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An interpretable stacking ensemble learning model for visual-manual distraction level classification for in-vehicle

Yahui Wang1, Zhoushuo Liang1, Pengfei Tian2

  • 1School of Medical Technology, Beijing Institute of Technology, Beijing 100081, China.

Accident; Analysis and Prevention
|June 28, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a new framework to detect driver distraction in intelligent vehicles. It accurately identifies distraction levels using pupil diameter and driving behavior, enhancing road safety.

Keywords:
Distraction detectionDriving performanceMachine learningVisual-manual distraction

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Area of Science:

  • Human-Computer Interaction
  • Intelligent Transportation Systems
  • Machine Learning for Automotive Applications

Background:

  • Driver distraction is a major safety concern in intelligent vehicles.
  • Accurate recognition of distraction is vital for human-vehicle interaction.
  • Existing methods may lack comprehensive analysis of distraction factors.

Purpose of the Study:

  • To develop and validate a framework for recognizing driver distraction levels.
  • To integrate feature selection, clustering, classification, and interpretability.
  • To enhance driver safety in intelligent vehicle environments.

Main Methods:

  • Feature Selection with Optimal Graph (SF²SOG) for dimensionality reduction.
  • Agglomerative Clustering for unsupervised classification of distraction behaviors.
  • A heuristic-based stacking ensemble model (AdaBoost, RF, XGBoost base learners; LR meta-classifier) for distraction level classification.
  • Shapley Additive exPlanations (SHAP) for model interpretability.

Main Results:

  • The heuristic-based stacking ensemble model achieved 96.25% accuracy.
  • Increased maximum pupil diameter (maxPD) and mean pupil diameter (meanPD) correlate with higher distraction.
  • Higher glance frequency and lane deviation indicate reduced situational awareness.

Conclusions:

  • The proposed framework effectively recognizes driver distraction levels.
  • Pupil diameter and driving behavior are key indicators of distraction.
  • Findings contribute to reducing accidents and improving safety in intelligent vehicles.