Drowsiness Mitigation Through Driver State Monitoring Systems: A Scoping Review

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Abstract

Objective: To explore the scope of available research and to identify research gaps on in-vehicle interventions for drowsiness that utilize driver monitoring systems (DMS). Background: DMS are gaining popularity as a countermeasure against drowsiness. However, how these systems can be best utilized to guide driver attention is unclear. Methods: A scoping review was conducted in adherence to PRISMA guidelines. Five electronic databases (ACM Digital Library, Scopus, IEEE Xplore, TRID, and SAE Mobilus) were systematically searched in April 2022. Original studies examining in-vehicle drowsiness interventions that use DMS in a driving context (e.g., driving simulator and driver interviews) passed the screening. Data on study details, state detection methods, and interventions were extracted. Results: Twenty studies qualified for inclusion. Majority of interventions involved warnings (n = 16) with an auditory component (n = 14). Feedback displays (n = 4) and automation takeover (n = 4) were also investigated. Multistage interventions (n = 12) first cautioned the driver, then urged them to take an action, or initiated an automation takeover. Overall, interventions had a positive impact on sleepiness levels, driving performance, and user evaluations. Whether interventions effective for one type of sleepiness (e.g., passive vs. active fatigue) will perform well for another type is unclear. Conclusion: Literature mainly focused on developing sensors and improving the accuracy of DMS, but not on the driver interactions with these technologies. More intervention studies are needed in general and for investigating their long-term effects. Application: We list gaps and limitations in the DMS literature to guide researchers and practitioners in designing and evaluating effective safety systems for drowsy driving.

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Ayas, S., Donmez, B., & Tang, X. (2024). Drowsiness Mitigation Through Driver State Monitoring Systems: A Scoping Review. Human Factors, 66(9), 2218–2243. https://doi.org/10.1177/00187208231208523

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