SPARSE MAP FOR AUTONOMOUS VEHICLE NAVIGATION

• US 20170010616A1
• Filed: 09/22/2016
• Published: 01/12/2017
• 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
• Granted Patent: US 9,665,100 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• 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 non-transitory computer-readable medium is provided. The computer-readable medium includes a sparse map for autonomous vehicle navigation along a road segment. The sparse map includes a polynomial representation of a target trajectory for the autonomous vehicle along the road segment, and a plurality of predetermined landmarks associated with the road segment. The plurality of predetermined landmarks are spaced apart by at least 50 meters, and the sparse map has a data density of no more than 1 megabyte per kilometer.

Claims

1. A non-transitory computer-readable medium including a sparse map for autonomous vehicle navigation along a road segment, the sparse map comprising:

a polynomial representation of a target trajectory for the autonomous vehicle along the road segment; and

a plurality of predetermined landmarks associated with the road segment, wherein the plurality of predetermined landmarks are spaced apart by at least 50 meters, and wherein the sparse map has a data density of no more than 1 megabyte per kilometer.

2. The non-transitory computer-readable medium of claim 1, wherein the polynomial representation is a three-dimensional polynomial representation.

3. The non-transitory computer-readable medium of claim 1, wherein the polynomial representation of the target trajectory is determined based on two or more reconstructed trajectories of prior traversals of vehicles along the road segment.

4. The non-transitory computer-readable medium of claim 1, wherein the plurality of predetermined landmarks include a traffic sign represented in the sparse map by no more than 50 bytes of data.

5. The non-transitory computer-readable medium of claim 1, wherein the plurality of predetermined landmarks include a directional sign represented in the sparse map by no more than 50 bytes of data.

6. The non-transitory computer-readable medium of claim 1, wherein the plurality of predetermined landmarks include a general purpose sign represented in the sparse map by no more than 100 bytes of data.

7. The non-transitory computer-readable medium of claim 1, wherein the plurality of predetermined landmarks include a generally rectangular object represented in the sparse map by no more than 100 bytes of data.

8. The non-transitory computer-readable medium of claim 7, wherein the representation of the generally rectangular object in the sparse map includes a condensed image signature associated with the generally rectangular object.

9. The non-transitory computer-readable medium of claim 1, wherein the plurality of predetermined landmarks are represented in the sparse map by parameters including landmark size, distance to previous landmark, landmark type, and landmark position.

10. The non-transitory computer-readable medium of claim 1, wherein the plurality of predetermined landmarks included in the sparse map are spaced apart by at least 2 kilometers.

11. The non-transitory computer-readable medium of claim 1, wherein the plurality of predetermined landmarks included in the sparse map are spaced apart by at least 1 kilometer.

12. The non-transitory computer-readable medium of claim 1, wherein the plurality of predetermined landmarks included in the sparse map are spaced apart by at least 100 meters.

13. The non-transitory computer-readable medium of claim 1, wherein the sparse map has a data density of no more than 100 kilobytes per kilometer.

14. The non-transitory computer-readable medium of claim 1, wherein the sparse map has a data density of no more than 10 kilobytes per kilometer.

15. The non-transitory computer-readable medium of claim 1, wherein the plurality of predetermined landmarks appear in the sparse map at a rate that is above a rate sufficient to maintain a longitudinal position determination accuracy within 1 meter.

16. An autonomous vehicle, comprising:

a body; and

a non-transitory computer-readable medium including a sparse map for autonomous vehicle navigation along a road segment, the sparse map comprising;

a polynomial representation of a target trajectory for the autonomous vehicle along the road segment; and

a plurality of predetermined landmarks associated with the road segment, wherein the plurality of predetermined landmarks are spaced apart by at least 50 meters, and wherein the sparse map has a data density of no more than 1 megabyte per kilometer; and

a processor configured to execute data included in the sparse map for providing autonomous vehicle navigation along the road segment.

17. The autonomous vehicle of claim 16, wherein the polynomial representation is a three-dimensional polynomial representation.

18. The autonomous vehicle of claim 16, wherein the polynomial representation of the target trajectory is determined based on two or more reconstructed trajectories of prior traversals of vehicles along the road segment.

19. An autonomous vehicle, comprising:

a body; and

a processor configured to receive data included in a sparse map and execute the data for autonomous vehicle navigation along a road segment, the sparse map comprising;

a polynomial representation of a target trajectory for the autonomous vehicle along the road segment; and

a plurality of predetermined landmarks associated with the road segment, wherein the plurality of predetermined landmarks are spaced apart by at least 50 meters, and wherein the sparse map has a data density of no more than 1 megabyte per kilometer.

20-28. -28. (canceled)