Factors Identification and Prediction for Mind Wandering Driving Using Machine Learning
Bibliographic record
Abstract
Traffic safety is affected by many complex factors. Mind wandering (MW) is a fatal cause affecting driving safety and is hard to be detected and prevented due to its uncertain and complex occurrence mechanism. The aim of this study was to propose a framework for analyzing and predicting MW based on readily available driving status data. The data used in this study are the single-trip information collected by the questionnaire, which includes drivers’ personal characteristics, contextual information in which MW occurs, and in-vehicle environmental factors. After investigating the extent of factors that influence MW, these chosen factors are used to forecast MW. Based on these results, we select factors reliable to be obtained in real life to forecast MW. To verify that the new factors explored are useful in improving the forecast accuracy, the compared analysis is conducted with the results found by our approach and the existing approaches. We compare results obtained by four machine-learning-enabled forecasting approaches on a real-life data set. The result shows that the factors found in this paper can significantly improve forecast accuracy. The confusion matrix, ROC curves, and AUC are conducted, and the performance of the gradient boosting decision tree algorithm is better than other forecast approaches. The importance rankings of most factors obtained by the Gradient Boosting Decision Tree and questionnaire are the same.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".