Autonomous vehicle tail alignment navigation

• US 9,690,293 B2
• Filed: 09/22/2016
• Issued: 06/27/2017
• Est. Priority Date: 02/10/2015
• Status: Active Grant

• Current Assignee: MobilEye Vision Technologies Ltd. (Intel Corporation)
• Original Assignee: MobilEye Vision Technologies Ltd. (Intel Corporation)
• Inventors: Braunstein, Daniel, Shashua, Amnon, Reisman, Aran, Tubis, Igor, Taieb, Yoav
• Primary Examiner(s): MARC-COLEMAN, MARTHE Y
• Application Number:
  US15/273,044
• Publication Number:
  US 20170010614A1
• Time in Patent Office: 278 Days
• Field of Search: 701 28, 701523, 382260, 34820799, 348E13017, 340937
• CPC Class Codes: B60W 2420/403 Image sensing, e.g. optical camera
• CPC Class Codes: B60W 2420/408 Radar; Laser, e.g. lidar
• CPC Class Codes: B60W 2552/30 Road curve radius
• CPC Class Codes: B60W 2552/53 Road markings, e.g. lane marker or crosswalk
• CPC Class Codes: B60W 2554/20 Static objects
• CPC Class Codes: B60W 2554/40 Dynamic objects, e.g. animals, windblown objects
• CPC Class Codes: B60W 2554/60 Traversable objects, e.g. speed bumps or curbs
• CPC Class Codes: B60W 2555/60 Traffic rules, e.g. speed limits or right of way
• CPC Class Codes: B60W 2556/35 Data fusion
• CPC Class Codes: B60W 2556/40 High definition maps
• CPC Class Codes: B60W 2556/45 External transmission of data to or from the vehicle
• CPC Class Codes: B60W 2556/50 of positioning data, e.g. GPS [Global Positioning System] data
• CPC Class Codes: B60W 2710/18 Braking system
• CPC Class Codes: B60W 2710/20 Steering systems
• CPC Class Codes: B60W 2720/10 Longitudinal speed
• CPC Class Codes: B60W 30/10 Path keeping cruise control for automatically following a preceding vehicle B60W30/165
• CPC Class Codes: B60W 30/14 Adaptive cruise control
• CPC Class Codes: B60W 30/18 Propelling the vehicle
• CPC Class Codes: B60W 40/06 Road conditions
• CPC Class Codes: B60W 60/00 Drive control systems specially adapted for autonomous road vehicles
• CPC Class Codes: B60W 60/0015 : specially adapted for safety
• CPC Class Codes: B60W 60/00274 : considering possible movement changes
• CPC Class Codes: B62D 15/025 : Active steering aids, e.g. helping the driver by actively influencing the steering system after environment evaluation B62D1/28 takes precedence
• CPC Class Codes: G01C 21/14 : by recording the course traversed by the object G01C21/16 takes precedence
• CPC Class Codes: G01C 21/1652 : with ranging devices, e.g. LIDAR or RADAR
• CPC Class Codes: G01C 21/1656 : with passive imaging devices, e.g. cameras
• CPC Class Codes: G01C 21/20 : Instruments for performing navigational calculations G01C21/24, G01C21/26 take precedence
• CPC Class Codes: G01C 21/30 : Map- or contour-matching
• CPC Class Codes: G01C 21/32 : Structuring or formatting of map data
• CPC Class Codes: G01C 21/34 : Route searching; Route guidance
• CPC Class Codes: G01C 21/3407 : specially adapted for specific applications
• CPC Class Codes: G01C 21/3476 : using point of interest [POI] information, e.g. a route passing visible POIs
• CPC Class Codes: G01C 21/36 : Input/output arrangements for on-board computers
• CPC Class Codes: G01C 21/3602 : Input other than that of destination using image analysis, e.g. detection of road signs, lanes, buildings, real preceding vehicles using a camera
• CPC Class Codes: G01C 21/3623 : using a camera or code reader, e.g. for optical or magnetic codes
• CPC Class Codes: G01C 21/3644 : Landmark guidance, e.g. using POIs or conspicuous other objects
• CPC Class Codes: G01C 21/3658 : Lane guidance
• CPC Class Codes: G01C 21/3691 : Retrieval, searching and output of information related to real-time traffic, weather, or environmental conditions arrangements for giving variable traffic instructions G08G1/09
• CPC Class Codes: G01C 21/3811 : Point data, e.g. Point of Interest [POI]
• CPC Class Codes: G01C 21/3819 : Road shape data, e.g. outline of a route
• CPC Class Codes: G01C 21/3822 : Road feature data, e.g. slope data
