Larger distances from larger vehicles: effect of vehicle size, viewing side and their facia on comfort distance in virtual reality
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it