Exploring the effect of driver drowsiness on takeover performance during automated driving: An updated literature review

Introduction: Vehicle automation technology has considerable potential for reducing road crashes associated with human error, including issues related to driver drowsiness. However, before full automation becomes available on public roads, it will be essential for drivers to take back control from automated driving systems when requested. This poses a challenge for drivers, particularly as automation may further exacerbate drowsiness. This paper aims to update a systematic review published in 2022 (Merlhiot & Bueno, Accident Analysis and Prevention, 170, 106536), to discuss factors affecting driving drowsiness and takeover performance with a particular focus on those not identified in previous review.

 Method: Following the Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines, three databases: Web of Science, PubMed and Scopus were searched for studies published between March 2021 and October 2024. The following eligibility criteria were applied for study inclusion: 1) participants must have interacted with a simulated or real-world vehicle featured with driving automation Level 2 or above; 2) with at least one measurement indicator of driver drowsiness; 3) with at least one measurement indicator of takeover performance; 4) be conducted within a controlled experimental design. From an initial selection of 182 articles from databases, a total of twelve published articles were obtained after removing duplicates, title, abstracts and full texts checking. Additionally, 17 articles from the previous review were included, resulting in a total of 29 articles for this review study. 

Results: Driver drowsiness (e.g, increased Karolinska Sleepiness Scale levels, blink frequency) tended to increase with both the duration of automated driving and automation levels. Engaging in non-driving related tasks (NDRTs) alleviates drowsiness (e.g, lower heart rate and percentage of eye closure), but reduces takeover performance (e.g., longer braking reaction times, stronger longitudinal acceleration, shorter minimal time to collision). Compared to older drivers, younger drivers were more susceptible to drowsiness, while older drivers had worse takeover performance (e.g., delayed steering reaction time, higher collision rates). Sleep inertia and circadian rhythms were also identified as factors influencing takeover performance. The road monitoring task helps prevent excessive participation in NDRTs and improves takeover performance (e.g, reduced brake reaction times and maximum steering velocity, increased the minimum time to collision). Digital voice assistants and scheduled manual driving help maintain alertness (e.g, decreased blink duration) and enhance takeover performance (e.g, shorter reaction time to resume steering). There were several limitations of the methodologies applied in the existing studies, among which were: 1) a lack of verification through real-world driving experiments; 2) insufficient diversity in the measurement of driver drowsiness; 3) singularity of takeover scenarios; 4) failure to reveal the mechanism by which drowsiness affects takeover performance. 

Conclusion: Factors such as duration of automated driving, NDRT engagement, driver age, sleep-related issues and automation levels influence the development of drowsiness and subsequent takeover performance. This literature review highlights several necessary directions for future research: 1) what underlying factors affect drowsiness and take over performance; 2) how to prevent the occurrence of driver drowsiness; 3) how to alleviate driver drowsiness once it occurs; 4) how to assist drowsy drivers to regain control of the vehicle safely and quickly.