An Exploration of Drivers’ Scanning Behaviors Towards Vulnerable Road User Areas at Intersections: An On-Road Study
Bibliographic record
Abstract
The safety of vulnerable road users (VRUs) at intersections is an active concern. An on-road study was conducted to understand the relationship between VRU infrastructure designs at intersections and drivers’ visual behaviors. 20 experienced drivers (9 cyclists, 11 non-cyclists) completed 228 turns at 13 unique intersections in Guelph, Ontario. Coding of drivers’ eye tracking data showed that VRU infrastructure did not appear to be associated with drivers’ visual scanning performance. Intersections with large numbers of past collisions did not appear to have more scanning failures. Other characteristics, like turn direction and VRU traffic, may interact with infrastructure design and contribute to VRU risks. Overall, drivers committed visual scanning failures at 31% of turns, of which 44% of failures had high criticality. Right turns had more failures than lefts, and drivers with cycling experience appeared to have fewer failures. A larger sample size across contexts would help generalize these results.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".