Detection of Driving Distractions and Their Impacts
Bibliographic record
Abstract
For decades, road crashes have caused many deaths and injuries and generally have had a severe social and economic impact on societies. According to studies, driver distraction has led to an increase in driving-related risks. In recent years, there have been more distracting factors that commonly affect drivers, highlighting the need for a resolution. Therefore, as technology is becoming more advanced, there is an opportunity to minimize these risks, for which driver distraction detection would be required. As there are a variety of distractions that might affect drivers and their performance, there are many studies focusing on this topic. To better understand the field of driver distraction detection, this paper has reviewed the existing studies in this field. For this purpose, different variables of the existing methodologies and experimental setups are identified and explained. Also, the results of these experiments and the impacts of different distraction factors on drivers’ physiological responses, visual signals, or their performances are categorized and described. Furthermore, this study discusses the factors of the existing methodologies and their results, along with pointing out the research gaps. The purpose of this study is to assist future research and investigation in this field, by creating a review that comprehensively covers different aspects of existing studies and discusses and assesses their methodologies and findings.
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".