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 warnings that speed road hazard detection can reduce collision incidence and severity. However, no technology is perfect, and it is critical to understand the impact of incorrect cues on hazard detection. This study, conducted between September 2022 and September 2023, examined the impact of non-spatial hazard warning auditory cues on licensed drivers’ ability to localize hazards (situations requiring immediate response to avoid a collision) in real road footage when cues are mistimed (Experiment 1) and when cues include false alarms (Experiment 2). In Experiment 1, we varied the duration between cue and hazard onset, and found that earlier cues speeded responses more than later cues, and warning cues reduced response time regardless of timing. However, each trial included a hazard, whereas hazards are rare on the road. In Experiment 2, we added false alarm warnings and hazard-absent trials in two cue reliability conditions (80% and 50%), and these cues did not significantly affect hazard localization performance regardless of reliability. Although earlier auditory temporal warnings can speed hazard localization, these benefits disappear in the presence of false alarms in attentive drivers and suggest that classic cueing results may not necessarily translate to dynamic natural scenes with ambiguous targets onsets.
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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 | 0.030 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".