Effects of Blur on Duration Thresholds for Road Hazard Detection
Bibliographic record
Abstract
How does the loss of visual acuity impact our ability to notice dangerous situations on the road, and might it impact particular kinds of hazards more? With age, visual performance declines, which impacts road safety since certain hazards may become harder to detect. In this study, we investigate the impact of blur on duration thresholds in a hazard detection task for older and younger drivers. Older participants (ages 55-70) and younger participants (ages 20-35) were shown blurred or non-blurred videos from the Road Hazard Stimuli and reported whether a hazard was present in the video. Video duration ranged from 67 to 1067 ms (2 to 32 frames). To determine duration thresholds, video duration on each trial was manipulated with a staircase procedure. In Experiment 1, observers simply determined whether a hazard was present in both blurred and non-blurred conditions, with separate staircases for each. As in prior results (Wolfe et al. 2020), older observers required longer viewing durations, but blur resulted in similar increases in threshold for older and younger observers (+78 ms, p<0.001). In a second experiment, we separated our hazard videos into two categories: vehicular (cars, trucks) and nonvehicular (pedestrians, cyclists, animals) categories, and independently determined hazard duration thresholds for each category across blurred and unblurred conditions and across older and younger observers. Duration thresholds were lower for nonvehicular hazards compared to vehicular hazards (p<0.001). We also found a larger effect of blur for nonvehicular hazards (+146 ms) compared to vehicular hazards (+53 ms), p=0.003. This result suggests that hazards defined by their higher spatial frequency content, like nonvehicular hazards, are more affected by blur. Our results indicate that loss of visual acuity has profound consequences for driver safety across age groups, and particularly for the safety of non-vehicular road users like pedestrians and cyclists.
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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 | 0.012 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".