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

Ying Yao1, Xiaohua Zhao2, Hongji Du3

  • 1Beijing Key Laboratory of Traffic Engineering, College of Metropolitan Transportation, Beijing University of Technology, 100 Pingleyuan, Chaoyang District, Beijing 100124, China. yaoying@emails.bjut.edu.cn.

International Journal of Environmental Research and Public Health
|June 5, 2019
PubMed
Summary

Driving performance data can identify alcohol impairment and fatigue. A decision tree model accurately distinguished alert driving from abnormal states, achieving 90.9% accuracy in classifying fatigued versus drunk driving.

Keywords:
decision treedriving performancedrunk drivingfatigued drivingroadway geometry

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Area of Science:

  • Road safety
  • Human factors in driving
  • Traffic psychology

Background:

  • Alcohol and fatigue are known to impair driving performance.
  • Accurate identification of driver impairment is crucial for road safety.
  • Existing methods for detecting these states may lack precision.

Purpose of the Study:

  • To develop and validate a method for identifying drunk and fatigued driving states.
  • To analyze the impact of alcohol (at various Blood Alcohol Content levels) and fatigue on driving performance indicators.
  • To assess the effectiveness of a Classification and Regression Tree (CART) model in distinguishing driving states.

Main Methods:

  • 22 participants completed driving tests under control (alert), fatigued, and three Blood Alcohol Content (BAC) levels (0.02%, 0.05%, 0.08%).
  • Driving performance was assessed using longitudinal speed and lane position data on varied road geometries (straight and curved segments).
  • A Classification and Regression Tree (CART) model was employed to analyze the data and classify driving states.

Main Results:

  • The CART model successfully differentiated between alert and abnormal driving states with 90.9% overall accuracy.
  • Classification accuracy for distinguishing between fatigued and drunk driving reached 94.4%.
  • The model showed lower accuracy in differentiating specific Blood Alcohol Content (BAC) levels.

Conclusions:

  • Decision tree analysis using vehicle speed and lane position data effectively identifies abnormal driving conditions, including fatigue and alcohol impairment.
  • The developed model provides a reliable framework for detecting dangerous driving states.
  • Further refinement is needed to improve the accuracy of distinguishing varying degrees of alcohol impairment.