Systems and systems for identifying landmarks

• US 9,631,943 B2
• Filed: 09/22/2016
• Issued: 04/25/2017
• 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, Shalev-Shwartz, Shai
• Primary Examiner(s): Tarcza, Thomas
• Assistant Examiner(s): Pipala, Edward J
• Application Number:
  US15/273,138
• Publication Number:
  US 20170010120A1
• Time in Patent Office: 215 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 identifying a landmark for use in autonomous vehicle navigation is provided. The system includes at least one processor programmed to receive at least one identifier associated with the landmark; associate the landmark with a corresponding road segment; update an autonomous vehicle road navigation model relative to the corresponding road segment to include the at least one identifier associated with the landmark; and distribute the updated autonomous vehicle road navigation model to a plurality of autonomous vehicles. The at least one identifier is determined based on acquisition, from a camera associated with a host vehicle, of at least one image representative of an environment of the host vehicle; analysis of the at least one image to identify the landmark in the environment of the host vehicle; and analysis of the at least one image to determine the at least one identifier associated with the landmark.

Claims

1. A system for identifying a landmark for use in autonomous vehicle navigation, the system comprising:

at least one processor programmed to;

receive at least one identifier associated with the landmark;

associate the landmark with a corresponding road segment;

update an autonomous vehicle road navigation model relative to the corresponding road segment to include the at least one identifier associated with the landmark; and

distribute the updated autonomous vehicle road navigation model to a plurality of autonomous vehicles,

wherein the at least one identifier is determined based on;

acquisition, from a camera associated with a host vehicle, of at least one image representative of an environment of the host vehicle;

analysis of the at least one image to identify the landmark in the environment of the host vehicle; and

analysis of the at least one image to determine the at least one identifier associated with the landmark.

2. The system of claim 1, wherein the at least one identifier includes a position of the landmark.

3. The system of claim 1, wherein the at least one identifier includes a shape of the landmark.

4. The system of claim 1, wherein the at least one identifier includes a size of the landmark.

5. The system of claim 1, wherein the at least one identifier includes a distance of the landmark relative to another landmark.

6. The system of claim 1, wherein the at least one identifier is determined based on the landmark being identified as one of a plurality of landmark types.

7. The system of claim 6, wherein the landmark types include a traffic sign.

8. The system of claim 6, wherein the landmark types include a post.

9. The system of claim 6, wherein the landmark types include a directional indicator.

10. The system of claim 6, wherein the landmark types include a rectangular sign.

11. The system of claim 1, wherein the at least one identifier further includes a condensed signature representation.

12. The system of claim 11, wherein the condensed signature representation of the landmark is determined based on mapping an image of the landmark to a sequence of numbers of a predetermined data size.

13. The system of claim 11, wherein the condensed signature representation indicates an appearance of the landmark.

14. The system of claim 11, wherein the condensed signature representation indicates at least one of a color pattern of an image of the landmark or a brightness pattern of the image.

15. The system of claim 1, wherein the landmark includes at least one of a directional sign, a traffic sign, a lamppost, a road marking, and a business sign.

16. A method of identifying a landmark for use in autonomous vehicle navigation, the method comprising:

receiving, by a processor, at least one identifier associated with the landmark;

associating, by a processor, the landmark with a corresponding road segment;

updating, by a processor, an autonomous vehicle road navigation model relative to the corresponding road segment to include the at least one identifier associated with the landmark; and

distributing, by a processor, the updated autonomous vehicle road navigation model to a plurality of autonomous vehicles.

17. The method of claim 16, further comprising determining the at least one identifier.

18. The method of claim 16, wherein determining the at least one identifier includes:

acquiring, from a camera associated with a host vehicle, at least one image representative of an environment of the host vehicle;

analyzing the at least one image to identify the landmark in the environment of the host vehicle; and

analyzing the at least one image to determine the at least one identifier associated with the landmark.

19. The method of claim 16, wherein the at least one identifier includes a distance of the landmark relative to another landmark, and wherein determining the at least one identifier includes determining a distance of the landmark relative to another landmark.

20. The method of claim 16, wherein the at least one identifier further includes a condensed signature representation, and wherein determining the at least one identifier includes determining the condensed signature representation from the at least one image.