Zhenlong Li1, Xue Jin2, Xiaohua Zhao2. 1. College of Metropolitan Transportation, Beijing University of Technology, Beijing, China. Electronic address: lzl@bjut.edu.cn. 2. College of Metropolitan Transportation, Beijing University of Technology, Beijing, China.
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.
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.