Autonomous vehicle navigation based on recognized landmarks

• US 9,623,905 B2
• Filed: 09/22/2016
• Issued: 04/18/2017
• 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): To, Tuan C.
• Application Number:
  US15/273,030
• Publication Number:
  US 20170008562A1
• Time in Patent Office: 208 Days
• 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 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 at least one image representative of an environment of the vehicle. The processor may also be programmed to analyze the at least one image to identify at least one recognized landmark. Further, the processor may be programmed to determine a current location of the vehicle relative to a predetermined road model trajectory associated with the road segment based, at least in part, on a predetermined location of the recognized landmark. In addition, the processor may be programmed to determine an autonomous steering action for the vehicle based on a direction of the predetermined road model trajectory at the determined current location of the vehicle relative to the predetermined road model trajectory.

Claims

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

at least one processor programmed to;

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

analyze the at least one image to identify at least one recognized landmark;

determine a current location of the vehicle relative to a predetermined road model trajectory associated with the road segment based, at least in part, on a predetermined location of the recognized landmark; and

determine an autonomous steering action for the vehicle based on a direction of the predetermined road model trajectory at the determined current location of the vehicle relative to the predetermined road model trajectory.

2. The system of claim 1, wherein the recognized 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 reflector, a landmark beacon, or a lamppost.

3. The system of claim 1, wherein the recognized landmark includes a change in spacing of lines on the road segment.

4. The system of claim 1, wherein the recognized landmark includes a sign for a business.

5. 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.

6. The system of claim 1, wherein navigation between recognized landmarks includes integration of vehicle velocity to determine a location of the vehicle along the predetermined road model trajectory.

7. The system of claim 1, wherein the processor is further programmed to adjust a steering system of the vehicle based on the autonomous steering action to navigate the vehicle.

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

determine a distance of the vehicle from the at least one recognized landmark; and

determine whether the vehicle is positioned on the predetermined road model trajectory associated with the road segment based on the distance.

9. The system of claim 8, wherein the processor is further programmed to adjust the steering system of the vehicle to move the vehicle from a current position of the vehicle to a position on the predetermined road model trajectory when the vehicle is not positioned on the predetermined road model trajectory.

10. A vehicle, comprising:

a body;

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

at least one processor programmed to;

receive from the at least one image capture device the at least one image;

analyze the at least one image to identify at least one recognized landmark;

determine a current location of the vehicle relative to a predetermined road model trajectory associated with the road segment based, at least in part, on a predetermined location of the recognized landmark; and

determine an autonomous steering action for the vehicle based on a direction of the predetermined road model trajectory at the determined current location of the vehicle relative to the predetermined road model trajectory.

11. The vehicle of claim 10, wherein the recognized 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 reflector, a landmark beacon, a lamppost, a change is spacing of lines on the road, or a sign for a business.

12. The 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 vehicle of claim 10, wherein navigation between recognized landmarks includes integration of vehicle velocity to determine a location of the vehicle along the predetermined road model trajectory.

14. The vehicle of claim 10, wherein the processor is further programmed to adjust the steering system of the vehicle based on the autonomous steering action to navigate the vehicle.

15. The vehicle of claim 10, wherein the processor is further programmed to:

determine a distance of the vehicle from the at least one recognized landmark; and

determine whether the vehicle is positioned on the predetermined road model trajectory associated with the road segment based on the distance.

16. The vehicle of claim 15, wherein the processor is further programmed to adjust the steering system of the vehicle to move the vehicle from a current position of the vehicle to a position on the predetermined road model trajectory when the vehicle is not positioned on the predetermined road model trajectory.

17. A method of navigating a vehicle, comprising:

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

analyzing, using a processor associated with the vehicle, the at least one image to identify at least one recognized landmark;

determining a current position of the vehicle relative to a predetermined road model trajectory associated with the road segment based, at least in part, on a predetermined location of the recognized landmark;

determining an autonomous steering action for the vehicle based on a direction of the predetermined road model trajectory at the determined current location of the vehicle relative to the predetermined road model trajectory; and

adjusting a steering system of the vehicle based on the autonomous steering action to navigate the vehicle.

18. The method of claim 17, further including determining a location of the vehicle along the predetermined road model trajectory by integrating the vehicle velocity.

19. The method of claim 17, further including:

determining, using the processor, a distance of the vehicle from the at least one recognized landmark; and

determining whether the vehicle is positioned on the predetermined road model trajectory associated with the road segment based on the distance.

20. The method of claim 19, further including:

determining a transformation required to move the vehicle from a current position of the vehicle to a position on the predetermined road model trajectory; and

adjusting the steering system of the vehicle based on the transformation.