Navigation using local overlapping maps

• US 10,082,798 B2
• Filed: 08/09/2017
• Issued: 09/25/2018
• 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): Nguyen, Tan Quang
• Application Number:
  US15/673,331
• Publication Number:
  US 20170336801A1
• Time in Patent Office: 412 Days
• Field of Search: 701423
• 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 autonomously navigating a vehicle may include at least one processor. The processor may be programmed to receive a first navigational map associated with a first road segment and a second navigational map, different from the first navigational map, associated with a second road segment. The first road segment and the second road segment may overlap one another at an overlap segment. The processor may be programmed to determine at least a first autonomous navigational response for the vehicle along the first road segment based on analysis of the first navigational map; at least a second autonomous navigational response for the vehicle along the second road segment based on analysis of the second navigational map; and at least a third autonomous navigational response for the vehicle in the overlap segment based on at least one of the first navigational map and the second navigational map.

Claims

1. A system for autonomously navigating a vehicle based on a plurality of overlapping navigational maps, the system comprising:

at least one processor programmed to;

receive a first navigational map for use in autonomously controlling the vehicle, wherein the first navigational map is associated with a first road segment;

determine at least a first autonomous navigational response for the vehicle along the first road segment based on analysis of the first navigational map;

receive a second navigational map for use in autonomously controlling the vehicle, wherein the second navigational map is associated with a second road segment, wherein the first road segment is different from the second road segment, and wherein the first road segment and the second road segment overlap one another at an overlap segment;

determine at least a second autonomous navigational response for the vehicle along the second road segment based on analysis of the second navigational map; and

determine at least a third autonomous navigational response for the vehicle in the overlap segment based on at least one of the first navigational map and the second navigational map.

2. The system of claim 1, wherein each of the plurality of overlapping navigational maps has its own coordinate frame.

3. The system of claim 1, wherein each of the plurality of overlapping navigational maps includes a polynomial representation of a target trajectory along a road segment.

4. The system of claim 1, wherein each of the overlapping navigational maps is a sparse map having a data density of no more than 10 kilobytes per kilometer.

5. The system of claim 1, wherein the overlap segment has a length of at least 50 meters.

6. The system of claim 1, wherein the overlap segment has a length of at least 100 meters.

7. The system of claim 1, wherein the at least one processor is programmed to determine the third autonomous navigational response based on both the first navigational map and the second navigational map.

8. The system of claim 1, wherein the third autonomous navigational response is a combination of the first autonomous navigational response and the second autonomous navigational response.

9. The system of claim 8, wherein the third autonomous navigational response is an average of the first autonomous navigational response and the second autonomous navigational response.

10. The system of claim 1, wherein the processor is further programmed to:

determine an error between the first autonomous navigational response and the second autonomous navigational response; and

determine the third autonomous navigational response based on the second autonomous navigational response when the error is less than a threshold error.

11. 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 vehicle;

at least one processor programmed to;

determine a current location of the vehicle based on the at least one image;

receive a first navigational map associated with a first road segment;

determine at least a first autonomous navigational response for the vehicle based on analysis of the first navigational map, when the current location of the vehicle lies on the first navigational map;

receive a second navigational map associated with a second road segment different from the second road segment, the first road segment and the second road segment overlapping one another at an overlap segment;

determine at least a second autonomous navigational response for the vehicle based on analysis of the second navigational map when the current location of the vehicle lies on the second navigational map; and

determine at least a third autonomous navigational response for the vehicle based on at least one of the first navigational map and the second navigational map when the current location of the vehicle lies in the overlap segment.

12. The autonomous vehicle of claim 11, wherein each of the first navigational map and the second navigational map has its own coordinate frame.

13. The autonomous vehicle of claim 11, wherein each of the first navigational map and the second navigational map includes a polynomial representation of a target trajectory along a road segment.

14. The autonomous vehicle of claim 11, wherein the at least one processor is programmed to determine the third autonomous navigational response based on both the first navigational map and the second navigational map.

15. The autonomous vehicle of claim 11, wherein the third autonomous navigational response is a combination of the first autonomous navigational response and the second autonomous navigational response.

16. The autonomous vehicle of claim 11, wherein the processor is further programmed to:

determine an error between the first autonomous navigational response and the second autonomous navigational response; and

determine the third autonomous navigational response based on the second autonomous navigational response when the error is less than a threshold error.

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

receiving from an image capture device, at least one image representative of an environment of the vehicle;

determining, using a processor associated with the vehicle, a current location of the vehicle based on the at least one image;

receiving a first navigational map associated with a first road segment;

determining at least a first autonomous navigational response for the vehicle based on analysis of the first navigational map, when the current location of the vehicle lies on the first navigational map;

receiving a second navigational map associated with a second road segment different from the second road segment, the first road segment and the second road segment overlapping one another at an overlap segment;

determining at least a second autonomous navigational response for the vehicle based on analysis of the second navigational map when the current location of the vehicle lies on the second navigational map; and

determining at least a third autonomous navigational response for the vehicle based on at least one of the first navigational map and the second navigational map when the current location of the vehicle lies in the overlap segment.

18. The method of claim 17, wherein

each of the plurality of overlapping navigational maps has its own coordinate frame, and

each of the plurality of overlapping navigational maps includes a polynomial representation of a target trajectory along a road segment.

19. The method of claim 17, wherein determining the third autonomous navigational response includes determining a combination of the first autonomous navigational response and the second autonomous navigational response.

20. The method of claim 17, further including:

determining an error between the first autonomous navigational response and the second autonomous navigational response; and

determining the third autonomous navigational response based on the second autonomous navigational response when the error is less than a threshold error.