VEHICLE STABILITY ENHANCEMENT CONTROL ADAPTATION TO DRIVING SKILL BASED ON PASSING MANEUVER

• US 20100209883A1
• 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: Huang, Jihua, Chin, Yuen-Kwok, Lin, William C.
• Application Number:
  US12/388,255
• Publication Number:
  US 20100209883A1
• Time in Patent Office: Days
• US Class Current: 434/65
• CPC Class Codes: G09B 19/167 Control of land vehicles

Abstract

A system and method that classify a driver'"'"'s driving skill based on a characteristic passing maneuver. The system reads vehicle speed and vehicle yaw rate signals and the maneuver identification processor determines whether the vehicle has made a characteristic passing maneuver. The skill characterization processor then uses the sensor information and the passing information to characterize the driver'"'"'s driving skill based on the passing 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 whether the vehicle has started a lane change from a first lane to a second lane as a first phase of the passing maneuver;

determining whether the lane change to the second lane has been completed as a second phase of the passing maneuver;

determining whether there has been a lane change back to the first lane to complete the passing maneuver if the passing maneuver is in the second phase as a third phase of the passing maneuver; and

classifying the driver'"'"'s driving skill using information obtained from the passing maneuver.

2. The method according to claim 1 further comprising determining whether a lane change has been aborted when the passing maneuver is in the first phase and the third phase.

3. The method according to claim 1 further comprising determining whether a time threshold has been exceeded after the passing maneuver is in the second phase, but no lane change back to the first lane has occurred.

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

5. The method according to claim 4 wherein the discriminant features are derived or obtained from the group comprising a maximum vehicle yaw rate, a maximum vehicle lateral acceleration, a maximum lateral jerk, a distance for lane change, an average vehicle speed, a maximum speed variation, a maximum braking pedal force, a maximum throttle percentage, a minimum distance to a preceding vehicle, a maximum range rate to the preceding vehicle and a minimum distance to a following vehicle for the first phase of the passing maneuver.

6. The method according to claim 5 wherein the discriminant features are derive or obtained from the group comprising a maximum throttle percentage, an average throttle percentage, a distance traveled and maximum speed variation for the second phase of the passing maneuver.

7. The method according to claim 4 wherein a discriminant feature extraction method is selected from the group comprising principal component analysis, linear discriminant analysis, colonial principal component analysis and generalized discriminant analysis, and a discriminant feature selection method is obtained from the group comprising exhaustive search, branch-and-bound search, sequential forward/backward selection and sequential forward/backward floating search.

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

9. The method according to claim 1 wherein classifying the driver'"'"'s driving skill includes using a technique selected from a group comprising fuzzy logic, clustering, neural networks, support vector machines and threshold-based logic.

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

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

determining whether the vehicle has made a passing maneuver; and

classifying the driver'"'"'s driving skill based on information obtained from the passing maneuver, wherein classifying the driver'"'"'s driving skill includes using selected discriminant features derived or obtained from the passing maneuver.

11. The method according to claim 10 wherein the discriminant features are derived or obtained from the group comprising a maximum vehicle yaw rate, a maximum vehicle lateral acceleration, a maximum lateral jerk, a distance for lane change, an average vehicle speed, a maximum speed variation, a maximum braking pedal force, a maximum throttle percentage, a minimum distance to a preceding vehicle, a maximum range rate to the preceding vehicle and a minimum distance to a following vehicle for the first phase of the passing maneuver.

12. The method according to claim 11 wherein the discriminant features are derived or obtained from the group comprising a maximum throttle percentage, an average throttle percentage, a distance traveled and maximum speed variation for the second phase of the passing maneuver.

13. The method according to claim 10 wherein a discriminant feature extraction method is selected from the group comprising principal component analysis, linear discriminant analysis, colonial principal component analysis and generalized discriminant analysis, and a discriminant feature selection method is obtained from the group comprising exhaustive search, branch-and-bound search, sequential forward/backward selection and sequential forward/backward floating search.

14. The method according to claim 10 wherein classifying the driver'"'"'s driving skill includes using support vector machines.

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

vehicle sensors that provide a vehicle speed signal and a vehicle yaw rate signal;

means for determining whether the vehicle has started a lane change from a first lane to a second lane as a first phase of the passing maneuver;

means for determining whether the lane change to the second lane has been completed as a second phase of the passing maneuver;

means for determining whether there has been a lane change back to the first lane to complete the passing maneuver if the passing maneuver is in the second phase as a third phase of the passing maneuver; and

means for classifying the drivers driving skill using information obtained from the passing maneuver, wherein the means for classifying the driver'"'"'s driving skill includes means for extracting and selecting discriminant features from the passing maneuver.

16. The system according to claim 15 further comprising means for determining whether a lane change has been aborted when the passing maneuver is in the first phase and the third phase.

17. The system according to claim 15 wherein the discriminant features are derived or obtained from the group comprising a maximum vehicle yaw rate, a maximum vehicle lateral acceleration, a maximum lateral jerk, a distance for lane change, an average vehicle speed, a maximum speed variation, a maximum braking pedal force, a maximum throttle percentage, a minimum distance to a preceding vehicle, a maximum range rate to the preceding vehicle and a minimum distance to a following vehicle for the first phase of the passing maneuver.

18. The system according to claim 17 wherein the discriminant features are selected from the group comprising a maximum throttle percentage, an average throttle percentage, a distance traveled and a maximum speed variation for the second phase of the passing maneuver.

19. The system according to claim 17 wherein a discriminant feature extraction method is selected from the group comprising principal component analysis, linear discriminant analysis, colonial principal component analysis and generalized discriminant analysis, and a discriminant feature selection method is obtained from the group comprising exhaustive search, branch-and-bound search, sequential forward/backward selection and sequential forward/backward floating search.

20. The system according to claim 15 wherein the means for classifying the driver'"'"'s driving skill uses support vector machines.