Dynamic collision envelope in virtual reality walking with colliding pedestrians
Bibliographic record
Abstract
When walking, we detect possible collisions with other pedestrians and avoid them, estimating body volumes and safety margins. This safety margin, collision envelope, has been measured in limited conditions: only parallel approaching collisions with lateral offset were tested while subjects watched a walking video. These scenarios limit generalizability for real-world walking where walkers navigate freely with various approaching directions of pedestrians. To evaluate realistic and dynamic collision envelopes in a risk-free environment, we developed a virtual reality walking scenario using the Meta Quest 2 head-mounted display (HMD). While a subject walking with gaze movement in an empty real-world corridor, a corresponding virtual shopping mall with pedestrians approached from 20°, 40°, or 60° bearing angles on a collision course face-to-face or overtaken were shown on HMD. 10 non-colliding pedestrians on various walking paths were also present. Subjects were asked to freely and naturally avoided potential collisions (walking path or speed change). Subjects with homonymous hemianopia (HH; n=6) and subjects with normal vision (NV; n=8) avoided 20 face-to-face and 20 overtaken pedestrians. As a result of the collision avoidance behavior, the trajectories of pedestrians relative to the subjects were changed, and thus the safety margins in various paths were collected. Dynamic collision envelope was calculated as the area kept as the safety margin in more than 50% of trials. HH subjects had larger envelopes (0.95m2, SD=0.60) than NV subjects (0.71m2, SD=0.42; p=0.044) and envelopes were larger when colliders were approaching (1.14m2, SD=0.53) compared to overtaken (0.49, SD=0.19; p<0.001). These results may suggest a more conservative safety margin in HH than NV when avoiding potential collisions. Since the relative walking speeds of the approaching pedestrians were faster than the overtaken pedestrians, estimated time-to-collision may also affect the size and structure of the collision envelope.
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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 | 0.000 | 0.000 |
| 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".