Comparison of Wrap Around Screens and HMDs on a Driver’s Response to an Unexpected Pedestrian Crossing Using Simulator Vehicle Parameters
Bibliographic record
Abstract
Driving simulators are typically used when analyzing hazardous and collision events, as dangerous drives can be performed in controlled environments without compromising driver safety. Most driving simulators use a form of wrap around screens to project the simulation as they provide a wide field of view for the user creating a more realistic experience. However, this visual modality is costly and not practical for smaller workspaces. Recent advancements in head mounted display (HMD) technology may make them a better alternative to wrap around screens, but studies have yet to compare the two visual modality effects on driver performance in hazardous scenarios. In this study, drivers completed two drive simulations, one using wrap around screens and the other using a commercially available HMD. Each simulation contained two different unexpected pedestrian crossings in which the perception-response time and brake movement time of the driver was assessed. Average vehicle speed and standard deviation of lateral position were also examined. Perception-response time was significantly longer for drivers when wearing the HMD than when using wrap around screens. There were also significant differences in vehicle speed during driver perception-response time and brake movement time between display modalities but standard deviation of lateral position only had significant differences during perception-response times. Further advancements in HMD technology are needed before they can provide an adequate alternative to wrap around screens when analyzing driver response scenarios.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.002 | 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".