In-vehicle Drowsy Driving Detection and Alerting

Gaspar, John G; Schwarz, Chris W; Marshall, Dawn; Jenness, James; De Leonardis, Doreen; Blenner, Jordan A · 2023 · ROSA P / United States. Department of Transportation. National Highway Traffic Safety Administration

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Summary

This study addresses the efficacy of in-vehicle drowsiness notification systems during long-duration driving, a scenario where previous research has been limited. While drowsy driving contributes significantly to fatal crashes, particularly during overnight trips, it is unclear whether driver monitoring and alerting systems can effectively mitigate risk over extended periods or influence drivers' decisions to stop and rest. The research specifically evaluated whether Lane Departure Warning (LDW) and Drowsiness Notification combined with LDW (DN/LDW) systems could improve driving performance and alter break-taking behavior compared to a no-notification baseline. The study employed a between-subjects experimental design using the high-fidelity National Advanced Driving Simulator. Seventy-two male participants, aged 21 to 30, completed a four-hour overnight drive between 2 a.m. and 6 a.m. To replicate the motivational tradeoffs of real-world drowsy driving—balancing the desire to reach a destination against safety risks—participants were subjected to an incentive structure offering monetary bonuses for completing the drive under a time limit, with penalties for road departures or crashes. Participants were randomly assigned to one of three conditions: a baseline with no notifications, an LDW-only condition, or a combined DN/LDW condition. The DN system utilized steering and eye-tracking data to estimate drowsiness, issuing visual and auditory alerts when drowsiness levels were high. Results indicated that the combined DN/LDW system was effective in improving driving performance, whereas the LDW alone was not. The DN/LDW condition resulted in a statistically significant reduction in the frequency of lane departures compared to the baseline. Additionally, the DN/LDW condition reduced the percentage of eyelid closure (PERCLOS) prior to lane departure events, suggesting that the notification increased alertness specifically during high-risk moments. In contrast, the LDW condition showed no significant difference from the baseline in either lane departure frequency or PERCLOS. Crucially, neither notification system altered drivers' decision-making regarding rest; there were no significant differences between conditions in the frequency, timing, or duration of breaks taken. The findings suggest that while in-vehicle notifications can mitigate immediate driving performance deficits associated with drowsiness, they do not effectively change the behavioral decision to stop and rest. The study implies that combined systems monitoring driver state and providing targeted alerts are more effective than performance-based warnings alone for maintaining lane control during long drives. However, because notifications failed to influence rest-taking behavior, the authors conclude that such technologies may not be sufficient to address the root cause of drowsy driving crashes, which often require drivers to recognize the need for sleep rather than just maintaining momentary alertness.

Key finding

A combined drowsiness notification and lane departure warning system significantly reduced lane departure frequency and eyelid closure during long overnight drives, but did not influence drivers' decisions to take rest breaks.

Methodology

simulator

Sample size: 72

Provenance

The full processing record for this entry. Every stage of this paper's journey through the pipeline is logged — what ran, with which tool and model, how many attempts it took, and when it last completed. Discovered via bulk_ingest_rosap on 2026-05-23 (6 acquisition events logged).

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
• sleep deprivation
• vigilance
• microsleep
• truck driver fatigue

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
• Methodological Resource: tool software