Effects of Level 3 Automated Vehicle Drivers’ Fatigue on Their Take-Over Behaviour: A Literature Review
Bibliographic record
Abstract
With advancements in automated driving technology, Level 3 vehicle automation has been proliferating. In Level 3, drivers can turn their attention to things unrelated to driving and only takeover in cases of emergency. However, the transition to the main driving task can be affected by different physical and psychological changes in drivers such as fatigue and distraction. Although existing research in the field of manual driving has demonstrated the effects of fatigue on driver performance, there have been relatively few studies on fatigue in the field of automated driving. Fatigue decreases drivers’ alertness, which can prevent drivers from conducting necessary emergency takeover manoeuvres in a safe and timely manner. This systematic literature review was conducted to establish relationships between driving fatigue and takeover behaviour in the existing literature on Level 3 automated driving systems. Different from existing reviews of driving fatigue, this paper focuses on the commonly used and most effective evaluation indicators of driving fatigue and further discusses the methods to improve the takeover efficiency of fatigued drivers. This is the first detailed systematic review of general relationships between fatigue in automated driving and drivers’ takeover behaviours when the vehicle sends a takeover request. Researchers are invited to expand on the findings here to further clarify the findings on the associations between driver fatigue indicators in automated vehicle driving and drivers’ takeover response times.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".