Trends and Future Prospects of the Drowsiness Detection and Estimation Technology

Arakawa, Toshiya · 2021 · Crossref

DOI: 10.3390/s21237921

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Summary

This report evaluates the scientific validity of six operator-centered, in-vehicle fatigue-monitoring technologies designed to detect drowsy driving. The research was motivated by the high incidence of fatigue-related crashes, the unreliability of subjective sleepiness estimates, and the potential for technology to offer a more flexible alternative to rigid hours-of-service regulations. The study aimed to determine which technologies could reliably detect hypovigilance, defined as lapses in visual attention, under controlled conditions. The methodology involved a double-blind, controlled laboratory experiment with 14 healthy adult males who remained awake for 42 hours. Every two hours, subjects performed a 20-minute Psychomotor Vigilance Task (PVT), which served as the validation criterion for fatigue-induced performance lapses. Six technologies were tested: video-based eye closure scoring (PERCLOS), two EEG algorithms, a head position monitoring device, and two wearable eye-blink monitors. Suppliers provided drowsiness metrics without knowledge of the PVT lapse data or the specific time of wakefulness, ensuring unbiased prospective validation. Coherence between each technology’s output and PVT lapses was calculated both bout-to-bout and minute-to-minute. The results demonstrated that PERCLOS, which measures the percentage of eyelid closure over the pupil, was the only technology to show high coherence with PVT lapses both within and between subjects (mean r = 0.875 for lapse frequency; r = 0.919 for lapse duration). PERCLOS outperformed subjective sleepiness ratings and maintained predictive validity even during the first 22 hours of wakefulness. While other technologies showed some potential in individual subjects, they lacked consistent inter-subject coherence. Additionally, a secondary pilot study with four subjects found that auditory and vibrotactile alerting stimuli did not significantly reduce PVT lapses beyond the immediate minute of stimulation, suggesting these modalities may be insufficient for sustained alertness restoration. The study concludes that PERCLOS is a scientifically valid index of fatigue-induced hypovigilance. However, for practical application, the manual scoring algorithm must be automated into a reliable, unobtrusive computer system suitable for over-the-road driving. The findings highlight the necessity of rigorous prospective validation for fatigue-detection technologies to prevent the deployment of ineffective devices that could provide a false sense of security. Future research should focus on automating PERCLOS and exploring more potent alerting stimuli, such as olfactory or thermoregulatory methods, to effectively manage driver fatigue.

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Topics

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• drowsiness detection algorithms
• microsleep
• drowsiness
• dms validation
• workload measurement
• vigilance

Information type

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• Empirical Findings: physiological data
• Methodological Resource: validation psychometrics, measurement protocol