• CPC Class Codes: G01C 21/3841 : Data obtained from two or more sources, e.g. probe vehicles
• CPC Class Codes: G01C 21/3896 : Transmission of map data from central databases
• CPC Class Codes: G01S 13/867 : Combination of radar systems with cameras
• CPC Class Codes: G01S 19/10 : providing dedicated supplementary positioning signals
• CPC Class Codes: G01S 2013/93271 : in the front of the vehicles
• CPC Class Codes: G01S 5/0027 : of actual mobile position, i.e. position determined on mobile
• CPC Class Codes: G01S 5/16 : using electromagnetic waves other than radio waves
• CPC Class Codes: G05D 1/0088 : characterized by the autonomous decision making process, e.g. artificial intelligence, predefined behaviours using knowledge based models G06N5/00
• CPC Class Codes: G05D 1/0212 : with means for defining a desired trajectory involving a plurality of land vehicles G05D1/0287
• CPC Class Codes: G05D 1/0219 : ensuring the processing of the whole working surface
• CPC Class Codes: G05D 1/0221 : involving a learning process
• CPC Class Codes: G05D 1/0246 : using a video camera in combination with image processing means
• CPC Class Codes: G05D 1/0251 : extracting 3D information from a plurality of images taken from different locations, e.g. stereo vision stereoscopic image analysis H04N13/00; depth recovery from images G06T7/593
• CPC Class Codes: G05D 1/0253 : extracting relative motion information from a plurality of images taken successively, e.g. visual odometry, optical flow determining position or orientation from images G06T7/70
• CPC Class Codes: G05D 1/0278 : using satellite positioning signals, e.g. GPS
• CPC Class Codes: G05D 1/0287 : involving a plurality of land vehicles, e.g. fleet or convoy travelling traffic control systems for road vehicles G08G1/00, particularly anticollision systems G08G1/16
• CPC Class Codes: G06F 16/2379 : Updates performed during online database operations; commit processing
• CPC Class Codes: G06F 16/29 : Geographical information databases
• CPC Class Codes: G06T 2207/20081 : Training; Learning
• CPC Class Codes: G06T 2207/30256 : Lane; Road marking
• CPC Class Codes: G06T 2207/30261 : Obstacle
• CPC Class Codes: G06T 7/00 : Image analysis
• CPC Class Codes: G06V 20/56 : exterior to a vehicle by using sensors mounted on the vehicle
• CPC Class Codes: G06V 20/58 : Recognition of moving objects or obstacles, e.g. vehicles or pedestrians; Recognition of traffic objects, e.g. traffic signs, traffic lights or roads
• CPC Class Codes: G06V 20/582 : of traffic signs
• CPC Class Codes: G06V 20/584 : of vehicle lights or traffic lights
• CPC Class Codes: G06V 20/588 : Recognition of the road, e.g. of lane markings; Recognition of the vehicle driving pattern in relation to the road
• CPC Class Codes: G06V 20/63 : Scene text, e.g. street names
• CPC Class Codes: G06V 20/64 : Three-dimensional objects
• CPC Class Codes: G08G 1/0112 : from the vehicle, e.g. floating car data [FCD]
• CPC Class Codes: G08G 1/0129 : for creating historical data or processing based on historical data
• CPC Class Codes: G08G 1/0141 : for traffic information dissemination
• CPC Class Codes: G08G 1/0145 : for active traffic flow control
• CPC Class Codes: G08G 1/09623 : Systems involving the acquisition of information from passive traffic signs by means mounted on the vehicle G08G1/0967 takes precedence
• CPC Class Codes: G08G 1/096725 : where the received information generates an automatic action on the vehicle control
• CPC Class Codes: G08G 1/096805 : where the transmitted instructions are used to compute a route
• CPC Class Codes: G08G 1/167 : Driving aids for lane monitoring, lane changing, e.g. blind spot detection
• CPC Class Codes: H04L 67/12 : specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
• CPC Class Codes: H04W 4/44 : for communication between vehicles and infrastructures, e.g. vehicle-to-cloud [V2C] or vehicle-to-home [V2H]

