Evaluation of Control Modalities in Highly Automated Vehicles: A Virtual Reality Simulation-Based Study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| 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) | 0.000 | 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 it