An Assessment of Driver Drowsiness, Distraction, and Performance in a Naturalistic Setting

Barr, Lawrence; Yang, C. Y. David; Hanowski, Richard J.; Olson, Rebecca L · 2011 · ROSA P / United States. Department of Transportation. Federal Motor Carrier Safety Administration

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Summary

This study, conducted under the sponsorship of the Federal Motor Carrier Safety Administration (FMCSA), characterizes episodes of driver drowsiness and assesses their impact on driving performance in a naturalistic setting. The research focuses on local/short-haul (L/SH) commercial truck drivers, aiming to understand drowsiness as a naturally occurring phenomenon and identify associated operational, environmental, and driver-specific factors. The study builds upon earlier FMCSA data to provide a comprehensive analysis of fatigue in L/SH operations, where drivers typically start and end their shifts at a home base within 100 miles. The methodology involved analyzing approximately 900 hours of continuous naturalistic driving video data collected from 42 L/SH drivers over two-week periods. Trucks were instrumented with sensors for vehicle parameters and cameras capturing interior and exterior views. Researchers identified 2,745 drowsy events, assigning each an Observer Rating of Drowsiness (ORD) from 2 (slightly drowsy) to 5 (extremely drowsy). A "Fatigue Index" was developed to normalize frequency and severity by driving time. Statistical analyses, including logistic regression, analysis of variance, and contingency table analysis, were used to examine relationships between drowsiness and variables such as driver demographics, time of day, sleep patterns, and driving conditions. Key findings indicate that higher levels of drowsiness are strongly associated with younger and less experienced drivers. Drivers aged 19–25 were nine times more likely to be classified as "High Fatigue" than older drivers, and those with less than one year of experience were seven times more likely than experienced drivers. Time of day was a significant predictor, with drowsiness twice as likely to occur between 6 a.m. and 9 a.m. Approximately 30% of all drowsy events occurred within the first hour of the work shift, suggesting drivers often begin their days without being fully refreshed. While sleep quantity and quality showed weak associations with drowsiness, likely due to data limitations, the study confirmed that drowsy drivers exhibit "tunnel vision," characterized by reduced eye transitions and less time looking away from the forward roadway. This reduction in environmental awareness compromises hazard recognition. The study also examined the relationship between drowsiness and driving performance. Lane-keeping violations were observed in 4.5% of drowsy events (ORD 5), with 19 of 41 drivers showing impaired performance. However, speed management was not significantly impacted by drowsiness. The research highlights that drowsiness often occurs during low-workload, monotonous driving, prompting drivers to engage in countermeasures like stretching or singing, whereas high-attention secondary tasks like phone use occurred primarily when drivers were alert. The findings provide an analytical framework for assessing drowsiness and suggest that future warning systems should integrate physiological data with vehicle performance metrics to overcome the limitations of current PERCLOS-based monitors.

Key finding

Drowsy driving events were twice as likely to occur between 6 a.m. and 9 a.m., and approximately 30 percent of all observed instances of drowsiness occurred within the first hour of the work shift.

Methodology

naturalistic

Sample size: 42

Provenance

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

Topics

Ranked by relevance to this paper. Hover a topic for its definition.

• drowsiness detection algorithms
• drowsiness
• truck driver fatigue
• time on task
• sleep deprivation
• vigilance

Information type

What kind of knowledge this paper contributes, grouped by family — independent of topic (what it is about) and method (how it was studied).

• Empirical Findings: physiological data, behavioral performance data
• Theoretical Contribution: theory or model