VEHICLE STABILITY ENHANCEMENT CONTROL ADAPTATION TO DRIVING SKILL BASED ON MULTIPLE TYPES OF MANEUVERS

• US 20100209889A1
• 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:
  US12/388,382
• Publication Number:
  US 20100209889A1
• Time in Patent Office: Days
• US Class Current: 434/65
• CPC Class Codes: B60W 40/09 Driving style or behaviour
• CPC Class Codes: G07C 5/0841 Registering performance data recording measured values G01D; information storage G11B
• CPC Class Codes: G09B 19/167 Control of land vehicles

Abstract

A system and method that classify driver driving skill based on multiple types of maneuvers. An original features generation processor receives signals identifying driving characteristics of the driver of the vehicle and outputs original features from the signals. A features extraction processor extracts certain features from the original features and outputs transformed features. A features selection processor selects certain ones of the transformed features and outputs final features and a classifier classifies the final features to determine the skill level of the vehicle driver.

Claims

1. A skill level determination system for determining the skill level of a driver driving a vehicle, said system comprising:

an original features generation processor receives parameter signals identifying various vehicle parameters, said original features generation processor outputting original features from the parameter signals;

a features extraction processor extracts transformed features from the original features, said features extraction processor outputting the transformed features;

a features selection processor selects certain ones of the transformed features and outputs final features; and

a classifier classifies the final features to determine the skill level of the driver.

2. The system according to claim 1 wherein the original features provided by the original features generation processor include driver steering input, vehicle speed, vehicle yaw-rate, vehicle lateral acceleration, throttle opening and longitudinal acceleration.

3. The system according to claim 1 wherein the features extraction processor extracts the transformed features that have the capability for differentiating different skill patterns.

4. A skill level determination system for determining the skill level of a driver driving a vehicle, said system comprising:

a plurality of skill classification processors, each skill classification processor receiving the same vehicle parameter signals identifying certain vehicle parameters, each skill classification processor providing a different classification analysis of the vehicle signals, each skill classification processor outputting a skill classification signal of the driver skill level of the vehicle; and

a classification combiner responsive to the skill classification signals from the plurality of classification processors, said classification combiner combining the skill classification signals into a single skill level signal.

5. The system according to claim 4 wherein the skill characterization processors use classification processes selected from the group comprising fuzzy logic, clustering, neural networks, self-organizing maps and threshold-based logic.

6. The system according to claim 4 wherein the skill characterization processors use discriminant features to classify the driving skill.

7. The system according to claim 6 wherein the discriminant features are derived or obtained from the group comprising vehicle yaw rate, vehicle lateral acceleration, vehicle speed, brake pedal position, brake pedal force, throttle percentage, distance to a preceding vehicle and distance to a following vehicle and signals processed and derived from same.

8. The system according to claim 4 wherein the skill characterization processors employ a process of feature extraction and feature selection to classify the driving skill.

9. The system according to claim 4 wherein the vehicle parameter signals are provided by vehicle sensors including a hand-wheel angle sensor, a yaw rate sensor, a vehicle speed sensor, wheel speed sensors, longitudinal accelerometer, a lateral accelerometer, a headway distance sensor, a forward-looking radar/lidar camera, a throttle opening sensor and brake pedal position/force sensors.

10. The system according to claim 4 wherein the classification combiner provides decision fusion.

11. The system according to claim 10 wherein the decision fusion employs a decision fusion technique selected from the group consisting of Bayesian fusion and Dempster-Shafer fusion.

12. A driver skill recognition system for determining a driver'"'"'s skill level of a vehicle, said system comprising:

a plurality of skill classification processors each classifying the driver'"'"'s skill based on vehicle parameter signals during specific a type of maneuver;

a switch that receives signals identifying vehicle parameters and a maneuver type signal identifying different vehicle maneuvers, said switch selecting the skill classification processor corresponding to a maneuver type and outputting the maneuver type signal and the vehicle parameter signals to the selected skill classification processor for classification; and

a decision fusion processor receiving a skill level classification signal from the selected skill classification processor and combining it with skill classification signals based on previous vehicle maneuvers into a single skill level signal.

13. The system according to claim 12 wherein the decision fusion processor employs a decision fusion technique selected from the group consisting of Bayesian fusion and Dempster-Shafer fusion.

14. The system according to claim 12 wherein the vehicle maneuvers include vehicle following maneuvers, lane-change maneuvers, U-turn maneuvers, left/right-turn maneuvers, highway on/off ramp maneuvers, passing maneuvers, backup maneuvers and curve-handling maneuvers.

15. The system according to claim 12 wherein the skill characterization processors use classification processes selected from the group comprising fuzzy logic, clustering, neural networks, self-organizing maps and threshold-based logic.

16. The system according to claim 12 wherein the skill characterization processors use discriminant features to classify the driving skill.

17. The system according to claim 16 wherein the discriminant features are derived or obtained from the group comprising vehicle yaw rate, vehicle lateral acceleration, vehicle speed, brake pedal position, brake pedal force, throttle percentage, distance to a preceding vehicle and distance to a following vehicle and signals processed and derived from same.

18. The system according to claim 12 wherein the skill characterization processors employ a process of feature extraction and feature selection to classify the driving skill.

19. The system according to claim 12 wherein the vehicle parameter signals are provided by vehicle sensors including a hand-wheel angle sensor, a yaw rate sensor, a vehicle speed sensor, wheel speed sensors, longitudinal accelerometer, a lateral accelerometer, a headway distance sensor, a forward-looking radar/lidar camera, a throttle opening sensor and brake pedal position/force sensors.