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Dataset rebalancing may hinder collision prediction accuracy in driver monitoring systems. Erratic gaze patterns, not pupil dilation, indicate higher collision risk during automated driving transitions.

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Area of Science:

  • Automotive Safety
  • Human-Computer Interaction
  • Cognitive Psychology

Background:

  • Driver Readiness (DR) is crucial for safe transitions between automated and manual driving.
  • Predictive modeling of collision risk in automated driving is challenged by rare collision events and imbalanced datasets.
  • The reliability of dataset rebalancing techniques for Driver Monitoring Systems (DMS) is largely untested.

Purpose of the Study:

  • To evaluate the statistical reliability of imbalance-corrected datasets in predictive modeling for driver readiness.
  • To assess the predictive value of gaze entropy and pupil diameter for collision risk during control transitions in SAE L2 driving.
  • To investigate the impact of dataset rebalancing on collision probability prediction accuracy.

Main Methods:

  • Simulated hands-off SAE L2 driving experiment with critical control transitions.
  • Analysis of gaze entropy and mean pupil diameter as indicators of driver state.
  • Comparison of predictive model performance using original versus rebalanced datasets.

Main Results:

  • Dataset rebalancing decreased prediction accuracy and inflated collision probability estimates.
  • Erratic, widely distributed gaze fixations correlated with increased collision probability.
  • Mean pupil diameter, indicating mental workload, showed minimal impact on collision risk prediction.

Conclusions:

  • Researchers should exercise caution when using rebalanced datasets for predictive driver readiness modeling.
  • Gaze behavior is a significant predictor of driver readiness and collision risk.
  • Dataset rebalancing presents challenges for reliable predictive modeling in driver monitoring systems.