Examining pedestrian evasive actions as a potential indicator for traffic conflicts
Bibliographic record
Abstract
The use of traffic conflicts is gaining acceptance as a proactive approach to studying road safety. A traffic conflict involves a chain of events in which at least one of the involved road‐users performs some sort of evasive actions to avoid a potential collision. Pedestrian evasive actions are normally manifested by changes in the walking behaviour which is expressed through variations in their speed profile. This paper investigates the automatic detection of pedestrian evasive actions in a computer‐vision framework. The study proposes a new measure for detecting pedestrians undertaking evasive actions based on permutation entropy (PE). PE is a robust approach for discovering dynamic characteristics of a time‐series. In the current context, it reveals the degree of abnormality in the walking pattern by identifying the deviations from the normal free walking. The methodology is applied and validated using video data from an intersection in Shanghai, China. Results show that the PE‐based indicator has a high potential to identify and measure the severity of conflicts that involve pedestrian evasive actions compared to traditional time‐proximity measures (e.g. time‐to‐collision and post‐encroachment‐time). This research finds many applications in the modern transportation infrastructure monitoring, studying pedestrian crossing behaviour and developing safety programs for vulnerable road‐users.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".