In-Vehicle Drowsy Driving Detection and Alerting [Traffic Tech]

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 safety risks associated with drowsy driving, a significant contributor to fatal crashes in the United States. While official statistics attribute a small percentage of fatalities to drowsiness, research suggests this is underreported, with estimates indicating that up to 21% of fatal crashes involve a drowsy driver. The study aimed to evaluate the efficacy of in-vehicle countermeasures, specifically comparing a state-based drowsiness notification (DN) system combined with lane departure warning (LDW) against a performance-based LDW system alone and a baseline condition with no countermeasures. The primary objective was to determine if these systems could reduce lane departures and improve driver alertness during long, monotonous drives. The experiment utilized the high-fidelity National Advanced Driving Simulator at the University of Iowa. Seventy-two male drivers, aged 21 to 30, were randomly assigned to one of three conditions: baseline, LDW only, or DN/LDW. To induce drowsiness, participants were awake for a minimum of 18 hours before undertaking a 220-mile drive on a 40-mile Interstate loop. A novel incentive structure was employed to replicate the motivational tradeoffs of real-world drowsy driving; participants were told they would lose compensation for road departures but earn bonuses for completing the drive quickly, encouraging them to "push through" drowsiness rather than rest. The DN/LDW system used steering and eye-tracking data to display an attention scale and issued auditory and visual alerts when drowsiness levels were high. Results indicated that the DN/LDW condition significantly reduced the frequency of lane departures compared to the baseline, whereas the LDW-only condition did not differ significantly from the baseline. The DN/LDW system also resulted in lower PERCLOS (percentage of eyelid closure) values prior to lane departures, suggesting improved alertness. Additionally, both notification conditions led to faster stabilization times following lane departures compared to the baseline, indicating quicker corrective responses. However, there were no significant differences in the severity of lane departures or in participants' stopping behavior; drivers in all conditions took similar numbers, durations, and timing of breaks. The findings suggest that state-based drowsiness notifications are effective preventive countermeasures that reduce the likelihood of lane departures and improve immediate driving performance by enhancing alertness. However, because these systems did not influence drivers' decisions to take breaks, they are characterized as short-term solutions that mitigate but do not eliminate the consequences of drowsiness. The study highlights the potential of integrated monitoring and alerting systems to improve safety during long drives, though limitations regarding the simulator environment and the specific demographic of young male drivers suggest caution in generalizing these results to the broader population.

Key finding

A combined drowsiness notification and lane departure warning system significantly reduced the frequency of lane departures and pre-departure drowsiness levels compared to a baseline condition, although it did not alter drivers' rest-taking behavior.

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
• truck driver fatigue
• vigilance
• drowsy as impairment

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
• Methodological Resource: tool software, validation psychometrics