Drunk driving detection based on classification of multivariate time series

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Drunk driving detection based on classification of multivariate time series

Li, Zhenlong / Jin, Xue / Zhao, Xiaohua

ABSTRACT Introduction This paper addresses the problem of detecting drunk driving based on classification of multivariate time series. Methods First, driving performance measures were collected from a test in a driving simulator located in the Traffic Research Center, Beijing University of Technology. Lateral position and steering angle were used to detect drunk driving. Second, multivariate time series analysis was performed to extract the features. A piecewise linear representation was used to represent multivariate time series. A bottom-up algorithm was then employed to separate multivariate time series. The slope and time interval of each segment were extracted as the features for classification. Third, a support vector machine classifier was used to classify driver's state into two classes (normal or drunk) according to the extracted features. Results The proposed approach achieved an accuracy of 80.0%. Conclusions and practical applications Drunk driving detection based on the analysis of multivariate time series is feasible and effective. The approach has implications for drunk driving detection.

Highlights • A method based on multivariate time series for detecting drunk driving was proposed. • A bottom-up algorithm was used to separate the series of lateral position and steering angle. • Alcohol affects the slopes of vehicle lateral position and the slopes of steering angle. • A detection accuracy of 80% was achieved using a SVM classifier.