Evaluation of Control Modalities in Highly Automated Vehicles: A Virtual Reality Simulation-Based Study
Bibliographic record
Abstract
The integration of effective control modalities is paramount for enhancing user experience and safety in autonomous vehicles. This study investigates the performance and user experience of three control modalities i.e., voice, hand gesture, and physical button controls in high-level autonomous vehicles (Levels 4 and 5), under both distraction and non-distraction conditions. Our objective was to evaluate error rates, physiological responses, and subjective workload across these control modalities. The results revealed that distraction significantly increases error rates and perceived workload across all models. Voice control exhibited the lowest error rates without distraction but was most affected by it, whereas Hand Gesture control showed the highest error rates and workload in both scenarios. Physical Button control demonstrated moderate error rates and the least impact from distraction. Physiological data supported these findings, with significant increases in heart rate under distraction for all models, particularly in the voice control model. The NASA Task Load Index scores indicated higher workload under distraction, with hand gesture control being the most demanding. Our findings suggest that a combination of Physical Button and Voice control may offer the most effective solution, with recommendations for adaptive and multimodal interaction designs to mitigate distraction effects and enhance overall user satisfaction.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".