DF Consulting

Assessing the feasibility of vehicle-based sensors to detect drowsy driving

Assessing the feasibility of vehicle-based sensors to detect drowsy driving


Brown, T., Lee, J., Schwarz, C., Fiorentino, D., & McDonald, A. (2014). Assessing the feasibility of vehicle-based sensors to detect drowsy driving (Report No. DOT HS 811 886). Washington, DC: National Highway Traffic Safety Administration.

Full text

Drowsy driving is a significant contributor to death and injury crashes on our Nation’s highways, accounting for more than 80,000 crashes and 850 fatalities per year. The successful detection of drowsiness is a crucial step in implementing mitigation strategies to reduce the cost to society of drowsy driving. Building upon prior research in detecting impairment from alcohol and distraction, the goal of this research was to determine the extent to which alcohol impairment algorithms could detect drowsiness and distinguish it from alcohol impairment. Data were collected from seventy-two participants during daytime (9 a.m. - 1 p.m.), early night (10 p.m. – 2 a.m.), and late night (2 a.m. - 6 a.m.) sessions to provide data for algorithm testing and refinement. Driving data indicated a complex relationship between driving performance and conditions associated with drowsiness: compared to daytime session, driving performance improved during the early night session, before degrading during the late night session. This non-linear relationship between continuous time awake, subjective assessments of drowsiness and driving performance has the potential to complicate the early detection of drowsiness. Drowsiness, as indicated by unintended lane departures, occurred in all sessions and demonstrated a transient nature. Algorithms based on lane position and steering wheel data, which can be obtained inexpensively, were best at predicting drowsiness related lane departures. Alcohol detection algorithms were not successful in detecting drowsiness but could be retrained to do so. Rather than one algorithm being generalized to detect multiple impairments, these results indicate that specialized algorithms might co-exist and allow one to detect and differentiate alcohol and drowsy-impaired driving. These findings provide a better understanding of the relationship between impairment from alcohol and drowsiness and lay the foundation for detecting and differentiating among impairment from alcohol, drowsiness, fatigue and drugs.