Larger distances from larger vehicles: effect of vehicle size, viewing side and their facia on comfort distance in virtual reality
Bibliographic record
Abstract
Objective: It is of critical importance to develop socially sensitive vehicles that will enhance pedestrians’ sense of comfort and safety. The current study is the first to extend these effects to vehicles, by investigating individual comfort distance in virtual reality with regard to vehicles that vary in terms of size, viewing angle and anthropomorphized emotional expression. Furthermore, we investigate the effect of individual differences in terms of height, anxiety and aggression.Method: Forty-four individuals were presented with three-dimensional stimuli of vehicle models differing in size and viewing angle in virtual reality and positioned them at the distance they felt the most comfortable with.Results: Our results show that individuals are more comfortable standing further from larger vehicles and when presented with the front versus the rear view of a vehicle. Moreover, the distance from vehicles was negatively associated with the height of the individuals.Conclusion: This paper suggests that it is important for designing self-driving and autonomous vehicles to consider that vehicle size and direction as well as pedestrian’s height may impact the comfort distance felt by pedestrians. These data have clear implications for vehicle design, including self-driving and autonomous vehicles.KEY POINTSWhat is already known: Individuals maintain larger distances when in front of individuals/agents than beside or behind them.Individuals provide greater physical space to larger agents (animals and/or humans).No previous study investigated the effect of vehicle size, view angle, and fascia on the comfort distance preferred by individuals as pedestrians.What it adds: Individuals are more comfortable standing further from larger vehicles.Individuals prefer to place more distance between themselves and a vehicle when seeing it from the front versus the rear.Shorter individuals adopt a larger distance from vehicles irrespective of vehicle size and viewing side.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".