Innovations in using virtual reality to study how children cross streets in traffic: evidence for evasive action skills
Bibliographic record
Abstract
PURPOSE: Children in middle childhood are at an increased risk for injury in pedestrian environments. This study examined whether they are capable of showing evasive action (ie, adjusting crossing speed) to avoid injury when crossing streets. METHODS: The study used a fully immersive virtual reality (VR) system interfaced with a three-dimensional movement measurement system so that the actual crossing behaviour of 7-10-year-old children under different traffic conditions could be precisely measured. Relating outcomes to that which would have been obtained based on using the approach of estimating walking speed and assuming a constant speed provided insights into the realised benefits of the current movement monitoring VR system. RESULTS: Controlling for age and sex, children showed evasive action, crossing more quickly as traffic conditions became more risky. Using an average and assuming a constant walking speed underestimated actual walking speed, failing to capture evasive action and leading to overestimation of children being hit compared with the actual incidence of hits. CONCLUSIONS: VR technology is a valuable tool for assessing child pedestrian behaviour. However, systems need to allow the child to cross the street so their level of pedestrian skill is appropriately measured. The current findings provide the first evidence that children are capable of implementing evasive action in reaction to risky traffic conditions.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".