VR Collide! Comparing Collision-Avoidance Methods Between Co-located Virtual Reality Users
Bibliographic record
Abstract
We present a pilot study comparing visual feedback mechanisms for preventing physical collisions between co-located VR users. These include Avatar (a 3D avatar in co-located with the other user), BoundingBox (similar to HTC's "chaperone"), and CameraOverlay (live video feed overlaid on the virtual environment). Using a simulated second user, we found that CameraOverlay and Avatar had the fastest travel time around an obstacle, but BoundingBox had the fewest collisions at 0.07 collision/trial versus 0.2 collisions/trial for Avatar and 0.4 collisions/trial for CameraOverlay. However, subjective participant impressions strongly favoured Avatar and CameraOverlay over BoundingBox. Based on these results, we propose future studies on hybrid methods combining the best aspects of Avatar (speed, user preference) and BoundingBox (safety).
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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 | 0.002 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".