method includes: providing a notification to a user of the first
vehicle based on the determination that the issue exists. At least
one aspect of the above method includes where the first object is a
traffic light. In accordance with at least one example of the
present disclosure, a system is provided, where the system includes
a memory and a processor in communication with the memory, wherein
the processor executes instructions stored in the memory, which
cause the processor to execute the above method, at least one
aspect of the above method, or combinations thereof. In accordance
with at least one example of the present disclosure, a
non-transitory computer readable medium is provided, where the
non-transitory computer readable medium includes instructions
stored thereon, which when executed by a processor cause the
processor to execute the above method, at least one aspect of the
above method, or combinations thereof.
[0246] In accordance with at least one example of the present
disclosure, a method is provided, where the method includes:
receiving sensor data from a sensor of a first vehicle while the
first vehicle is in an autonomous drive mode; determining that a
parameter of a second vehicle followed by the first vehicle has
changed; determining that a quantity of parameter changes within a
period of time exceeds a threshold; and when the quantity of
parameter changes within the period of time exceeds the threshold,
automatically storing a condition of the second vehicle. At least
one aspect of the above method includes where the parameter of the
second vehicle corresponds to a lane change. At least one aspect of
the above method includes where the parameter of the second vehicle
corresponds to at least one of a change in velocity and/or a change
in acceleration. At least one aspect of the above method includes
where the condition of the second vehicle is an assessment of the
vehicle condition. At least one aspect of the above method includes
where the first vehicle provides a message indicating the condition
of the second vehicle. At least one aspect of the above method
includes where the message is provided to the second vehicle. At
least one aspect of the above method includes where the second
vehicle is not in an autonomous drive mode. At least one aspect of
the above method includes changing an operating parameter of the
first vehicle based on the stored condition of the second vehicle.
At least one aspect of the above method includes where the
operating parameter includes a disengagement from the autonomous
drive mode. At least one aspect of the above method includes
associating the stored condition of the second vehicle with a
previously stored condition of the second vehicle. In accordance
with at least one example of the present disclosure, a system is
provided, where the system includes a memory and a processor in
communication with the memory, wherein the processor executes
instructions stored in the memory, which cause the processor to
execute the above method, at least one aspect of the above method,
or combinations thereof. In accordance with at least one example of
the present disclosure, a non-transitory computer readable medium
is provided, where the non-transitory computer readable medium
includes instructions stored thereon, which when executed by a
processor cause the processor to execute the above method, at least
one aspect of the above method, or combinations thereof.
[0247] The foregoing discussion of the disclosure has been
presented for purposes of illustration and description. The
foregoing is not intended to limit the disclosure to the form or
forms disclosed herein. In the foregoing Detailed Description for
example, various features of the disclosure are grouped together in
one or more embodiments, configurations, or aspects for the purpose
of streamlining the disclosure. The features of the embodiments,
configurations, or aspects of the disclosure may be combined in
alternate embodiments, configurations, or aspects other than those
discussed above. This method of disclosure is not to be interpreted
as reflecting an intention that the claimed disclosure requires
more features than are expressly recited in each claim. Rather, as
the following claims reflect, inventive aspects lie in less than
all features of a single foregoing disclosed embodiment,
configuration, or aspect. Thus, the following claims are hereby
incorporated into this Detailed Description, with each claim
standing on its own as a separate preferred embodiment of the
disclosure.
[0248] Moreover, though the description of the disclosure has
included description of one or more embodiments, configurations, or
aspects and certain variations and modifications, other variations,
combinations, and modifications are within the scope of the
disclosure, e.g., as may be within the skill and knowledge of those
in the art, after understanding the present disclosure. It is
intended to obtain rights, which include alternative embodiments,
configurations, or aspects to the extent permitted, including
alternate, interchangeable and/or equivalent structures, functions,
ranges, or steps to those claimed, whether or not such alternate,
interchangeable and/or equivalent structures, functions, ranges, or
steps are disclosed herein, and without intending to publicly
dedicate any patentable subject matter.
[0249] The phrases "at least one," "one or more," "or," and
"and/or" are open-ended expressions that are both conjunctive and
disjunctive in operation. For example, each of the expressions "at
least one of A, B and C," "at least one of A, B, or C," "one or
more of A, B, and C," "one or more of A, B, or C," "A, B, and/or
C," and "A, B, or C" means A alone, B alone, C alone, A and B
together, A and C together, B and C together, or A, B and C
together.
