title: Drunk driving detection based on classification of multivariate time series.
abstract: BACKGROUND
  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
  Drunk driving detection based on the analysis of multivariate time series is feasible and effective. The approach has implications for drunk driving detection.
authors.affiliation: College of Metropolitan Transportation, Beijing University of Technology, Beijing, China. Electronic address: lzl@bjut.edu.cn.
authors.forename: Zhenlong
authors.initials: Z
authors.lastname: Li
authors.affiliation: College of Metropolitan Transportation, Beijing University of Technology, Beijing, China.
authors.forename: Xue
authors.initials: X
authors.lastname: Jin
authors.affiliation: College of Metropolitan Transportation, Beijing University of Technology, Beijing, China.
authors.forename: Xiaohua
authors.initials: X
authors.lastname: Zhao
mesh_terms: D000063:Accidents, Traffic / Q000517:prevention & control; D000328:Adult; D000435:Alcoholic Intoxication; D000465:Algorithms; D001334:Automobile Driving; D001519:Behavior; D000068476:Beijing; D000066448:Driving Under the Influence; D006801:Humans; D008297:Male; D018986:Motor Vehicles; D015999:Multivariate Analysis; D011187:Posture; D062211:Spatio-Temporal Analysis; D060388:Support Vector Machine; D013647:Task Performance and Analysis; D055815:Young Adult
keywords: Bottom-up segmentation; Drunk driving detection; Multivariate time series; Support vector machine