Comprehensive assessment of simulator vehicle parameters and upper body kinematics and muscle activation between two display modalities in a driving simulator
Bibliographic record
Abstract
Driving simulators in hazard response studies provide safer environments for participants than naturalistic driving studies. Many driving simulator visual cueing systems are comprised of a set of wrap around screens (WASs) that when increased in number and size can enhance the realism of the simulation, creating a driving experience closer to naturalistic driving. This, however, can be costly. Recent designs of head mounted display (HMD) technology are low-cost making them a potential alternative, however, their effect on hazard response results in comparison to WASs is untested. Driver responses were compared between WASs and an HMD during an unanticipated pedestrian crossing. Perception-response times were significantly greater with the HMD, highlighting the importance of understanding the effects display modality may have on results. In contrast, many of the physiological variables did not exhibit significant differences between the display modalities suggesting that HMD specifications may not have a large impact on physiological responses.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".