The Effects of Voice Technology on Test Track Driving Performance: Implications for Driver Distraction
Bibliographic record
Abstract
Twenty-one subjects completed two sets of (8) laps around a 7.5-mile test track during two 4-hour sessions. They drove an instrumented vehicle while performing a combination of car following, peripheral target detection, and secondary (in-vehicle) tasks of varying complexity. Subjects performed one set of laps with each of two interfaces, voice-based and visual/manual. Secondary tasks comprised three categories including baseline tasks (radio tuning, phone dialing), simple tasks (message retrieval plus voice memo creation), and complex tasks (simple task components plus phone dialing and information retrieval from automated phone systems). Measures of driving performance, target-detection, secondary task performance and eye movements were recorded. Analyses were conducted to determine whether the voice-based interface reduced the relative distraction potential for secondary tasks of varying complexity. Generally, differences between tasks were stronger than differences between interface conditions. Measures of car-following performance, target detection, and secondary task performance revealed differences attributable to task complexity. Differences between the two interfaces were observed on peripheral target detection measures and on several driving performance measures. Overall, the benefits of using the voice-based interface were not large enough to appreciably reduce the distraction potential associated with performing the secondary tasks in the car-following scenario.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".