Work Zone Safety: Benchmarking Studies between Virtual Reality-based Traffic Co-simulation Platform and Real Work-Zones
Bibliographic record
Abstract
The need for improvements in roadway work zone safety becomes increasingly pressing, with continuous accidents/incidents reporting around a thousand fatalities in the United States in 2021 alone.Though rare, efforts that lead to a deeper understanding of worker behaviors around work zones are necessary to increase safety of workers.Unlike long term work zones, short term or mobile work zones lack clear standards.Understanding worker behavior in such work zones is an integral part of the solution.However current means are ineffective due to simplicity in intrusion scenarios and/or lack of immersiveness of trainees in situations/scenarios where workzones are intruded.Our earlier work resulted in the development of an immersive Virtual Reality (VR) based traffic co-simulation platform with innovative alarming systems for work zone safety.However, there is still a need to understand whether the behaviors captured in immersive VR based work zones are representative of reality in terms of how they respond to received safety alarms.This work presents the findings of the same user studies performed on real and VR based work zones to compare worker behaviors in both settings.The results show that participants, across more than 90 trials (with 31 participants), had similar response times to received alarms in both settings (around 2.5 seconds) with a slightly faster reactions in real settings.
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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".