Driver Distraction Recognition Based on Smartphone Sensor Data
Bibliographic record
Abstract
Driver distraction is one of the leading causes of vehicle accidents and injury. Automated systems for identifying driver distraction are of great interest for improving road safety. This study develops a smartphone sensor based driver distraction system using an ensemble learning method. After data collection, linear velocity data is first linearly interpolated. Then, 3-axial acceleration and 3-axial gyro signals are filtered for reducing noise. Next, a sliding window is applied to IMU and GPS data collected by the smartphone for feature extraction, where temporal features are calculated. Ensemble learning of four standard classifiers is used to recognize distraction events: K-Nearest Neighbor, Logistic Regression, Gaussian Naive Bayes, Random Forest. To evaluate the proposed approach, 24 drivers were recruited to participate in a user study, driving on a route consisting of suburban and highway driving. Driver cognitive distraction was induced by asking the driver questions while driving. The experimental results show that the best weighted F1-score of our proposed system is 87% with all smartphone sensor signals.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".