Navigating in snow

• US 10,613,532 B2
• Filed: 08/09/2017
• Issued: 04/07/2020
• 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): Camby, Richard M
• Application Number:
  US15/673,343
• Publication Number:
  US 20170336794A1
• Time in Patent Office: 972 Days
• Field of Search: None
• 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

Systems and methods navigate a vehicle on a road with snow covering at least some lane markings and road edges. In one implementation, a system may include at least one processor programmed to receive from an image capture device, at least one environmental image forward of the vehicle, including areas where snow covers at least some lane markings and road edges, identify, based on an analysis of the at least one image, at least a portion of the road that is covered with snow and probable locations for road edges bounding the at least a portion of the road that is covered with snow, and cause the vehicle to navigate a navigational path that includes the identified portion of the road and falls within the determined probable locations for the road edges.

Claims

1. A system for navigating a vehicle on a road with snow covering at least some lane markings and road edges, the system comprising:

at least one processor programmed to;

receive from one or more image capture devices, a first environmental image forward of the vehicle and a second environmental image forward of the vehicle, the first environmental image and the second environmental image including areas where snow covers at least some lane markings and road edges, and wherein the first environmental image was captured using a first image capture parameter and the second environmental was captured using a second image capture parameter, the second image capture parameter being different from the first image capture parameter;

identify, based on an analysis of the first environmental image and the second environmental image, at least a portion of the road that is covered with snow and probable locations for road edges bounding the at least a portion of the road that is covered with snow; and

cause the vehicle to navigate a navigational path that includes the identified portion of the road and falls within the determined probable locations for the road edges.

2. The system of claim 1, wherein the analysis of the at least one image includes identifying at least one tire track in the snow.

3. The system of claim 1, wherein the analysis of the at least one image includes identifying a change of light across a surface of the snow.

4. The system of claim 1, wherein the analysis of the at least one image includes identifying a plurality of trees along an edge of the road.

5. The system of claim 1, wherein the analysis of the at least one image includes recognizing a change in curvature at a surface of the snow.

6. The system of claim 5, wherein the recognized change in curvature is determined to correspond to a probable location of a road edge.

7. The system of claim 1, wherein the analysis of the at least one image includes a pixel analysis of the at least one image in which at least a first pixel is compared to at least a second pixel in order to determine a feature associated with a surface of the snow covering at least some lane markings and road edges.

8. The system of claim 7, wherein the feature corresponds to an edge of a tire track.

9. The system of claim 7, wherein the feature corresponds to an edge of the road.

10. The system of claim 7, wherein the at least one processor is further programmed to cause the vehicle to navigate between determined edges of the road.

11. The system of claim 7, wherein the at least one processor is further programmed to cause the vehicle to navigate by at least partially following tire tracks in the snow.

23. The system of claim 1, wherein the first image capture parameter includes a predetermined field of view.

24. The system of claim 1, wherein the second image capture parameter includes a preset focal length.

25. The system of claim 1, wherein the first environmental image and the second environmental image area captured from two different locations.

26. The system of claim 1, wherein the first environmental image and the second environmental image are received from separate image capture devices, and the analysis of the first environmental image and the second environmental image includes a stereo image analysis.

27. The system of claim 1, wherein the first environmental image and the second environmental image are received from the same image capture device, and the analysis of the first environmental image and the second environmental image includes an optical flow analysis.

12. A method of navigating a vehicle on a road with snow covering at least some lane markings and road edges, the method comprising:

receiving from one or more image capture devices, a first environmental image forward of the vehicle and a second environmental image forward of the vehicle, the first environmental image and the second environmental image including areas where snow covers at least some lane markings and road edges, and wherein the first environmental image was captured using a first image capture parameter and the second environmental was captured using a second image capture parameter, the second image capture parameter being different from the first image capture parameter;

identifying, based on an analysis of the first environmental image and the second environmental image, at least a portion of the road that is covered with snow and probable locations for road edges bounding the at least a portion of the road that is covered with snow; and

causing the vehicle to navigate a navigational path that includes the identified portion of the road and falls within the determined probable locations for the road edges.

13. The method of claim 12, wherein the analysis of the at least one image includes identifying at least one tire track in the snow.

14. The method of claim 12, wherein the analysis of the at least one image includes identifying a change of light across a surface of the snow.

15. The method of claim 12, wherein the analysis of the at least one image includes identifying a plurality of trees along an edge of the road.

16. The method of claim 12, wherein the analysis of the at least one image includes recognizing a change in curvature at a surface of the snow.

17. The method of claim 16, wherein the recognized change in curvature is determined to correspond to a probable location of a road edge.

18. The method of claim 12, wherein the analysis of the at least one image includes a pixel analysis of the at least one image in which at least a first pixel is compared to at least a second pixel in order to determine a feature associated with a surface of the snow covering at least some lane markings and road edges.

19. The method of claim 18, wherein the feature corresponds to an edge of a tire track.

20. The method of claim 18, wherein the feature corresponds to an edge of the road.

21. The method of claim 18, further comprising causing the vehicle to navigate between determined edges of the road.

22. The method of claim 18, further comprising causing the vehicle to navigate by at least partially following tire tracks in the snow.