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Modeling behavioral indicators for driver drowsiness detection: a simulator-based study
Rahele Azaddel1, Mahdi Rezaei2, Homayoun Sadeghi-Bazargani2
1Department of Occupational Health and Ergonomics, Faculty of Health, Tabriz University of Medical Sciences, Tabriz, Iran.
Traffic Injury Prevention
|August 7, 2026
Summary
Behavioral indicators like increased blinking and head movements can predict driver drowsiness. This study models these signs for noninvasive detection systems, achieving high accuracy.
Area of Science:
- Road safety and human factors research.
- Development of noninvasive biosensing technologies.
- Application of machine learning in automotive safety.
Background:
- Driver drowsiness is a significant cause of road accidents globally.
- Current detection methods often rely on invasive measures or subjective reporting.
- There is a need for reliable, noninvasive methods to monitor driver alertness.
Purpose of the Study:
- To model behavioral indicators of driver drowsiness.
- To develop a system for noninvasive drowsiness detection.
- To assess the predictive value of specific behavioral metrics.
Main Methods:
- Simulated driving tasks with 24 participants under varying alertness levels.
- Recording of behavioral metrics: eye-blinking frequency, head movement acceleration, eye movement variability.
- Drowsiness classification using Partial Least Squares Discriminant Analysis (PLS-DA) and Karolinska Sleepiness Scale.
Main Results:
- Increased blink frequency and fluctuations in head/eye movements correlated with drowsiness.
- Derived variables like Corrected Turning Ratio (CTR) showed predictive relevance.
- The PLS-DA model achieved high performance with an ROC AUC of 0.935.
Conclusions:
- Behavioral metrics offer practical predictive value for noninvasive drowsiness detection.
- The developed model effectively distinguishes between alertness and drowsiness.
- Future research should focus on larger sample sizes and external validation.
