System and method for generating and communicating lane information from a host vehicle to a vehicle-to-vehicle network

• US 10,282,997 B2
• Filed: 06/30/2016
• Issued: 05/07/2019
• Est. Priority Date: 07/20/2015
• Status: Active Grant

• Current Assignee: New Eagle LLC (Woodward Incorporated)
• Original Assignee: Dura Operating LLC (Dura Automotive Systems)
• Inventors: Gignac, Donald Raymond, Thompson, Aaron Evans, Siddiqui, Danish Uzair, Patil, Rajashekhar, Thomas, Gordon M.
• Primary Examiner(s): Chu, Randolph I
• Application Number:
  US15/197,924
• Publication Number:
  US 20170025018A1
• Time in Patent Office: 1,041 Days
• Field of Search: None
• CPC Class Codes: G06F 18/2413 based on distances to training or reference patterns
• 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: G08G 1/161 Decentralised systems, e.g. inter-vehicle communication
• CPC Class Codes: G08G 1/163 involving continuous checking
• 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: H04N 7/181 for receiving images from a plurality of remote sources
• CPC Class Codes: H04W 4/023 using mutual or relative location information between multiple location based services [LBS] targets or of distance thresholds
• CPC Class Codes: H04W 4/025 using location based information parameters
• CPC Class Codes: H04W 4/46 for vehicle-to-vehicle communication [V2V]

Abstract

A method of generating and communicating lane information from a host vehicle to a vehicle-to-vehicle (V2V) network includes collecting visual data from a camera, detecting a lane within the visual data, generating a lane classification for the lane based on the visual data, assigning a confidence level to the lane classification, generating a lane distance estimate from the visual data, generating a lane model from the lane classification and the lane distance estimate, and transmitting the lane model and the confidence level to the V2V network.

Claims

1. A method of generating and communicating lane information from a host vehicle to a vehicle-to-vehicle (V2V) network, the method comprising:

collecting visual data from a camera;

detecting a lane within the visual data;

generating a lane classification for the lane based on the visual data;

assigning a confidence level to the lane classification;

generating a lane distance estimate from the visual data;

generating a lane model from the lane classification and the lane distance estimate;

scanning a predetermined area for remote V2V equipped vehicles within a predefined range of the host vehicle; and

transmitting the lane model and the confidence level to the V2V network immediately upon determining that the remote V2V equipped vehicle is within the predefined range of the host vehicle.

2. The method of claim 1 wherein the camera comprises a front camera mounted to a front-facing surface of the host vehicle.

3. The method of claim 1 wherein the detecting a plurality of lanes further comprises determining a position, a width, a curvature, a topography, a distance of each of the plurality of lanes relative to a reference position on the host vehicle, and a color and a shape of a plurality of lane markers for the plurality of lanes.

4. The method of claim 3 wherein the generating a lane classification further comprises comparing the color and the shape of the plurality of lane markers to a library of colors and shapes of known lane markers.

5. The method of claim 1 wherein the generating a lane distance estimate further comprises mathematically interpolating from the visual data the distance from a lane edge relative to a reference position on the host vehicle.

6. The method of claim 1 wherein the V2V network includes at least one remote V2V equipped vehicle.

7. The method of claim 1 wherein transmitting the lane model and confidence level further comprises periodically transmitting the lane model and confidence level over the V2V network.

18. The method of claim 1 further comprising:

determining which of the remote vehicles in the V2V network the lane model is relevant by analyzing the lane model with respect to locational information about each of the remote vehicles, wherein the locational information may include global positioning system (GPS) location information, heading information, or speed information pertaining to each of the remote vehicles, and

wherein transmitting the lane model and the confidence level to the V2V network immediately upon determining that the remote V2V equipped vehicle is within the predefined range of the host vehicle includes immediately transmitting the lane model and the confidence level to the relevant remote vehicles.

8. A method of generating and communicating lane information from a host to data vehicle-to-vehicle (V2V) network, the method comprising:

optically scanning a predefined area of road surface surrounding the host vehicle;

tracking a plurality of lanes;

detecting target V2V equipped vehicles;

encoding information about the plurality of lanes into a mathematical lane model;

determining for which of any target vehicles the mathematical lane model is relevant; and

communicating the mathematical model over the V2V network to the relevant target vehicles immediately as the relevant target vehicles are identified.

9. The method of claim 8 wherein the optically scanning further comprises collecting optical data from a plurality of cameras mounted to the host vehicle.

10. The method of claim 9 wherein the tracking a plurality of lanes further comprises determining a position, a width, a curvature, a topography, a distance of each of the plurality of lanes relative to a reference position on the host vehicle, and a color and a shape of a plurality of lane markers for the plurality of lanes.

11. The method of claim 10 wherein the tracking further comprises comparing the color and the shape of the plurality of lane markers to a library of colors and shapes of known lane markers.

12. The method of claim 8 wherein the detecting target V2V equipped vehicles comprises transmitting V2V data packets and receiving V2V data packets sent by remote V2V equipped vehicles over the V2V network.

13. The method of claim 12 wherein the communicating the mathematical lane model further comprises encoding the mathematical lane model to create an encoded mathematical lane model that conforms to a communications protocol and transmitting the encoded mathematical lane model over the V2V network.

19. The method of claim 8 wherein determining for which of any target vehicles the mathematical lane model is relevant includes analyzing the mathematical lane model with respect to locational information about each of the target vehicles, the locational information including global positioning system (GPS) location information, heading information, and speed information.

14. A system for generating and communicating lane information from a host vehicle to a vehicle-to-vehicle (V2V) network, the system comprising:

a camera;

a V2V sub-system having a receiver and a transmitter;

a controller in communication with the camera and the V2V sub-system, the controller having memory for storing control logic and a processor configured to execute the control logic, the control logic including a first control logic for collecting visual data from the camera, a second control logic for detecting a lane within the visual data, a third control logic for generating a lane classification for the lanes based on the visual data, a fourth control logic for assigning a base confidence level to the lane classification, a fifth control logic for generating a lane distance estimate from the visual data, a sixth control logic for generating a base lane model from the lane classification and the lane distance estimate, a seventh control logic for generating a formatted lane model and a formatted confidence level, an eighth control logic for determining for which of any target vehicles the formatted lane model is relevant by analyzing the formatted lane model with respect to locational information retrieved by the V2V sub-system about each of the target vehicles, the locational information including global positioning system (GPS) location information, heading information, and speed information, and a ninth control logic for immediately transmitting the formatted lane model and the confidence level to the relevant target vehicles in the V2V network.

15. The system of claim 14 wherein the camera comprises a plurality of cameras attached to the host vehicle.

16. The system of claim 15 wherein the base and formatted lane models comprise lane positioning, lane markings, lane curvature, speed, and trajectory data for the host vehicle.

17. The system of claim 16 wherein the seventh control logic further comprises aligning the base lane model and base confidence level to a standardized communications protocol.