Effect of Visual and Auditory Alerts on Older Drivers’ Glances toward Latent Hazards while Turning Left at Intersections
Bibliographic record
Abstract
Older drivers are known to make significantly fewer glances toward hazards that are hidden from view (latent hazards) than middle-aged drivers. This is especially true when the driver is making a left turn at an intersection at that critical point in the turn immediately after the driver enters the intersection. This has led to the development of training programs that can increase the frequency of these glances toward latent hazards at intersections. However, training programs can require time and money that many older adults may not have. Advances in machine vision and vehicle-to-vehicle communications technologies make possible the use of alerts that warn older drivers of the location of latent hazards at intersections. This driving simulator study investigates the effect of auditory and visual warning alerts on older drivers’ primary (before entering the intersection) and secondary (just after entering the intersection) glance behavior when making a left turn at an intersection. In a between-subjects design, forty older drivers navigated eight unique scenarios containing latent hazards either in the presence or in the absence of combined auditory and visual hazard warning alerts. The results showed that older drivers anticipated a significantly greater proportion of latent hazards in the presence of warning alerts both before they enter the intersection and after they enter the intersection. The results of this study suggest that a combination of auditory and visual alerts may be effective at improving older drivers’ glance behavior while making left turns at intersections.
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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 | 0.006 |
| 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) | 0.002 | 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".