A benchmark for cycling close pass detection from video streams
Bibliographic record
Abstract
Cycling is a healthy and sustainable mode of transport. However, interactions with motor vehicles remain a key barrier to increased cycling participation. The ability to detect potentially dangerous interactions from on-bike sensing could provide important information to riders and policymakers. A key influence on rider comfort and safety is close passes, i.e ., when a vehicle narrowly passes a cyclist. In this paper, we introduce a novel benchmark, called Cyc-CP, towards close pass (CP) event detection from video streams. The task is formulated into two problem categories: scene-level and instance-level. Scene-level detection ascertains the presence of a CP event within the provided video clip. Instance-level detection identifies the specific vehicle within the scene that precipitates a CP event. To address these challenges, we introduce four benchmark models, each underpinned by advanced deep-learning methodologies. For training and evaluating those models, we have developed a synthetic dataset alongside the acquisition of a real-world dataset. The benchmark evaluations reveal that the models achieve an accuracy of 88.13% for scene-level detection and 84.60% for instance-level detection on the real-world dataset. We envision this benchmark as a test-bed to accelerate CP detection and facilitate interaction between the fields of road safety, intelligent transportation systems and artificial intelligence. Both the benchmark datasets and detection models will be available at https://github.com/SustainableMobility/cyc-cp to facilitate experimental reproducibility and encourage more in-depth research in the field. • First benchmark for close pass (CP) detection between vehicles and cyclists from video • Formulation of Approaches for Scene-level and instance-level detection • Evaluation of Large Multimodal Models for CP using a prompting • Comprehensive Benchmarking on both real-world and synthetic datasets
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.003 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".