Systems and methods of vehicular path prediction for cooperative driving applications through digital map and dynamic vehicle model fusion

• US 8,315,756 B2
• Filed: 08/24/2009
• Issued: 11/20/2012
• 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
• Primary Examiner(s): Beaulieu, Yonel
• Application Number:
  US12/546,434
• Publication Number:
  US 20110046843A1
• Time in Patent Office: 1,184 Days
• Field of Search: 701 23- 25, 701/400, 701408-409, 701/532, 701/301
• US Class Current: 701/23
• 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, by predicting the yaw rate for a time horizon corresponding to the 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

α

_x(t) is assumed to be constant over the prediction time period, denoted as T, with a value α

_x (t)=α

_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, by predicting the yaw rate for a time horizon corresponding to the 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

α

_x(t) is assumed to be constant over the prediction time period, denoted as T, with a value α

_x (t)=α

_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 ω

(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, by predicting the yaw rate for a time horizon corresponding to the 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.