Identifying factors that contribute to collision avoidance behaviours while walking in a natural environment
Bibliographic record
Abstract
ABSTRACT Busy walking paths, like in a park, a sidewalk in a city centre, or a shopping mall, frequently necessitate collision avoidance behaviour. Lab-based research has shown how a variety of situation-specific factors (e.g., distraction, object/pedestrian proximity) and person-specific factors (e.g., pedestrian size, age), typically studied independently, affect avoidance behaviour. What happens in the real world is unclear. Thus, we filmed unscripted pedestrian walking behaviours on a busy ∼3.5 m urban path adjacent to the water. We leveraged deep learning algorithms to identify and extract walking trajectories of pedestrians and had unbiased raters characterize interaction details. Here we analyzed over 500 situations where two pedestrians approached each other from opposite ends (i.e., one-on-one pedestrian interactions). We found that smaller medial-lateral distance between approaching pedestrians and a lower number of surrounding pedestrians (i.e., smaller crowd size) predicted an increase in the likelihood of a subsequent path deviation. Furthermore, we found that whether a pedestrian looked distracted or held, pushed, or pulled something while walking predicted the medial-lateral distance between pedestrians at the time of crossing. Although pedestrians maintained a larger personal space boundary compared to lab settings, this is likely because of the outdoor path’s width. Overall, our results suggest that collision avoidance behaviours in lab and real-world environments share similarities and offer insights relevant to developing more accurate computational models for realistic pedestrian movement.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".