PREDICTION OF DRIVER INTENT AT INTERSECTION

• US 20170190334A1
• Filed: 01/06/2016
• Published: 07/06/2017
• Est. Priority Date: 01/06/2016
• Status: Active Grant

• Current Assignee: GM Global Technology Operations LLC (General Motors Company)
• Original Assignee: GM Global Technology Operations LLC (General Motors Company)
• Inventors: Zelman, Ido, Mudalige, Upali P., Elvitigala, Thanura Ranmal
• Granted Patent: US 10,486,707 B2
• Time in Patent Office: Days
• CPC Class Codes: B60W 2050/0029 Mathematical model of the driver
• CPC Class Codes: B60W 2050/0075 Automatic parameter input, automatic initialising or calibrating means
• 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 2520/06 Direction of travel
• CPC Class Codes: B60W 2520/10 Longitudinal speed
• CPC Class Codes: B60W 2520/105 Longitudinal acceleration
• CPC Class Codes: B60W 2520/14 Yaw
• CPC Class Codes: B60W 2540/18 Steering angle
• CPC Class Codes: B60W 2552/30 Road curve radius
• CPC Class Codes: B60W 2554/80 Spatial relation or speed relative to objects
• CPC Class Codes: B60W 2555/60 Traffic rules, e.g. speed limits or right of way
• CPC Class Codes: B60W 2556/10 Historical data
• CPC Class Codes: B60W 2556/50 of positioning data, e.g. GPS [Global Positioning System] data
• CPC Class Codes: B60W 2900/00 Indexing codes relating to the purpose of, or problem solved of road vehicle drive control systems not otherwise provided for in groups B60W30/00
• CPC Class Codes: B60W 30/18145 Cornering
• CPC Class Codes: B60W 30/18154 Approaching an intersection
• CPC Class Codes: B60W 40/09 Driving style or behaviour
• CPC Class Codes: B60W 50/0097 Predicting future conditions
• CPC Class Codes: G08G 1/096725 where the received information generates an automatic action on the vehicle control
• CPC Class Codes: G08G 1/096758 : where no selection takes place on the transmitted or the received information
• CPC Class Codes: G08G 1/096775 : where the origin of the information is a central station
• CPC Class Codes: G08G 1/096783 : where the origin of the information is a roadside individual element
• CPC Class Codes: G08G 1/096791 : where the origin of the information is another vehicle
• CPC Class Codes: G08G 1/163 : involving continuous checking
• CPC Class Codes: G08G 1/166 : for active traffic, e.g. moving vehicles, pedestrians, bikes

Abstract

A system and method for predicting whether a driver of a host vehicle or a remote vehicle intends to make a left or right turn or travel straight through an intersection before the host vehicle or remote vehicle reaches the intersection that relies on a probability model that employs a dynamic Bayesian network. The method includes obtaining a plurality of environmental cues that identify external parameters at or around the intersection, where the environmental cues include position and velocity of the remote vehicle, and obtaining a plurality of host vehicle cues that define operation of the host vehicle. The method then predicts the turning intent of the host vehicle and/or remote vehicle at the intersection using the model based on both the external cues and the vehicle cues using the model. The model can use learned information about previous driver turns at the intersection.

Claims

1. A method for predicting turning intent of a host vehicle when approaching an intersection, said method comprising:

obtaining a plurality of environmental cues that identify external parameters at or around the intersection, said environmental cues including position and velocity of remote vehicles;

obtaining a plurality of host vehicle cues that define operation of the host vehicle; and

predicting the turning intent of the host vehicle at the intersection before the host vehicle reaches the intersection based on both the environmental cues and the vehicle cues.

2. The method according to claim 1 wherein predicting the turning intent of the host vehicle includes using a probability model.

3. The method according to claim 2 wherein predicting the turning intent of the host vehicle includes using a dynamic Bayesian network probability analysis.

4. The method according to claim 1 wherein predicting the turning intent of the host vehicle includes predicting the probability that the host vehicle will turn right, the probability that the host vehicle will turn left, and the probability that the host vehicle will travel straight through the intersection.

5. The method according to claim 1 further comprising predicting a turning intent of one or more remote vehicles at the intersection.

6. The method according to claim 5 wherein predicting the turning intent of remote vehicles includes using the environment cues.

7. The method according to claim 1 further comprising predicting traffic flow in the intersection and providing a probability that the host vehicle will proceed, give right-of-way or stop based on the turning intent of the host vehicle.

