Design of driving fatigue detection system based on hybrid measures using wavelet-packets transform

With the rapid development of urbanization and motorization in China, fatigue driving has become an increasingly serious road traffic problem. Driving fatigue affects drivers' alertness, decreasing an individual's ability to operate a vehicle safely and increasing the risk of human error that could lead to fatalities, which have been widely recognized as critical safety issues that cut across all modes in the transportation industry. In this paper, firstly, with a virtual driving system we developed, driving simulation experiments were designed to collect subjects' electroencephalogram (EEG) signals and mental fatigue data. To detect drivers' mental state in real time, wavelet-packets transform (WPT) was selected to extract continuous features; then, the subjective evaluation combined with video monitoring was used to evaluate driver's mental state in experiment accurately. At last, with fatigue feature as the input and fatigue state as the output, driving fatigue detection model can be constructed by classification methods. In this paper, Support Vector Machine (SVM) was used to build driving fatigue detection model to estimate mental fatigue state of EEG signal features, and the binary classification accuracy can be achieved up to 88.6207%.