Driver Situational Awareness Prediction During Takeover Transitions: A Multimodal Machine Learning Approach

This study predicts drivers’ situational awareness (SA) of specific traffic elements during takeover transitions in Level 3 automated vehicles. Using a simulator dataset from 44 participants, we analyzed multimodal features, including driver characteristics, physiological data, eye movement, and environmental attributes. Various machine learning models (e.g., SVM, Logistic Regression, and XGBoost) were optimized through feature selection and time window tuning. The SVM model, using a 3-second post-TOR and 1-second pre-TOR window, achieved the best performance with a macro F1 score of 0.75 and 0.77 accuracy. Our approach highlights the importance of comprehensive feature sets and timely predictions for improving driver support systems. This model aids in identifying potential hazards and enhances takeover readiness, contributing to safer autonomous driving transitions.

• Record URL:
  • https://doi.org/10.1177/10711813241275904
• Supplemental Notes:
  • Copyright © 2024 Human Factors and Ergonomics Society.
• Authors:
  • Jia, Lesong
  • 0000-0002-3950-0905
  • Du, Na
• Publication Date: 2024-9

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 885-887
• Serial:
  • Volume: 68
  • Issue Number: 1

Subject/Index Terms

• TRT Terms: Alertness; Autonomous vehicle handover; Human factors; Level 3 driving automation; Machine learning; Predictive models
• Subject Areas: Highways; Safety and Human Factors; Vehicles and Equipment;

Filing Info

• Accession Number: 01944962
• Record Type: Publication
• Files: TRIS
• Created Date: Feb 6 2025 10:49AM