Title and sub-title: Driver Distraction: A Review of the Literature Authors:
Bibliographic record
Abstract
This report provides a comprehensive review of the current research on driver distractions deriving from within the vehicle. The impact of technology (e.g., mobile phones and route guidance systems) and non technology-based distractions (e.g., eating, smoking and conversing with passengers) on driving performance is examined and the relative influence of these distractions on driving is discussed. Approximately one quarter of vehicle crashes in the United States are estimated to result from the driver being inattentive or distracted. Whilst the full extent to which distraction is a causal factor in vehicle crashes in Australia is not yet known, there is converging evidence that it likely to be a significant problem here. As more wireless communication, entertainment and driver assistance systems proliferate the vehicle market, the prevalence of distraction-related crashes here and overseas is expected to escalate. The various methods that have been employed to measure driver distraction are examined and those measurement techniques that appear most promising in being able to accurately measure invehicle distraction are identified. In the final section of the report, recommendations for research and for the management of driver distraction are provided as a first step in stimulating development of a national agenda for dealing with this issue.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.006 |
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".