Cyclist Overtaking Safety Study Using Video Data
Bibliographic record
Abstract
While cycling has grown considerably in recent years, it is not as safe as it should be, and the perception of unsafety hinders its further growth. Intersections are the most complex parts of the road network, but cyclists are also particularly vulnerable on road segments when overtaken by motorized vehicles. Laws have been enacted to guarantee safe overtaking events by vehicles with minimum distance and other speed requirements. While there have been several studies on cyclist passing distance using instrumented bikes or vehicles, there have been few attempts at developing an automated system for the safety analysis of cyclist overtaking at a fixed location. This paper presents a computer vision tool to measure the cyclist passing distance and vehicle speeds before, during and after the overtaking. The tool is tested on a case study in Montréal, Canada. It shows that most passing distances are safe, but that most drivers fail to comply with the requirement to decelerate while overtaking cyclists.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".