Providing Views of the Driving Scene to Drivers’ Conversation Partners Mitigates Cell-Phone-Related Distraction
Bibliographic record
Abstract
Cell-phone use impairs driving safety and performance. This impairment may stem from the remote partner's lack of awareness about the driving situation. In this study, pairs of participants completed a driving simulator task while conversing naturally in the car and while talking on a hands-free cell phone. In a third condition, the driver drove while the remote conversation partner could see video of both the road ahead and the driver's face. We tested the extent to which this additional visual information diminished the negative effects of cell-phone distraction and increased situational awareness. Collision rates for unexpected merging events were high when participants drove in a cell-phone condition but were reduced when they were in a videophone condition, reaching a level equal to that observed when they drove with an in-car passenger or drove alone. Drivers and their partners made shorter utterances and made longer, more frequent traffic references when they spoke in the videophone rather than the cell-phone condition. Providing a view of the driving scene allows remote partners to help drivers by modulating their conversation and referring to traffic more often.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".