[0250] The term "a" or "an" entity refers to one or more of that
entity. As such, the terms "a" (or "an"), "one or more," and "at
least one" can be used interchangeably herein. It is also to be
noted that the terms "comprising," "including," and "having" can be
used interchangeably.
[0251] The term "automatic" and variations thereof, as used herein,
refers to any process or operation, which is typically continuous
or semi-continuous, done without material human input when the
process or operation is performed. However, a process or operation
can be automatic, even though performance of the process or
operation uses material or immaterial human input, if the input is
received before performance of the process or operation. Human
input is deemed to be material if such input influences how the
process or operation will be performed. Human input that consents
to the performance of the process or operation is not deemed to be
"material."
[0252] Aspects of the present disclosure may take the form of an
embodiment that is entirely hardware, an embodiment that is
entirely software (including firmware, resident software,
micro-code, etc.) or an embodiment combining software and hardware
aspects that may all generally be referred to herein as a
"circuit," "module," or "system." Any combination of one or more
computer-readable medium(s) may be utilized. The computer-readable
medium may be a computer-readable signal medium or a
computer-readable storage medium.
[0253] A computer-readable storage medium may be, for example, but
not limited to, an electronic, magnetic, optical, electromagnetic,
infrared, or semiconductor system, apparatus, or device, or any
suitable combination of the foregoing. More specific examples (a
non-exhaustive list) of the computer-readable storage medium would
include the following: an electrical connection having one or more
wires, a portable computer diskette, a hard disk, a random access
memory (RAM), a read-only memory (ROM), an erasable programmable
read-only memory (EPROM or Flash memory), an optical fiber, a
portable compact disc read-only memory (CD-ROM), an optical storage
device, a magnetic storage device, or any suitable combination of
the foregoing. In the context of this document, a computer-readable
storage medium may be any tangible medium that can contain or store
a program for use by or in connection with an instruction execution
system, apparatus, or device.
[0254] A computer-readable signal medium may include a propagated
data signal with computer-readable program code embodied therein,
for example, in baseband or as part of a carrier wave. Such a
propagated signal may take any of a variety of forms, including,
but not limited to, electro-magnetic, optical, or any suitable
combination thereof. A computer-readable signal medium may be any
computer-readable medium that is not a computer-readable storage
medium and that can communicate, propagate, or transport a program
for use by or in connection with an instruction execution system,
apparatus, or device. Program code embodied on a computer-readable
medium may be transmitted using any appropriate medium, including,
but not limited to, wireless, wireline, optical fiber cable, RF,
etc., or any suitable combination of the foregoing.
[0255] The terms "determine," "calculate," "compute," and
variations thereof, as used herein, are used interchangeably and
include any type of methodology, process, mathematical operation or
technique.
[0256] Examples of the processors as described herein may include,
but are not limited to, at least one of Qualcomm.RTM.
Snapdragon.RTM. 800 and 801, Qualcomm.RTM. Snapdragon.RTM. 610 and
615 with 4G LTE Integration and 64-bit computing, Apple.RTM. A7
processor with 64-bit architecture, Apple.RTM. M7 motion
coprocessors, Samsung.RTM. Exynos.RTM. series, the Intel.RTM.
Core.TM. family of processors, the Intel.RTM. Xeon.RTM. family of
processors, the Intel.RTM. Atom.TM. family of processors, the Intel
Itanium.RTM. family of processors, Intel.RTM. Core.RTM. i5-4670K
and i7-4770K 22 nm Haswell, Intel.RTM. Core.RTM. i5-3570K 22 nm Ivy
Bridge, the AMD.RTM. FX.TM. family of processors, AMD.RTM. FX-4300,
FX-6300, and FX-8350 32 nm Vishera, AMD.RTM. Kaveri processors,
Texas Instruments.RTM. Jacinto C6000.TM. automotive infotainment
processors, Texas Instruments.RTM. OMAP.TM. automotive-grade mobile
processors, ARM.RTM. Cortex.TM.-M processors, ARM.RTM. Cortex-A and
ARM926EJ-S.TM. processors, other industry-equivalent processors,
and may perform computational functions using any known or
future-developed standard, instruction set, libraries, and/or
architecture.
[0257] The term "means" as used herein shall be given its broadest
possible interpretation in accordance with 35 U.S.C., Section
112(f) and/or Section 112, Paragraph 6. Accordingly, a claim
incorporating the term "means" shall cover all structures,
materials, or acts set forth herein, and all of the equivalents
thereof. Further, the structures, materials or acts and the
equivalents thereof shall include all those described in the
summary, brief description of the drawings, detailed description,
abstract, and claims themselves.
* * * * *