Non‐instinct detection of cellphone usage from lane‐keeping performance based on eXtreme gradient boosting and optimal sliding windows
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Driving distraction caused by cellphone usage has become a common safety threat. As distraction detection methods based on driver's position or eye movement may raise privacy issues, a promising way is to analyze the vehicle's lane‐keeping performance. This paper proposed a detection algorithm based on eXtreme gradient boosting (XGBoost), to develop a real‐time driving distraction detection based on lane‐keeping performance. The algorithm includes knowledge‐based volatility feature extraction and feature selection by recursive feature elimination (RFE). To obtain dynamic patterns of lane‐keeping performance affected by different types of cellphone usage, browsing a short message, browsing a long message, and answering a phone call, a driving simulator experiment was conducted on 28 drivers. Results showed that the proposed XGBoost‐RFE method is reliable and promising to predict phone usage with 80% accuracy. The results also evoke the fact that sliding window size, which is about 80% of subtask duration, can be appropriate for real‐time detection of multiple cellphone usages. For overlap percentages, 67% of sliding window size can balance the efficiency and continuity of data in adjacent sliding windows. The paper's potential application includes the design of a real‐time driving distraction detection system.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it