A Novel Approach for Diagnosing Cycling Safety Issues using Automated Computer Vision Techniques
Bibliographic record
Abstract
The use of traffic conflicts for safety diagnosis has been gaining acceptance as a surrogate for collision data analysis as they provide insight into the failure mechanism that leads to road collisions. This paper demonstrates an automated proactive safety diagnosis approach for vehicles-cycling interactions using video-based computer vision techniques. Traffic conflicts are automatically detected and conflict indicators such as Time to collision (TTC) are calculated based on the analysis of the road-user positions in space and time. Additionally, non-conformance of vehicles to travel regulations; specified as failure to respect yielding signage at the intersection are identified. The procedure is applied for the safety analysis of a newly installed bike lane at the southern approach of a major Bridge (Burrard Bridge) in Vancouver, British Columbia. The results showed a high exposure of cyclists to traffic conflicts. Rear-end and merging conflicts between vehicles at the location were also identified and analyzed. Practical solutions to address the safety issues at the location were presented. The proposed approach overcomes shortcomings with reliance on collision data and the manual observations of traffic conflicts.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".