How Common In-Car Distractions Affect Driving Performance in Simple and Complex Road Environments
Bibliographic record
Abstract
Distracted driving (driving while performing a secondary task) is the cause of many collisions. Although the research has stressed the deleterious effects of distraction, there may be situations where distraction improves driving performance. Boredom is associated with collision risk, and it is possible that some types of secondary task may combat boredom on simple monotonous drives. In this study, licensed drivers were tested in a driving simulator (a car body surrounded by screens) that simulated simple or complex roads. Road complexity was manipulated by increasing traffic, scenery, and the number of curves in the drive. Participants either drove (single task), or they drove while listening to an audiobook or having a hands-free cellular phone conversation. Driving performance was measured in terms of speed, standard deviation of speed, standard deviation of lateral position (SDLP), and hazard response times. Task condition and road complexity had no significant effect on driving speed or standard deviation of speed. There was a trend to greater SDLP on the simple drives, where there was little oncoming traffic, though this was only statistically significant in the Audiobook condition. However, there was also evidence that audiobooks could be beneficial. On simple roads, drivers listening to audiobooks had significantly faster hazard response times that those that were driving (single task) or driving while having a hands-free conversation, though this pattern of response was not evident on complex drives. These results suggest that audiobooks could play a role in helping drivers stay focused on monotonous drives.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".