Exploring the effect of driver drowsiness on takeover performance during automated driving: An updated literature review
DOI: 10.1016/j.aap.2025.108023
archive: archived pipeline: cataloged verified
Get this paper ↗ (DOI — opens at the source; we link to it, we don't host it)
Summary
This updated literature review addresses the critical safety challenge of driver drowsiness during automated driving, specifically focusing on its impact on takeover performance. As vehicle automation advances, drivers in SAE Level 2–4 systems must transition from passive monitoring to active control when requested. However, automation can exacerbate drowsiness due to task underload, creating a significant risk when drivers must regain control. The study aims to update a 2022 systematic review by synthesizing recent research (2021–2024) to identify factors influencing drowsiness and subsequent takeover performance, highlighting gaps in current knowledge. The authors conducted a systematic review following PRISMA guidelines, searching Web of Science, PubMed, and Scopus for studies published between March 2021 and October 2024. Eligibility criteria required studies to involve SAE Level 2 or higher automation, include measurements of both driver drowsiness and takeover performance, and utilize controlled experimental designs. From an initial pool of 182 articles, 12 new studies were selected and combined with 17 from the previous review, resulting in 29 total articles. The methodological quality of these studies was assessed using the PEDro Scale, with most rated as "good" or "excellent." The review analyzed experimental elements such as automation levels, simulation versus real-world settings, and specific metrics for drowsiness and performance. The findings indicate that driver drowsiness increases with longer durations of automated driving and higher automation levels. Engaging in non-driving-related tasks (NDRTs) alleviates physiological signs of drowsiness, such as heart rate and eye closure, but significantly impairs takeover performance, leading to longer braking reaction times and increased collision risks. Age plays a divergent role: younger drivers are more susceptible to becoming drowsy, whereas older drivers exhibit worse takeover performance, including delayed steering reactions and higher collision rates. Sleep inertia and circadian rhythms also negatively impact performance. Conversely, strategies such as road monitoring tasks, digital voice assistants, and scheduled manual driving help maintain alertness and improve takeover metrics, such as reducing brake reaction times and steering velocity. The review concludes that while several factors influence drowsiness and takeover capability, existing research suffers from methodological limitations, including a lack of real-world verification, insufficient diversity in drowsiness measurements, and singular takeover scenarios. The authors emphasize the need for future research to uncover the underlying mechanisms linking drowsiness to performance, develop effective prevention and alleviation strategies, and design systems that assist drowsy drivers in regaining control safely. This work underscores the necessity of addressing human factors in automated driving to ensure safety before full automation becomes widespread.
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.
| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | Crossref | — | — | 1 | 2026-08-09 |
| archive | success | openalex | — | — | 5 | 2026-08-09 |
| extract | success | cached | — | — | 3 | 2026-08-10 |
| clean | success | clean | — | — | 1 | 2026-08-09 |
| chunk | success | chunk | — | — | 1 | 2026-08-09 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-08-09 |
| enrich | success | semantic_scholar | — | — | 1 | 2026-08-09 |
| promote | success | — | — | — | 1 | 2026-08-09 |
| summarize | success | llm | qwen3.6-27b-nvidia | summ-v5 | 2 | 2026-08-10 |
| tag | success | vector_similarity | — | — | 10 | 2026-08-11 |
| verify | success | — | — | — | 2 | 2026-08-10 |
Summary generated by qwen3.6-27b-nvidia on 2026-08-10; verification: verified.
Topics
Ranked by relevance to this paper. Hover a topic for its definition.
- takeover transitions
- drowsiness
- automation
- drowsiness detection algorithms
- automation complacency bias
- time on task
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: behavioral performance data
- Methodological Resource: measurement protocol
- Theoretical Contribution: conceptual framework