Improving Takeover Requests in Automated Vehicles: The Role of Dynamic Alerts and Cognitive State
Bibliographic record
Abstract
Effective driver re-engagement is essential for the safe operation of automated driving systems (ADS), especially during takeover requests. This study examines the effects of alert intensity and driver engagement in non-driving related tasks on takeover performance in automated vehicles. Forty-one participants navigated simulated driving scenarios with various hazards and alert conditions, allowing analysis of response times, physiological responses, and subjective perceptions. The findings showed that higher-intensity alerts significantly reduced reaction times, leading to quicker driver takeovers. Interestingly, engagement in certain secondary tasks sometimes improved driver responsiveness, suggesting that moderate cognitive engagement may enhance alertness. Response times varied across different driving scenarios, indicating the influence of situational context on driver behavior. Physiological measures, including eye-tracking and heart rate monitoring, showed increased cognitive and physiological arousal during takeover events, particularly in response to stronger alerts. Participants reported higher confidence and satisfaction with high-intensity alerts, without reporting increased annoyance, indicating a preference for more assertive alert mechanisms. These outcomes highlight the critical role of adaptive alert mechanisms in ADS design, promoting for context-aware signaling and real-time driver state monitoring to improve takeover performance and ensure vehicular safety. The study suggests that future ADS should integrate dynamic alert systems capable of adjusting intensity based on situational urgency and driver engagement, thereby enhancing overall system reliability and user acceptance.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".