Non‐instinct detection of cellphone usage from lane‐keeping performance based on eXtreme gradient boosting and optimal sliding windows
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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 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".