Can unreliable auditory hazard warnings help the driver? The effect of timing errors and false alarms on road hazard detection in dynamic road scenes.
Bibliographic record
Abstract
Vehicle-based cues can speed hazard detection and reduce collisions. However, no technology is perfect, and the impact of erroneous cues on driver performance must be understood. We examined how auditory hazard warning cues with errors affect drivers’ ability to locate hazards in real road footage. Experiment 1 examined the effect of cue timing error on localization performance by varying the duration between cue and hazard onset. Warning cues reduced response time regardless of timing, and earlier cues speeded responses more than later cues. However, hazards are rare in real driving. Therefore, in Experiment 2, we added hazard absent trials and false alarm cues to investigate the impact of decreased cue reliability (80% or 50%). In Experiment 2, cues did not significantly affect hazard localization performance regardless of reliability. These results indicate that false alarms and hazard prevalence modulate warning cue usage and must be considered when designing in-car warning systems.
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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 | 0.002 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".