The effect of visually induced motion sickness on driving performance in a virtual reality simulator and the efficacy of airflow as a countermeasure
Bibliographic record
Abstract
Background: Virtual reality (VR) driving simulation technologies have a myriad of applications from entertainment to scientific and medical research. However, they also are known to cause visually induced motion sickness (VIMS), a special form of traditional motion sickness. Common side effects of VIMS include nausea and disorientation, suggesting VIMS can bias driving performance. Objectives: We (1) investigated how VIMS affects performance in a simulated driving task and (2) examined a potential treatment to reduce VIMS through in-vehicle ventilation. Method: Twenty-three participants were engaged in a driving task where they react to hazards, obey speed limits, and complete common driving maneuvers. Driving performance (Objective 1) was evaluated based on various common driving criteria and compared between high- and low VIMS groups. To study the effect of airflow on VIMS (Objective 2), for half of the participants the car vents were positioned to face the drivers head and torso having airflow directly contact the driver’s skin, creating the experimental group: airflow (direct, indirect). The level of VIMS was measured before and after the simulated drive. Results: We found no differences in VIMS severity between airflow groups, indicating both direct and indirect airflow were equally successful prophylaxes to VIMS. We found no differences in driving performance between participants who experienced high- and low VIMS, indicating driving performance was not influenced by VIMS. Conclusion: Due to their equal prophylactic effects, both direct and indirect airflow can be used as a low cost, effective means of reducing VIMS in VR driving simulators. Furthermore, as driving-performance was not affected by VIMS, driving-simulator results of participants who experience high levels of VIMS can be treated equal to participants who experience low levels of VIMS. As these results are preliminary given we were unable to reach our minimum sample size of 40 participants, more data collection is required in order to be able to make a concrete conclusion.
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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 | 0.001 | 0.003 |
| 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) | 0.001 | 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".