Road model management based on selective feedback

• US 10,162,355 B2
• Filed: 09/22/2016
• Issued: 12/25/2018
• 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: Hayon, Gaby, Gdalyahu, Yoram, Stein, Gideon, Buberman, Ori
• Primary Examiner(s): Nguyen, Cuong H
• Application Number:
  US15/273,640
• Publication Number:
  US 20170010109A1
• Time in Patent Office: 824 Days
• Field of Search: 701517, 701 23, 701 24, 701 26, 701117
• 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 are provided for interacting with a plurality of autonomous vehicles. In one implementation, a navigation system for a vehicle may include a memory including a predetermined road model representative of at least one road segment and at least one processor. The at least one processor may be programmed to selectively receive, from the plurality of autonomous vehicles, road environment information based on navigation by the plurality of autonomous vehicles through their respective road environments, determine whether one or more updates to the predetermined road model are required based on the road environment information, and update the predetermined road model to include the one or more updates.

Claims

1. A system for interacting with a plurality of autonomous vehicles, the system comprising:

a memory including a predetermined road model representative of at least one road segment, wherein the predetermined road model includes at least a spline representation of a target path for the plurality of autonomous vehicles along the at least one road segment; and

at least one processor programmed to;

selectively receive, from the plurality of autonomous vehicles, road environment information based on navigation by the plurality of autonomous vehicles through their respective road environments, wherein selectively receiving the road environment information is accomplished by causing some of the plurality of autonomous vehicles travelling within a particular geographic region to transmit road environment information to the system and preventing others of the plurality of autonomous vehicles travelling within the particular geographic region from transmitting road environment information to the system;

determine whether one or more updates to the predetermined road model are required based on the received road environment information; and

update the predetermined road model to include the one or more updates.

2. The system of claim 1, wherein selectively receiving the road environment information further includes imposing a limitation on a frequency of information transmissions from a group of vehicles.

3. The system of claim 1, wherein selectively receiving the road environment information further includes imposing a limitation on a frequency of information transmissions from a particular vehicle.

4. The system of claim 1, wherein selectively receiving the road environment information further includes imposing a limitation on a frequency of information transmissions from one or more of the plurality of autonomous vehicles based on a determined road model confidence level associated with a particular geographic region.

5. The system of claim 1, wherein selectively receiving the road environment information further includes limiting information transmissions from one or more of the plurality of autonomous vehicles to only those transmissions that include a potential discrepancy with respect to at least one aspect of the predetermined road model.

6. A method for interacting with a plurality of autonomous vehicles, the method comprising:

selectively receiving, from the plurality of autonomous vehicles, road environment information based on navigation by the plurality of autonomous vehicles through their respective road environments, wherein selectively receiving the road environment information is accomplished by causing some of the plurality of autonomous vehicles travelling within a particular geographic region to transmit road environment information and preventing others of the plurality of autonomous vehicles travelling within the particular geographic region from transmitting road environment information;

determining whether one or more updates to a predetermined road model are required based on the received road environment information, wherein the predetermined road model includes at least a spline representation of a target path for the plurality of autonomous vehicles along at least one road segment; and

updating the predetermined road model to include the one or more updates.

7. The method of claim 6, wherein selectively receiving the road environment information further includes imposing a limitation on a frequency of information transmissions from one or more of the plurality of autonomous vehicles based on a determined road model confidence level associated with a particular geographic region.

8. The method of claim 6, wherein selectively receiving the road environment information further includes imposing a limitation on a frequency of information transmissions from a particular vehicle.

9. The method of claim 6, wherein selectively receiving the road environment information further includes imposing a limitation on a frequency of information transmissions from a group of vehicles.

10. The method of claim 6, wherein selectively receiving the road environment information further includes limiting information transmissions from one or more of the plurality of autonomous vehicles to only those transmissions that include a potential discrepancy with respect to at least one aspect of the predetermined road model.

11. A non-transitory computer-readable medium comprising instructions that are executable by at least one processor to cause the at least one processor to perform a method, the method comprising:

selectively receiving, from the plurality of autonomous vehicles, road environment information based on navigation by the plurality of autonomous vehicles through their respective road environments, wherein selectively receiving the road environment information is accomplished by causing some of the plurality of autonomous vehicles travelling within a particular geographic region to transmit road environment information and preventing others of the plurality of autonomous vehicles travelling within the particular geographic region from transmitting road environment information;

determining whether one or more updates to a predetermined road model are required based on the received road environment information, wherein the predetermined road model includes at least a spline representation of a target path for the plurality of autonomous vehicles along at least one road segment; and

updating the predetermined road model to include the one or more updates.

12. The computer-readable medium of claim 11, wherein selectively receiving the road environment information further includes imposing a limitation on a frequency of information transmissions from a particular vehicle.

13. The computer-readable medium of claim 11, wherein selectively receiving the road environment information further includes imposing a limitation on a frequency of information transmissions from a group of vehicles.

14. The computer-readable medium of claim 11, wherein selectively receiving the road environment information further includes limiting information transmissions from one or more of the plurality of autonomous vehicles to only those transmissions that include a potential discrepancy with respect to at least one aspect of the predetermined road model.