8. The method according to claim 7 wherein predicting traffic flow includes processing signals concerning objects surrounding the host vehicle, traffic signs, traffic lights and a map database.

9. The method according to claim 1 further comprising providing previously learned turning information from when the host vehicle previously passed through the intersection that is obtained by capturing, recording and processing signals related to host vehicle turning maneuvers and remote vehicle turning maneuvers including extracting values for parameters being used to predict the turning intent so as to allow better prediction ability and personalization to a specific driver and to a specific intersection, wherein predicting the turning intent of the host vehicle includes using the previously learned turning information of the host vehicle at the intersection.

10. The method according to claim 1 wherein obtaining environmental cues include obtaining a distance to the intersection.

11. The method according to claim 1 wherein obtaining host vehicle cues includes obtaining one or more of turn signal activity, host vehicle velocity, host vehicle acceleration, host vehicle yaw rate, host vehicle heading and host vehicle steering/road wheel angle.

12. The method according to claim 1 wherein obtaining environmental cues includes obtaining one or more curvature of a preceding road segment, traffic signs, traffic lights and map branching.

13. The method according to claim 1 wherein obtaining a plurality of environmental cues and a plurality of host vehicle cues includes using information from one or more of radar sensors, cameras, map database, lidar sensors, V2X communications, roadside information units, and a controller area network (CAN) bus.

14. The method according to claim 1 further comprising using the prediction of the turning intent of the host vehicle in a collision avoidance system.

15. A method for predicting turning intent of a host vehicle or a remote vehicle at or near an intersection, said method comprising:

obtaining a plurality of environmental cues that identify external parameters at or around the intersection, said environmental cues including position and velocity of the remote vehicle;

obtaining a plurality of host vehicle cues that define operation of the host vehicle;

obtaining information of previous turning maneuvers of the host vehicle at the intersection; and

predicting the turning intent of the host vehicle or the remote vehicle at the intersection using a probability model including a dynamic Bayesian network that uses the environmental cues, the vehicle cues and the previous turning maneuver information.

16. The method according to claim 15 wherein obtaining host vehicle cues includes obtaining one or more of turn signal activity, host vehicle velocity, host vehicle acceleration, host vehicle yaw rate, host vehicle heading and host vehicle steering/road wheel angle.

17. The method according to claim 15 wherein obtaining environmental cues includes obtaining one or more of curvature of a preceding road segment, traffic signs, traffic lights and map branching.

18. The method according to claim 15 further comprising predicting traffic flow in the intersection and providing a probability that the host vehicle will proceed, give right-of-way or stop based on the turning intent of the host vehicle.

19. The method according to claim 15 wherein obtaining information of previous turning maneuvers includes providing previously learned turning information from when the host vehicle previously passed through the intersection that is obtained by capturing, recording and processing signals related to host vehicle turning maneuvers and remote vehicle turning maneuvers including extracting values for parameters being used to predict the turning intent so as to allow better prediction ability and personalization to a specific driver and to a specific intersection.

20. A method for predicting turning intent of a host vehicle or a remote vehicle at or near an intersection, said method comprising:

obtaining a plurality of environmental cues that identify external parameters at or around the intersection, said environmental cues including position and velocity of the remote vehicle, wherein obtaining environmental cues includes obtaining one or more curvature of a preceding road segment, traffic signs, traffic lights and map branching;

obtaining a plurality of host vehicle cues that define operation of the host vehicle, wherein obtaining host vehicle cues includes obtaining one or more of turn signal activity, host vehicle velocity, host vehicle acceleration, host vehicle yaw rate, host vehicle heading and host vehicle steering/road wheel angle, and wherein obtaining a plurality of environmental cues and a plurality of host vehicle cues includes using information from one or more of radar sensors, cameras, map database, lidar sensors, V2X communications, roadside information units, and a controller area network (CAN) bus;

obtaining information of previous turning maneuvers of the host vehicle at the intersection including providing previously learned turning information from when the host vehicle previously passed through the intersection that is obtained by capturing, recording and processing signals related to host vehicle turning maneuvers and remote vehicle turning maneuvers including extracting values for parameters being used to predict the turning intent so as to allow better prediction ability and personalization to a specific driver and to a specific intersection; and

predicting the turning intent of the host vehicle or the remote vehicle at the intersection using a probability model including a dynamic Bayesian network that uses the environmental cues, the vehicle cues and the previous turning maneuver information, wherein predicting the turning intent of the host vehicle includes predicting the probability that the host vehicle will turn right, the probability that the host vehicle will turn left, and the probability that the host vehicle will travel straight through the intersection.