Effects of Passenger and Cellular Phone Conversations on Driver Distraction
Bibliographic record
Abstract
The distracting effects of a simulated conversation with passengers and those of a conversation over a hands-free cellular phone were compared. The conversation was also analyzed to determine if passengers modulated their conversations as driving demands changed. Eighty participants were randomly assigned to one of three conditions: driving alone, driving with a passenger, and driving with a cellular phone. Drivers drove through residential and urban traffic environments in a fixed-based driving simulator in which a variety of events occurred, such as pedestrian activity, oncoming vehicles, and intersections. The results indicated that lane and speed maintenance were influenced by increased driving demands. Response times to a pedestrian incursion increased when the driver was driving and talking compared with those detected when the driver was not talking at all. Contrary to what some researchers have assumed, there was little practical evidence that passengers adjusted their conversations to changes in the traffic environment. The workload was rated higher when the driver was driving and talking and was also rated higher by drivers than by nondrivers. The discussion focuses on future research and implications for driver safety and training.
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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 | 0.012 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".