Drowsy driver

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Assessing the Feasibility of Vehicle-Based Sensors to Detect Drowsy Driving

The goal of this research was to determine the feasibility of detecting drowsiness with vehicle-based sensors and the extent to which alcohol-impairment algorithms could detect drowsiness and distinguish it from alcohol impairment. Results showed that differences in alcohol impairment and drowsiness impairment prevent a single algorithm from detecting both types of impairment; rather, a more complex approach involving multiple algorithms is necessary.

Using an advanced driving simulator, data were collected from 72 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 the daytime session, driving performance improved during the early night session before degrading during the late night session. 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. DOT HS 811 358 (1.6 MB PDF)