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Deriving workload from driving behavior and psycho-physiology in work zones.

Chi Zhao1, Siyang Zhang1, Zherui Zhang1

  • 1The Key Laboratory of Road and Traffic Engineering, Ministry of Education, Tongji University, Shanghai 201804, China; College of Transportation, Tongji University, 4800 Cao'an Road, Shanghai 201804, China.

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Summary

Work zone driving increases driver uncertainty and workload. This study developed a machine learning model to accurately identify workload levels by analyzing driving behavior and physiological responses.

Keywords:
Driving behavior and psycho-physiologyDriving workloadSHapley Additive exPlanationStacking ensemble modelWork zones

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Area of Science:

  • Human Factors and Ergonomics
  • Transportation Engineering
  • Cognitive Psychology

Background:

  • Work zones present unique driving challenges, increasing uncertainty, behavioral deviations, and psychological workload, unlike typical driving scenarios.
  • Existing models fail to adequately explain work zone driving behaviors and psycho-physiology due to the complex and distinct driving environment.
  • Understanding the dynamic interplay between work zone driving behavior, driver psychology, and physiology is crucial for safety.

Purpose of the Study:

  • To investigate the dynamic interactions among work zone driving behavior, driver psychology, and physiology.
  • To develop an interpretable framework for representing driver workload in work zones.
  • To enhance the design and optimization of driver assistance and autonomous driving systems.

Main Methods:

  • A driving simulator study was conducted with baseline and three work zone scenarios.
  • Collected data included driving behavior, physiological metrics (heart rate, heart rate variability, pupil diameter, gaze), and post-simulation surveys.
  • A stacking ensemble learning model, optimized using Bayesian hyperparameter tuning (Optuna), was employed to identify workload levels.

Main Results:

  • The developed stacking ensemble model achieved 93.14% accuracy in identifying workload levels, outperforming other machine learning models.
  • Analysis using SHapley Additive exPlanation (SHAP) revealed significant correlations between high workload and specific driving behaviors (lateral position shifts, braking) and physiological responses (heart rate, heart rate variability, pupil diameter, gaze).
  • Higher workload was associated with greater lateral position shifts, more frequent brake pedal use, elevated heart rate, reduced heart rate variability, and increased changes in pupil diameter and gaze.

Conclusions:

  • The study successfully reveals dynamic relationships between driving behavior, driver psychology, and physiology in various work zone conditions.
  • The findings provide critical insights for improving the design and optimization of driver assistance and autonomous driving systems for work zone environments.
  • The interpretable workload representation framework offers a novel approach to understanding driver states in complex driving scenarios.