Can Virtual Reality Headsets be Used to Measure Accurately Drivers’ Anticipatory Behaviors?
Bibliographic record
Abstract
VR headsets are several orders of magnitude less expensive than driving simulators. Their use in research and clinical settings could explode were it shown that the results obtained with VR headsets were similar to those obtained with more standard driving simulators. Towards this end, the current study expands on a previous initial validation study of VR headsets. In particular, it has been shown in conventional driving simulation and on-road studies that middle-aged drivers glance longer at latent hazards than their younger counterparts. In this study the total time middle-aged drivers spend glancing at a latent hazard and the average duration of each glance were compared to these same times for younger drivers using a VR headset and fixed-based driving simulator. The results indicate that the middle-aged participants glanced longer than their younger counterparts on both platforms at latent hazards, as measured by the total glance duration but had no difference when measured by the average glance duration. Moreover, the magnitude of the difference between middle-aged and younger drivers was the same across the two platforms. These results are in line with previous simulator studies. There appears here a real opportunity to expand the powers of simulation using VR headsets, both for purposes of research and clinical practice.
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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 | 0.002 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".