Classification of Fatigued and Drunk Driving Based on Decision Tree Methods: A Simulator Study

Ying Yao, Xiaohua Zhao, Hongji Du, Yunlong Zhang, Guohui Zhang and Jian Rong
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Ying Yao: Beijing Key Laboratory of Traffic Engineering, College of Metropolitan Transportation, Beijing University of Technology, 100 Pingleyuan, Chaoyang District, Beijing 100124, China
Xiaohua Zhao: Beijing Key Laboratory of Traffic Engineering, College of Metropolitan Transportation, Beijing University of Technology, 100 Pingleyuan, Chaoyang District, Beijing 100124, China
Hongji Du: Autonomous Driving unit, Baidu.com, Inc, No. 10 Xibeiwang East Road, Haidian District, Beijing 100193, China
Yunlong Zhang: Zachry Department of Civil Engineering, Texas A&M University, 3136 TAMU, College Station, TX 77843, USA
Guohui Zhang: Department of Civil and Environmental Engineering, University of Hawaii at Manoa, 2540 Dole Street, Holmes 338, Honolulu, HI 96822, USA
Jian Rong: Beijing Key Laboratory of Traffic Engineering, College of Metropolitan Transportation, Beijing University of Technology, 100 Pingleyuan, Chaoyang District, Beijing 100124, China

IJERPH, 2019, vol. 16, issue 11, 1-17

Abstract: It is a commonly known fact that both alcohol and fatigue impair driving performance. Therefore, the identification of fatigue and drinking status is very important. In this study, each of the 22 participants finished five driving tests in total. The control condition, serving as the benchmark in the five driving tests, refers to alert driving. The other four test conditions include driving with three blood alcohol content (BAC) levels (0.02%, 0.05%, and 0.08%) and driving in a fatigued state. The driving scenario included straight and curved roads. The straight roads connected the curved ones with radii of 200 m, 500 m, and 800 m with two turning directions (left and right). Driving performance indicators such as the average and standard deviation of longitudinal speed and lane position were selected to identify drunk driving and fatigued driving. In the process of identification, road geometry (straight segments, radius, and direction of curves) was also taken into account. Alert vs. abnormal and fatigued vs. drunk driving with various BAC levels were analyzed separately using the Classification and Regression Tree (CART) model, and the significance of the variables on the binary response variable was determined. The results showed that the decision tree could be used to distinguish normal driving from abnormal driving, fatigued driving, and drunk driving based on the indexes of vehicle speed and lane position at curves with different radii. The overall accuracy of classification of “alert” and “abnormal” driving was 90.9%, and that of “fatigued” and “drunk” driving was 94.4%. The accuracy was relatively low in identifying different BAC degrees. This experiment is designed to provide a reference for detecting dangerous driving states.

Keywords: fatigued driving; drunk driving; driving performance; roadway geometry; decision tree (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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