SYSTEMS AND METHODS OF VEHICULAR PATH PREDICTION FOR COOPERATIVE DRIVING APPLICATIONS THROUGH DIGITAL MAP AND DYNAMIC VEHICLE MODEL FUSION

• US 20110046843A1
• Filed: 08/24/2009
• Published: 02/24/2011
• Est. Priority Date: 08/24/2009
• Status: Active Grant

• Current Assignee: Toyota Motor Corporation
• Original Assignee: Toyota Motor Engineering & Manufacturing North America Incorporated (Toyota Motor Corporation)
• Inventors: Caveney, Derek Stanley
• Granted Patent: US 8,315,756 B2
• Time in Patent Office: Days
• US Class Current: 701/33
• CPC Class Codes: G08G 1/161 Decentralised systems, e.g. inter-vehicle communication
• CPC Class Codes: G08G 1/167 Driving aids for lane monitoring, lane changing, e.g. blind spot detection

Abstract

Method and system of vehicular path prediction for a vehicle travelling on a road. A yaw rate of the vehicle is estimated over a prediction time period based on vehicle sensor information and map information for the road. Then, a further path of the vehicle on the road is predicted for the prediction time period based on a speed and a direction of the vehicle, and the estimated yaw rate. Map information includes a geometry for a portion of the road on which the vehicle is travelling, and the vehicle sensor information includes yaw rate information from a yaw rate sensor on the vehicle, and location information of the vehicle relative to the map information from a positioning device on the vehicle. A vehicle provided for path prediction includes a communication system for transmitting the predicted path to other vehicles for collision avoidance.

Claims

1. A method of vehicular path prediction for a vehicle travelling on a road, comprising:

estimating a yaw rate of the vehicle over a prediction time period based on vehicle sensor information and map information for the road; and

predicting a future path of the vehicle on the road for the prediction time period based on a speed and a direction of the vehicle, and the estimated yaw rate.

2. The method according to claim 1, wherein the map information includes a geometry for a portion of the road on which the vehicle is travelling.

3. The method according to claim 1, wherein the vehicle sensor information includes yaw rate information from a yaw rate sensor on the vehicle and location information of the vehicle relative to the map information from a positioning device on the vehicle.

4. The method according to claim 1, wherein

the predicted future path of the vehicle is denoted as a vector x(t),

5. The method according to claim 4, wherein

a_x(t) is assumed to be constant over the prediction time period, denoted as T, with a value a_x (t)=a_x[0]∀

t ε

[0, T] taken from an accelerometer measurement.

6. The method according to claim 5, wherein R(t) is an instantaneous radius of curvature of the vehicle, and ω

(t)=ν

_x(t)/R(t).

7. The method according to claim 6, wherein the instantaneous radius of curvature is the inverse of a combined curvature, which includes a road curvature based on the map information for the road and a maneuvering curvature based on a vehicle maneuver determined based on the vehicle sensor information.

8. The method according to claim 7, wherein the maneuvering curvature is based on a maneuvering time period for completing the vehicle maneuver.

9. The method according to claim 1, further comprising:

transmitting the predicted path of the vehicle to another vehicle.

10. A vehicle, comprising:

a yaw rate sensor to produce yaw rate information of the vehicle;

a positioning device to determine a global position of the vehicle relative to map information for a road; and

a processing device to estimate a yaw rate of the vehicle over a prediction time period based on vehicle sensor information including the produced yaw rate information from the yaw rate sensor and the map information for the road, and further to predict a future path of the vehicle on the road for the prediction time period based on a speed and a direction of the vehicle, and the estimated yaw rate.

11. The vehicle according to claim 10, wherein the map information includes a geometry for a portion of the road on which the vehicle is travelling.

12. The vehicle according to claim 10, wherein

the predicted future path of the vehicle is denoted as a vector x(t),

13. The vehicle according to claim 12, further comprising an accelerometer, wherein

a_x(t) is assumed to be constant over the prediction time period, denoted as T, with a value a_x (t)=a_x[0]∀

t ε

[0, T] taken from a measurement using the accelerometer.

14. The vehicle according to claim 13, wherein R(t) is an instantaneous radius of curvature of the vehicle, and ψ

(t)=ν

_x(t)/R(t).

15. The vehicle according to claim 14, wherein the instantaneous radius of curvature is the inverse of a combined curvature determined by the processing device, the processing device determining the combined curvature using a road curvature based on the map information for the road and a maneuvering curvature based on a vehicle maneuver determined based on the vehicle sensor information.

16. The vehicle according to claim 15, wherein the maneuvering curvature is based on a maneuvering time period for completing the vehicle maneuver.

17. The vehicle according to claim 10, further comprising a communication device to transmit the predicted path of the vehicle to another vehicle.

18. A computer readable medium, including computer executable instructions, wherein the instructions, when executed by a processor, cause the processor to perform a method of vehicular path prediction for a vehicle travelling on a road, the method comprising:

estimating a yaw rate of the vehicle over a prediction time period based on vehicle sensor information and map information for the road; and

predicting a future path of the vehicle on the road for the prediction time period based on a speed and a direction of the vehicle, and the estimated yaw rate.

19. The computer readable medium according to claim 18, wherein the map information includes a geometry for a portion of the road on which the vehicle is travelling.

20. The computer readable medium according to claim 18, wherein the vehicle sensor information includes yaw rate information from a yaw rate sensor on the vehicle, and location information of the vehicle relative to the map information from a positioning device on the vehicle.