METHODS AND SYSTEMS FOR DETERMINING LANE HEALTH FROM AN AUTONOMOUS VEHICLE

• US 20180022348A1
• Filed: 09/15/2017
• Published: 01/25/2018
• Est. Priority Date: 09/15/2017
• Status: Abandoned Application

• Current Assignee: GM Global Technology Operations LLC (General Motors Company)
• Original Assignee: GM Global Technology Operations LLC (General Motors Company)
• Inventors: Chu, Kevin, Ghose, Shiva
• Application Number:
  US15/706,197
• Publication Number:
  US 20180022348A1
• Time in Patent Office: 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 2420/54 Audio sensitive means, e.g. ultrasound
• CPC Class Codes: B60W 2552/00 Input parameters relating to infrastructure
• CPC Class Codes: B60W 2552/05 Type of road, e.g. motorways, local streets, paved or unpaved roads
• CPC Class Codes: B60W 2552/35 Road bumpiness, e.g. potholes
• CPC Class Codes: B60W 2552/53 Road markings, e.g. lane marker or crosswalk
• CPC Class Codes: B60W 2556/35 Data fusion
• CPC Class Codes: B60W 2556/50 of positioning data, e.g. GPS [Global Positioning System] data
• CPC Class Codes: B60W 40/06 Road conditions
• CPC Class Codes: B60W 60/00253 Taxi operations
• CPC Class Codes: G01C 21/28 with correlation of data from several navigational instruments
• CPC Class Codes: G01C 21/3415 Dynamic re-routing, e.g. recalculating the route when the user deviates from calculated route or after detecting real-time traffic data or accidents
• CPC Class Codes: G05D 1/0274 using mapping information stored in a memory device navigation using map-matching G01C21/30
• CPC Class Codes: G06F 18/24143 Distances to neighbourhood prototypes, e.g. restricted Coulomb energy networks [RCEN]
• 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 1/0112 from the vehicle, e.g. floating car data [FCD]
• CPC Class Codes: G08G 1/096725 where the received information generates an automatic action on the vehicle control
• CPC Class Codes: G08G 1/096741 where the source of the transmitted information selects which information to transmit to each vehicle
• CPC Class Codes: G08G 1/096791 where the origin of the information is another vehicle
• CPC Class Codes: G08G 1/161 : Decentralised systems, e.g. inter-vehicle communication
• CPC Class Codes: G08G 1/165 : for passive traffic, e.g. including static obstacles, trees
• CPC Class Codes: G08G 1/167 : Driving aids for lane monitoring, lane changing, e.g. blind spot detection

Abstract

Systems and method are provided for controlling an autonomous vehicle. In one embodiment, a method includes: receiving, by a processor, sensor data associated with an environment of a first autonomous vehicle; determining, by a processor, lane health information based on the sensor data; and selectively controlling, by a processor, a second autonomous vehicle based on the lane health information.

Claims

1. A method of controlling an autonomous vehicle, comprising:

receiving, by a processor, sensor data associated with an environment of a first autonomous vehicle;

determining, by a processor, lane health information based on the sensor data; and

selectively controlling, by a processor, a second autonomous vehicle based on the lane health information.

2. The method of claim 1, wherein the sensor data is obtained by at least one of a lidar, a radar, and a camera that senses an environment of the autonomous vehicle.

3. The method of claim 1, further comprising processing the sensor data to determine map data associated with the environment, and wherein the determining the lane health is based on the map data associated with the environment.

4. The method of claim 1, further comprising processing the sensor data to determine observation data associated with the vehicle, wherein the observation data includes at least one of vehicle data, road data, and activity data.

5. The method of claim 1, further comprising processing the sensor data to determine object data associated with the environment, and wherein the determining the lane health is based on the object data associated with the environment.

6. The method of claim 5, wherein the object data includes at least one of object speed data, object acceleration data, and object classification data.

7. The method of claim 1, further comprising processing the sensor data to determine vehicle location data in relation to the environment, and wherein the determining the lane health is based on the vehicle location data in relation to the environment.

8. The method of claim 1, further comprising processing the lane health information from the first vehicle with lane health information from at least one other vehicle to determine an overall lane health, and wherein the selectively controlling is based on the overall lane health.

9. The method of claim 1, wherein the lane health information indicates the health of a lane of a road.

10. The method of claim 1, further comprising at least one of determining if multiple riders can ride together, determining when to dispatch an autonomous vehicle, determining which autonomous vehicle to dispatch from a plurality of autonomous vehicles, and determining a route including which lane of the route for the autonomous vehicle, and wherein the selectively controlling is based on the determining.

11. A system for controlling an autonomous vehicle, comprising:

a first non-transitory module that, by a processor, receives sensor data associated with an environment of a first autonomous vehicle;

a second non-transitory module that, by a processor, determines lane health information based on the sensor data; and

a third non-transitory module that, by a processor, selectively controls a second autonomous vehicle based on the lane health information.

12. The system of claim 11, wherein the sensor data is obtained by at least one of a lidar, a radar, and a camera that senses an environment of the autonomous vehicle.

13. The system of claim 11, further comprising a fourth non-transitory module that, by a processor, processes the sensor data to determine map data associated with the environment, and wherein the second non-transitory module determines the lane health based on the map data associated with the environment.

14. The system of claim 11, further comprising a fourth non-transitory module that, by a processor, processes the sensor data to determine observation data associated with the vehicle, wherein the observation data includes at least one of vehicle data, road data, and activity data.

15. The system of claim 11, further comprising a fourth non-transitory module that, by a processor, processes the sensor data to determine object data associated with the environment, and wherein the second non-transitory module determines the lane health based on the object data associated with the environment.

16. The system of claim 15, wherein the object data includes at least one of object speed data, object acceleration data, and object classification data.

17. The system of claim 11, further comprising a fourth non-transitory module that, by a processor, processes the sensor data to determine vehicle location data in relation to the environment, and wherein the third non-transitory module determines the lane health based on the vehicle location data in relation to the environment.

18. The system of claim 11, further comprising a fourth module that, by a processor, processes the lane health information from the first vehicle with lane health information from at least one other vehicle to determine an overall lane health, and wherein the third non-transitory module selectively controls based on the overall lane health.

19. The system of claim 11, further comprising a fourth module that, by a processor, determines at least one of if multiple riders can ride together, determining when to dispatch an autonomous vehicle, which autonomous vehicle to dispatch from a plurality of autonomous vehicles, and a route including which lane of the route for the autonomous vehicle, and wherein the third non-transitory module selectively controls based on the determining.

20. An autonomous vehicle, comprising:

at least one of a camera, a lidar, and a radar that senses an environment of the autonomous vehicle and that generates sensor data; and

a controller that, by a processor, receives the sensor data, determines lane health information based on the sensor data, and communicates the lane health information to a remote location for selectively controlling a second autonomous vehicle based on the lane health information.