Commercial Motor Vehicle Driver Fatigue And Alertness Study: Technical Summary

Wylie, CD.; Shultz, T.; Miller, J. C.; Mitler, M. M.; Mackie, RR. · 1996 · ROSA P / United States. Department of Transportation. Federal Highway Administration

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Summary

This technical summary outlines the Commercial Motor Vehicle Driver Fatigue and Alertness Study (DFAS), the largest and most comprehensive over-the-road investigation into driver fatigue conducted in North America at the time. The research was motivated by the established link between driver fatigue and severe, often fatal, single-vehicle crashes, as well as regulatory mandates from the U.S. Department of Transportation and Transport Canada to better understand the relationship between hours-of-service regulations, operator fatigue, and accident frequency. Despite historical studies, there was a critical need for factual data on how specific work-related factors influence fatigue impairment in real-world operational settings. The study employed a rigorous field methodology involving eighty commercial motor vehicle drivers in the United States and Canada, monitored over a sixteen-week period during revenue-generating trips. Researchers investigated several key variables: the duration of driving per work period, the number of consecutive driving days, the time of day, and schedule regularity. Data collection utilized a multi-modal approach, including video recordings of the drivers’ faces to assess drowsiness, physiological measures such as polysomnography (PSG) and quantitative EEG (QEEG) during driving and sleep, and performance metrics like lane tracking and steering wheel movement. Additionally, surrogate performance tests (e.g., code substitution, critical tracking) and driver self-assessments were administered to evaluate alertness and cognitive function. The primary finding was that time of day was the strongest and most consistent predictor of driver fatigue and alertness. Drowsiness, as evidenced by facial video recordings, was markedly greater during night driving compared to daytime driving. Conversely, the number of hours spent driving (time-on-task) and the cumulative number of consecutive driving days were not strong or consistent predictors of observed fatigue within the context of the sleep patterns and four-to-five-day driving cycles observed in the study. The results highlighted the dominance of circadian rhythm effects over cumulative workload in determining immediate alertness levels. The study concludes that fatigue is a complex issue influenced heavily by biological timing rather than just duration of work. These findings have significant implications for hours-of-service regulations, suggesting that current metrics may not adequately capture the risks associated with night driving. The report recommends further research into sleep needs, circadian rhythms, and the development of performance-based warning systems. It also emphasizes the need for improved driver education regarding self-awareness of fatigue and the potential utility of napping as a countermeasure. The study serves as a foundational reference for future policy and safety interventions aimed at reducing fatigue-related crashes in the commercial trucking industry.

Key finding

Time of day was the strongest and most consistent factor influencing driver fatigue and alertness, with drowsiness markedly greater during night driving than during daytime driving, while hours of driving and cumulative days were not strong predictors.

Methodology

naturalistic

Sample size: 80

Provenance

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Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.

Topics

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• truck driver fatigue
• drowsiness detection algorithms
• time on task
• drowsiness
• circadian factors
• shift work driving

Information type

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• Empirical Findings: physiological data, behavioral performance data
• Methodological Resource: validation psychometrics