Collision avoidance behaviours of young adult walkers: Influence of a virtual pedestrian's age-related appearance and gait profile
Bibliographic record
Abstract
Populating virtual words with realistic pedestrian behaviours is challenging but essential to ensure natural interactions with users. In pedestrian encounters, successful collision avoidance requires adapting speed and/or locomotor trajectory based on situational- and personal-characteristics. Personal characteristics such as the age of a pedestrian can easily be observed, but whether it affects avoidance behaviours is unknown. This is however an important question to ensure realism and variety of the simulations. The purpose of the current study was to examine the influence of a virtual pedestrian’s (VP) age-related appearance and gait profile on avoidance behaviours during a circumvention task. We expected that older adult (OA) gait characteristics and/or appearance would result in more cautious behaviours. Young adults (YA; n=17, 23.6 ± 2.7yrs) navigated a virtual street using a HMD. Individuals walked 8m towards a goal, while avoiding an approaching VP who would approach and steer towards the participant’s left, right, or continue straight, while exhibiting different age-related appearances and gait profiles: 1. YA appearance, YA gait; 2. OA appearance, OA gait; 3. OA appearance, YA gait; and 4. YA appearance, OA gait. Results indicate that clearance was larger when the appearance of VP resembled an OA, and when the VP walked like an OA compared to a YA. Larger clearance distances observed with OA characteristics may be due to societal norms associated with the principle of parental respect as well as a cautious strategy for any potential instability (wavering) in balance commonly observed in OA. This research sheds light on how age-related cues influence pedestrian interactions, with implications for the design of populated virtual environments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".