Abstract

A system for navigating an autonomous vehicle along a road segment is disclosed. The system may have at least one processor. The processor may be programmed to receive from an image capture device, images representative of an environment of the autonomous vehicle. The processor may also be programmed to determine a travelled trajectory along the road segment based on analysis of the images. Further, the processor may be programmed to determine a current location of the autonomous vehicle along a predetermined road model trajectory based on analysis of one or more of the plurality of images. The processor may also be programmed to determine a heading direction based on the determined traveled trajectory. In addition, the processor may be programmed to determine a steering direction, relative to the heading direction, by comparing the traveled trajectory to the predetermined road model trajectory at the current location of the autonomous vehicle.

Claims

1. A system for autonomously navigating an autonomous vehicle along a road segment, the system comprising:

at least one processor programmed to;

receive from an image capture device, a plurality of images representative of an environment of the autonomous vehicle;

determine a traveled trajectory of the autonomous vehicle along the road segment based, at least in part, on analysis of one or more of the plurality of images;

determine a current location of the autonomous vehicle along a predetermined road model trajectory based on analysis of one or more of the plurality of images;

determine a heading direction for the autonomous vehicle based on the determined traveled trajectory; and

determine a steering direction for the autonomous vehicle, relative to the heading direction, by comparing the traveled trajectory to the predetermined road model trajectory at the current location of the autonomous vehicle.

2. The system of claim 1, wherein the comparison between the traveled trajectory and the predetermined road model trajectory includes determination of a transformation that reduces an error between the traveled trajectory and the predetermined road model trajectory.

3. The system of claim 2, wherein the processor is further programmed to adjust the steering system of the autonomous vehicle based on the transformation.

4. The system of claim 1, wherein the predetermined road model trajectory includes a three-dimensional polynomial representation of a target trajectory along the road segment.

5. The system of claim 1, wherein the predetermined road model trajectory is retrieved from a database stored in a memory included in the autonomous vehicle.

6. The system of claim 1, wherein the predetermined road model trajectory is retrieved from a database accessible to the autonomous vehicle over a wireless communications interface.

7. The system of claim 1, wherein the image capture device is included in the autonomous vehicle.

8. The system of claim 1, wherein determination of the steering direction is further based on one or more additional cues, including one or more of a left lane mark polynomial model, a right lane mark polynomial model, holistic path prediction, motion of a forward vehicle, determined free space ahead of the autonomous vehicle, and virtual lanes or virtual lane constraints determined based on positions of vehicles forward of the autonomous vehicle.

9. The system of claim 8, wherein determination of the steering direction is based on weights applied to the one or more additional cues.

10. An autonomous vehicle, comprising:

a body;

at least one image capture device configured to acquire at least one image representative of an environment of the autonomous vehicle; and

at least one processor programmed to;

receive from the image capture device, a plurality of images representative of the environment of the autonomous vehicle;

determine a traveled trajectory of the autonomous vehicle along the road segment based, at least in part, on analysis of one or more of the plurality of images;

determine a current location of the autonomous vehicle along a predetermined road model trajectory based on analysis of one or more of the plurality of images;

determine a heading direction for the autonomous vehicle based on the determined traveled trajectory; and

determine a steering direction for the autonomous vehicle, relative to the heading direction, by comparing the traveled trajectory to the predetermined road model trajectory at the current location of the autonomous vehicle.

11. The autonomous vehicle of claim 10, wherein the comparison between the traveled trajectory and the predetermined road model trajectory includes determination of a transformation that reduces an error between the traveled trajectory and the predetermined road model trajectory.

12. The autonomous vehicle of claim 10, wherein the predetermined road model trajectory includes a three-dimensional polynomial representation of a target trajectory along the road segment.

13. The autonomous vehicle of claim 10, wherein the predetermined road model trajectory is retrieved from one of a database stored in a memory included in the autonomous vehicle and a database accessible to the autonomous vehicle over a wireless communications interface.

14. The autonomous vehicle of claim 10, wherein determination of the steering direction is further based on one or more additional cues, including one or more of a left lane mark polynomial model, a right lane mark polynomial model, holistic path prediction, motion of a forward vehicle, determined free space ahead of the autonomous vehicle, and virtual lanes or virtual lane constraints determined based on positions of vehicles forward of the autonomous vehicle.

15. The autonomous vehicle of claim 14, wherein determination of the steering direction is based on weights applied to the one or more additional cues.

16. A method of navigating an autonomous vehicle, comprising:

receiving, from an image capture device, a plurality of images representative of an environment of the autonomous vehicle;

determining a traveled trajectory of the autonomous vehicle along the road segment based, at least in part, on analysis of one or more of the plurality of images;

determining a current location of the autonomous vehicle along a predetermined road model trajectory based on analysis of one or more of the plurality of images;

determining a heading direction for the autonomous vehicle based on the determined traveled trajectory; and

determining a steering direction for the autonomous vehicle, relative to the heading direction, by comparing the traveled trajectory to the predetermined road model trajectory at the current location of the autonomous vehicle.

17. The method of claim 16, wherein comparing the traveled trajectory to the predetermined road model trajectory includes determining a transformation that reduces an error between the traveled trajectory and the predetermined road model trajectory.

18. The method of claim 17, further including adjusting the steering system of the autonomous vehicle based on the transformation.

19. The method of claim 18, wherein determining the steering direction includes applying weights to the one or more additional cues.

20. The method of claim 16, wherein determining a steering direction is based on one or more additional cues, including one or more of a left lane mark polynomial model, a right lane mark polynomial model, holistic path prediction, motion of a forward vehicle, determined free space ahead of the autonomous vehicle, and virtual lanes or virtual lane constraints determined based on positions of vehicles forward of the autonomous vehicle.