ONLINE MONITORING METHOD OF DRIVER STATE AND SYSTEM USING THE SAME

• US 20090033501A1
• Filed: 07/24/2008
• Published: 02/05/2009
• Est. Priority Date: 07/31/2007
• Status: Abandoned Application

• Current Assignee: National Taiwan University of Science and Technology
• Original Assignee: National Taiwan University of Science and Technology
• Inventors: Peng, Meng-Hsuan, Chen, Liang-Kuang
• Application Number:
  US12/179,528
• Publication Number:
  US 20090033501A1
• Time in Patent Office: Days
• US Class Current: 340/576
• CPC Class Codes: A61B 5/18 for vehicle drivers or machine operators
• CPC Class Codes: A61B 5/7267 involving training the classification device
• CPC Class Codes: B60W 2540/18 Steering angle
• CPC Class Codes: G08B 21/06 indicating a condition of sleep, e.g. anti-dozing alarms

Abstract

An online monitoring method of driver state and a system using the same are provided. First a driver model is established, wherein the driver model generates a steering angle to control transportation means for riding according to the lateral position error of the riding transportation means. Next, a system identification processing is performed on the lateral position error and the steering angle of the riding transportation means to obtain a transfer function of the driver model. After that, an analysis processing is performed on the driver model transfer function to extract specific information therefrom, following by performing an assessment processing on the specific information and multiple statistics of raw data to generate the driver state assessment.

Claims

1. An online monitoring method of driver state, comprising:

establishing a driver model, wherein the driver model generates a steering angle to control a transportation means for riding according to a lateral position error of the riding transportation means;

performing a system identification processing on the lateral position error and the steering angle to obtain a transfer function of the driver model;

performing an analysis processing on the transfer function to extract a specific information therefrom; and

performing an assessment processing on the specific information and multiple statistics of raw data to generate a driver state assessment.

2. The online monitoring method of driver state according to claim 1, wherein the step of establishing the driver model comprises:

multiplying the lateral position error by a first polynomial with a forward shift operator to get a first product, multiplying a residual value of the driver model by a second polynomial with the forward shift operator to get a second product and adding the first and second products to obtain an equation; and

dividing the equation by a third polynomial with the forward shift operator to obtain the steering angle.

3. The online monitoring method of driver state according to claim 1, wherein the driver model is an auto-regression moving average with exogenous inputs model (ARMAX model).

4. The online monitoring method of driver state according to claim 1, wherein the lateral position error is the lateral difference between a real path of the riding transportation means and a predetermined path.

5. The online monitoring method of driver state according to claim 1, wherein the system identification processing comprises:

adopting an extended recursive least square algorithm (ERLS algorithm) to calculate a parameter vector of the driver model; and

obtaining the transfer function of the driver model according to the parameter vector.

6. The online monitoring method of driver state according to claim 1, wherein the step of analysis processing comprises:

calculating any one of a phase lead, a maximal phase lead, a DC gain, a crossover frequency and a main frequency of the steering angle of the transfer function and taking the calculation result as the specific information.

7. The online monitoring method of driver state according to claim 1, wherein the statistics of raw data comprise any one of a residual value of the driver model, the lateral position error, the steering angle, a yaw angle and a roll angle.

8. The online monitoring method of driver state according to claim 1, wherein the step of assessment processing comprises:

performing a probability neural network (PNN) processing on the specific information and the statistics of raw data to obtain a probability index corresponding to the driver state; and

generating the driver state assessment according to the possibility index.

9. An online monitoring system of driver state, comprising:

a system identification module for establishing a driver model and performing a system identification processing on a lateral position error and a steering angle of a riding transportation means to obtain a transfer function of the driver model;

an analyzing module, coupled to the system identification module for performing a analysis processing on the transfer function and extracting a specific information therefrom; and

an assessment module, coupled to the analyzing module for performing an assessment processing on the specific information and multiple statistics of raw data to generate a driver state assessment.

10. The online monitoring system of driver state according to claim 9, wherein the driver model comprises:

a first transfer unit for multiplying the lateral position error by a first polynomial with a forward shift operator;

a second transfer unit for multiplying a residual value of the driver model by a second polynomial with the forward shift operator; and

a third transfer unit for dividing the summation of two calculation results of the first transfer unit and the second transfer unit by a third polynomial with the forward shift operator to obtain the steering angle.

11. The online monitoring system of driver state according to claim 9, wherein the driver model is an auto-regression moving average with exogenous inputs model (ARMAX model).

12. The online monitoring system of driver state according to claim 9, wherein the lateral position error is a lateral difference between a real path of the riding transportation means and a predetermined path.

13. The online monitoring system of driver state according to claim 9, wherein the system identification processing is to adopt an extended recursive least square algorithm (ERLS algorithm) to calculate a parameter vector of the driver model and thereby to obtain the transfer function of the driver model.

14. The online monitoring system of driver state according to claim 9, wherein the analysis processing is employed for calculating any one of a phase lead, a maximal phase lead, a DC gain, a crossover frequency and a main frequency of the steering angle of the transfer function and taking the calculation result as the specific information.

15. The online monitoring system of driver state according to claim 9, wherein the statistics of raw data comprise any one of a residual value of the driver model, the lateral position error, the steering angle, a yaw angle and a roll angle.

16. The online monitoring system of driver state according to claim 9, wherein the assessment processing is employed for performing a probability neural network (PNN) processing on the specific information and the statistics of raw data to obtain a probability index corresponding to the driver state and generating the driver state assessment according to the possibility index.