Abstract: A lane departure warning method and system thereof are provided. First a driver model is established, wherein the driver model is established according to a lateral position error of a vehicle being driven and a steering angle generated by a driver for controlling the vehicle. Next, a system identification process is performed on the lateral position error and the steering angle, so as to obtain specific information of the driver model. An estimation process is performed on the specific information and a plurality of raw statistical data, so as to obtain a time to lane crossing (TLC). A decision process is performed on the TLC and a driver drowsiness index, so as to obtain a lane departure warning strategy.

Abstract: A lane departure warning method and system thereof are provided. First a driver model is established, wherein the driver model is established according to a lateral position error of a vehicle being driven and a steering angle generated by a driver for controlling the vehicle. Next, a system identification process is performed on the lateral position error and the steering angle, so as to obtain specific information of the driver model. An estimation process is performed on the specific information and a plurality of raw statistical data, so as to obtain a time to lane crossing (TLC). A decision process is performed on the TLC and a driver drowsiness index, so as to obtain a lane departure warning strategy.

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.