DRIVING SKILL RECOGNITION BASED ON U-TURN PERFORMANCE

• US 20100209886A1
• Filed: 02/18/2009
• Published: 08/19/2010
• Est. Priority Date: 02/18/2009
• Status: Abandoned Application

• Current Assignee: GM Global Technology Operations LLC (General Motors Company)
• Original Assignee: GM Global Technology Operations Incorporated (Motors Liquidation Co. GUC Trust)
• Inventors: LIN, WILLIAM C., HUANG, JIHUA, CHIN, YUEN-KWOK
• Application Number:
  US12/388,311
• Publication Number:
  US 20100209886A1
• Time in Patent Office: Days
• US Class Current: 434/65
• CPC Class Codes: B60W 40/09 Driving style or behaviour
• CPC Class Codes: G09B 19/167 Control of land vehicles

Abstract

A system that classifies driver driving skill based on U-turn maneuvers. The system reads sensor signals for vehicle speed and vehicle yaw rate. The system determining that the vehicle has made a U-turn maneuver using the vehicle speed signal and the yaw-rate signal and then classifies the driver'"'"'s driving skill using selected discriminant features obtained or derived from the U-turn maneuver.

Claims

1. A method for determining a driver'"'"'s driving skill of a vehicle, said method comprising:

reading a vehicle speed signal and a vehicle yaw rate signal from vehicle sensors;

determining that the vehicle has started a turn if the yaw rate signal is greater than a first yaw rate threshold;

determining a vehicle heading angle based on the yaw rate signal and a sampling time if the vehicle has started a turn;

determining that the vehicle maneuver has been completed if the yaw rate signal is less than a second yaw rate threshold;

determining that the completed maneuver was a U-turn maneuver if the vehicle yaw rate signal is less than a third yaw rate threshold during the maneuver, the vehicle heading angle at the end of the maneuver is within a heading angle range and the time duration of the maneuver is less than a predetermined time threshold; and

classifying the driver'"'"'s driving skill using information obtained from the U-turn maneuver.

2. The method according to claim 1 wherein the heading angle range is between 165°

and 195°

.

3. The method according to claim 1 further comprising updating the heading angle if the vehicle is still in the maneuver because the yaw rate signal is not less than the second yaw rate threshold.

4. The method according to claim 1 wherein the first yaw rate threshold is greater than the second yaw rate threshold and less than the third yaw rate threshold.

5. The method according to claim 1 wherein classifying the driver'"'"'s driving skill includes using selected discriminant features obtained or derived from the U-turn maneuver.

6. The method according to claim 5 wherein the discriminant features are derived or obtained from the group comprising a maximum vehicle lateral acceleration, a maximum vehicle yaw rate, the vehicle speed at the beginning of the U-turn maneuver, the minimum vehicle speed during the U-turn maneuver, the vehicle speed at the end of the U-turn maneuver, the maximum braking force of the vehicle, an array of braking indexes based on the distribution of the braking force, a maximum vehicle longitudinal acceleration, a maximum throttle opening and an array of throttle indexes based on the distribution of the throttle opening.

7. The method according to claim 5 wherein obtaining the discriminant features includes using a process selected from the group comprising principal component analysis, linear discriminant analysis, kernel principal component analysis and generalize discriminant analysis.

8. The method according to claim 5 wherein obtaining the discriminant features includes using a linear discriminant analysis process.

9. The method according to claim 1 wherein classifying the driver'"'"'s driving skill includes using a support vector machine.

10. The method according to claim 1 wherein classifying the driver'"'"'s driving skill includes using a technique selected from the group comprising fuzzy logic, neural networks, a self-organizing map and threshold-based logic.

11. A method for determining a driver'"'"'s driving skill of a vehicle, said method comprising:

reading a vehicle speed signal and a vehicle yaw rate signal from vehicle sensors;

determining that the vehicle has made a U-turn maneuver using the vehicle speed signal and the yaw-rate signal; and

classifying the driver'"'"'s driving skill using selected discriminant features obtained or derived from the U-turn maneuver.

12. The method according to claim 11 wherein classifying the driver'"'"'s driving skill includes using selected discriminant features obtained or derived from the U-turn maneuver.

13. The method according to claim 12 wherein the discriminant features are derived or obtained from the group comprising a maximum vehicle lateral acceleration, a maximum vehicle yaw rate, the vehicle speed at the beginning of the U-turn maneuver, the minimum vehicle speed during the U-turn maneuver, the vehicle speed at the end of the U-turn maneuver, the maximum braking force of the vehicle, an array of braking indexes based on the distribution of the braking force, a maximum vehicle longitudinal acceleration, a maximum throttle opening and an array of throttle indexes based on the distribution of the throttle opening.

14. The method according to claim 12 wherein obtaining the discriminant features includes using a process selected from the group comprising principal component analysis, linear discriminant analysis, kernel principal component analysis and generalize discriminant analysis.

15. The method according to claim 12 wherein obtaining the discriminant features includes using a linear discriminant analysis process.

16. The method according to claim 11 wherein classifying the driver'"'"'s driving skill includes using a support vector machine.

17. The method according to claim 11 wherein classifying the driver'"'"'s driving skill includes using a technique selected from the group comprising fuzzy logic, neural networks, a self-organizing map and threshold-based logic.

18. A system for determining a driver'"'"'s driving skill of a vehicle, said system comprising:

a plurality of vehicle sensors providing a vehicle speed signal and a vehicle yaw rate signal;

means for determining that the vehicle has started a turn if the yaw rate signal is greater than a first yaw rate threshold;

means for determining a vehicle heading angle based on the yaw rate signal and a sampling time if the vehicle has started a turn;

means for determining that the vehicle maneuver has been completed if the yaw rate signal is less than a second yaw rate threshold;

means for determining that the completed maneuver was a U-turn maneuver if the vehicle yaw rate signal is less than a third yaw rate threshold during the maneuver, the vehicle heading angle at the end of the maneuver is within a heading angle range and the time duration of the maneuver is less than a predetermined time threshold; and

means for classifying the driver'"'"'s driving skill using discriminant features derived or obtained from the U-turn maneuver.

19. The system according to claim 18 wherein the discriminant features are derived or obtained from the group comprising a maximum vehicle lateral acceleration, a maximum vehicle yaw rate, the vehicle speed at the beginning of the U-turn maneuver, the minimum vehicle speed during the U-turn maneuver, the vehicle speed at the end of the U-turn maneuver, the maximum braking force of the vehicle, an array of braking indexes based on the distribution of the braking force, a maximum vehicle longitudinal acceleration, a maximum throttle opening and an array of throttle indexes based on the distribution of the throttle opening.

20. The system according to claim 18 wherein the means for classifying the driving skill includes using a technique selected from the group comprising fuzzy logic, neural networks, a self-organizing map and threshold-based logic.