Drunk driving is a serious threat to traffic safety. But there are many serious deficiencies for current identification methods, which are based on drivers' physiological parameters. Operating the steering wheel belongs to continuous driving behavior and the previous research reveals that the steering wheel performance is closely related to driver state. In this study, steering wheel angle signals from both unimpaired drivers and alcohol-impaired drivers were collected and processed through wavelet analysis. Furthermore, the time-frequency characteristics of the signals in the two states were extracted. The results show that the frequency of adjusting the steering wheel was significantly higher during unimpaired driving. Conversely, sudden adjustment of steering wheel during alcohol-impaired driving was more likely. Based on the coefficient feature of wavelet analysis, the standard deviation of detailed coefficients of wavelet analysis was proposed to be one of the indices for identification. The validity was demonstrated by additional experiments.
Study of Identification Method of Drunk Driving Based on Wavelet Analysis
11th International Conference of Chinese Transportation Professionals (ICCTP) ; 2011 ; Nanjing, China
ICCTP 2011 ; 2308-2318
2011-07-26
Conference paper
Electronic Resource
English
Drunk driving vehicle identification method based on SVM algorithm
European Patent Office | 2021
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