Crowd sourcing data for autonomous vehicle navigation

• US 9,760,090 B2
• Filed: 09/22/2016
• Issued: 09/12/2017
• 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: Shashua, Amnon, Gdalyahu, Yoram, Springer, Ofer, Reisman, Aran, Braunstein, Daniel
• Primary Examiner(s): Black, Thomas G
• Assistant Examiner(s): HUYNH, LUAT T
• Application Number:
  US15/272,802
• Publication Number:
  US 20170010106A1
• Time in Patent Office: 355 Days
• Field of Search: 701 28, 701301
• 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 method of processing vehicle navigation information for use in autonomous vehicle navigation is provided. The method includes receiving, by a server, navigation information from a plurality of vehicles. The navigation information from the plurality of vehicles is associated with a common road segment. The method also includes storing, by the server, the navigation information associated with the common road segment. The method also includes generating, by the server, at least a portion of an autonomous vehicle road navigation model for the common road segment based on the navigation information from the plurality of vehicles. The method further includes distributing, by the server, the autonomous vehicle road navigation model to one or more autonomous vehicles for use in autonomously navigating the one or more autonomous vehicles along the common road segment.

Claims

1. A method of processing vehicle navigation information for use in autonomous vehicle navigation, the method comprising:

receiving, by a server, navigation information from a plurality of vehicles, wherein the navigation information from the plurality of vehicles is associated with a common road segment;

storing, by the server, the navigation information associated with the common road segment;

generating, by the server, at least a portion of an autonomous vehicle road navigation model for the common road segment, wherein the autonomous vehicle road navigation model includes a three-dimensional spline corresponding to a target trajectory along the common road segment; and

distributing, by the server, the autonomous vehicle road navigation model to one or more autonomous vehicles for use in autonomously navigating the one or more autonomous vehicles along the common road segment.

2. The method of claim 1, wherein the navigation information includes a trajectory from each of the plurality of vehicles as each vehicle travels over the common road segment.

3. The method of claim 2, wherein the trajectory is determined based on sensed motion of a camera, including three-dimensional translation and three-dimensional rotational motions.

4. The method of claim 1, wherein the navigation information includes a lane assignment.

5. The method of claim 1, wherein generating at least a portion of the autonomous vehicle road navigation model includes clustering vehicle trajectories along the common road segment and determining the target trajectory along the common road segment based on the clustered vehicle trajectories.

6. The method of claim 5, wherein the target trajectory is associated with a single lane of the common road segment.

7. The method of claim 5, wherein the autonomous vehicle road navigation model includes a plurality of target trajectories, each associated with a separate lane of the common road segment.

8. The method of claim 5, wherein determining the target trajectory along the common road segment based on the clustered vehicle trajectories includes finding a mean or average trajectory based on the clustered vehicle trajectories.

9. The method of claim 1, wherein the three-dimensional spline is defined by less than 10 kilobytes per kilometer.

10. The method of claim 1, wherein the autonomous vehicle road navigation model includes identification of at least one landmark, including a position of the at least one landmark.

11. The method of claim 10, wherein the position of the at least one landmark is determined based on position measurements performed using sensor systems associated with the plurality of vehicles.

12. The method of claim 11, wherein the position measurements are averaged to obtain the position of the at least one landmark.

13. The method of claim 10, wherein the at least one landmark includes at least one of a traffic sign, an arrow marking, a lane marking, a dashed lane marking, a traffic light, a stop line, a directional sign, a landmark beacon, or a lamppost.

14. The method of claim 1, wherein the three-dimensional spline includes one or more three-dimensional polynomials connecting a plurality of data points.

15. The method of claim 14, wherein at least one of the data points is associated with the navigation information.

16. The method of claim 1, wherein the navigation information includes road geometry data or road profile data.

17. A navigation system for a vehicle, the system comprising:

at least one processor programmed to;

receive from a camera, at least one environmental image associated with the vehicle;

analyze the at least one environmental image to determine navigation information related to the vehicle;

transmit the navigation information from the vehicle to a server;

receive, from the server, at least one update to an autonomous vehicle road navigation model based on the transmitted navigation information, wherein the autonomous vehicle road navigation model includes a three-dimensional spline corresponding to a target trajectory along the road segment; and

cause at least one navigational maneuver by the vehicle based on the updated autonomous vehicle road navigation model.

18. The navigation system of claim 17, wherein the navigation information includes a trajectory of the vehicle as the vehicle travels over the road segment.

19. A server for processing vehicle navigation information for use in autonomous vehicle navigation, comprising:

a communication unit configured to communicate with a plurality of vehicles; and

at least one processor programmed to;

receive, via the communication unit, the navigation information from the vehicles;

generate at least a portion of an autonomous vehicle road navigation model, wherein the autonomous vehicle road navigation model includes a three-dimensional spline corresponding to a target trajectory along a road segment; and

transmit at least the portion of the autonomous vehicle road navigation model to at least one of the vehicles to cause a navigational maneuver by the at least one of the vehicles based on the portion of the autonomous vehicle road navigation model.

20. The server of claim 19, wherein the navigation information includes a trajectory from each of the plurality of vehicles as each vehicle travels over the road segment.

21. The server of claim 19, wherein the portion of autonomous vehicle road navigation model includes an update to the autonomous vehicle road navigation model.

22. The server of claim 19, wherein the three-dimensional spline includes one or more three-dimensional polynomials connecting a plurality of data points.

23. The server of claim 22, wherein at least one of the data points is associated with the navigation information.

24. The server of claim 19, wherein the navigation information includes road geometry data or road profile data.