Investigating the Effects of Attention-Oriented vs. Reaction-Oriented Visual Alerts on Driver Performance during Takeover Events in Highly Automated Driving
Bibliographic record
Abstract
Highly automated driving requires drivers to regain vehicle control if a takeover request (ToR) is issued.This transition from passive occupant to active driver is affected by human cognitive abilities and research in this area aims to improve driver performance and safety in these situations.This research investigated the impact of ToR icons on driver takeover performance and cognitive processes (situation awareness, mental workload).Three alert designs were tested: one attention-oriented, one reaction-oriented, and one non-informative alert.In Experiment 1 (N=30), the methodology and alert designs were validated and refined.In Experiment 2, (N=45) the impact of alerting strategy on takeover performance was tested.Results indicated that reaction-oriented alerts produced the best performance and were most preferred among participants.These findings contribute to the understanding of takeover performance in highly autonomous vehicles and can be used for developing alerts that best support drivers' cognitive processing and decision-making during takeovers.
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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 | 0.011 |
| 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 | 0.000 | 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".