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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Ying Yao & Xiaohua Zhao & Hongji Du & Yunlong Zhang & Guohui Zhang & Jian Rong, 2019. "Classification of Fatigued and Drunk Driving Based on Decision Tree Methods: A Simulator Study," IJERPH, MDPI, vol. 16(11), pages 1-17, May.
    • Handle: RePEc:gam:jijerp:v:16:y:2019:i:11:p:1935-:d:236048
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    References listed on IDEAS

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    1. Dulebenets, Maxim A. & Abioye, Olumide F. & Ozguven, Eren Erman & Moses, Ren & Boot, Walter R. & Sando, Thobias, 2019. "Development of statistical models for improving efficiency of emergency evacuation in areas with vulnerable population," Reliability Engineering and System Safety, Elsevier, vol. 182(C), pages 233-249.
    2. Drew Dawson & Kathryn Reid, 1997. "Fatigue, alcohol and performance impairment," Nature, Nature, vol. 388(6639), pages 235-235, July.
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    Cited by:

    1. Jianping Zhang & Zhenling Chen & Weidong Liu & Pengxin Ding & Qinggang Wu, 2021. "A Field Study of Work Type Influence on Air Traffic Controllers’ Fatigue Based on Data-Driven PERCLOS Detection," IJERPH, MDPI, vol. 18(22), pages 1-17, November.

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