Traffic Gap Detection for Pedestrians with Low Vision
Bibliographic record
Abstract
PURPOSE: Pedestrians with low vision have identified crossing the street as a difficult task. With the increasing complexity of the crossing environment (actuated signals and roundabouts), the challenges are increasing. The purpose of this study was to evaluate the effect of two types of vision loss (central or peripheral) on the ability to detect gaps in traffic. METHODS: Forty-one subjects participated with 14 being fully sighted (FS), 10 having central vision loss from age-related macular degeneration (AMD), and 17 having peripheral vision loss from either retinitis pigmentosa or glaucoma. Standing at entry and exit lanes of a roundabout, subjects depressed a handheld trigger to indicate when there was a sufficient gap in traffic to cross the street. A total of twelve 2-min intervals were completed including four of those intervals with occluded hearing. RESULTS: No difference was found in the ability of the three subject groups to identify crossable or short gaps. There were significant differences in latency and safety margin. The AMD subjects did not perform as well as the FS or the subjects with retinitis pigmentosa/glaucoma. When hearing was occluded, the two vision loss groups did not show a change in sensitivity but the FS group did, being more sensitive when hearing was occluded. CONCLUSIONS: The purpose of this study was to evaluate the effect of low vision on the ability to detect crossable gaps in traffic. The findings suggest that subjects with AMD have an increased risk because they show significant latency in their identification of gaps and this in turn results in a reduction of safety margin.
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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 | 0.000 | 0.002 |
| 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".