Drunk driving detection based on classification of multivariate time series |
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| Institution: | 1. The Netherlands Organisation for Applied Scientific Research TNO, Soesterberg, The Netherlands;2. University of Twente, Enschede, The Netherlands;1. School of Civil and Environmental Engineering, Georgia Institute of Technology, 788 Atlantic Drive, Atlanta, GA 30332, United States;2. Department of Psychology, Morehead State University, 151 Fourth Street, Morehead, KY 43051, United States;1. School of Transportation Engineering, Tongji University, Shanghai 201804, China;2. Road and Traffic Key Laboratory, Ministry of Education, Shanghai 201804, China;3. School of Transportation and Logistics, Southwest Jiaotong University, Chengdu 610031, China;4. National United Engineering Laboratory of Integrated and Intelligent Transportation, Chengdu 610031, China |
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| Abstract: | IntroductionThis paper addresses the problem of detecting drunk driving based on classification of multivariate time series.MethodsFirst, 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.ResultsThe proposed approach achieved an accuracy of 80.0%.Conclusions and practical applicationsDrunk driving detection based on the analysis of multivariate time series is feasible and effective. The approach has implications for drunk driving detection. |
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