Driving Behaviour Detection Using Smart Steering Wheel: Supervised and Unsupervised Classification
Bibliographic record
Abstract
Driving behaviours are the root cause of millions of road accidents every year. Aggressive and distracted driving are the two most important examples of driving misbehaviours. This paper proposes a real-time system to distinguish aggressive and distracted driving from safe driving in a five-second time window. The system monitors the driver's heart rate, hand position and force on the steering wheel, and the vehicle's inputs (steering wheel angle and gas/brake pedal position). Data were collected from three different driving scenarios by recruiting five participants using a driving simulator. The performance of various supervised classification methods including KNN, SVM, Neural Network, and Decision Tree was compared in which Gaussian SVM featured the highest accuracy at 95.4%. To address labelling challenges for supervised learning, we assessed the performance of unsupervised clustering methods, K-mean and Fuzzy C-mean (FCM), in the classification of collected data, where the FCM exhibited an accuracy of 81.16% in separating distracted and aggressive driving from safe driving. The proposed sensing and clustering method provides a suitable real-time assessment of the quality of driving while the fuzzy outputs can be used in designing different assertion levels and taking proper actions in assisted driving systems to minimize the risk of accidents.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 0.001 |
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".