Comparative Study: Physiological-based Driver Drowsiness Detection Utilizing Traditional and Hybrid Methods
Bibliographic record
Abstract
Driver drowsiness is identified as a major factor leading to traffic accidents and fatalities worldwide. To address this critical public concern, researchers are developing various driver-centered drowsiness detection systems. However, most of the studies in this field rely on either visual-based monitoring techniques or physiological-based indicators. This paper focuses on the latter and presents a comparative study aimed at identifying the most reliable and promising method for detecting the level of drowsiness using physiological signals. This study explores various techniques employing traditional machine learning methods or pre-trained deep neural networks. The former focus on generating features leveraging the temporal aspect of the signals using time series and frequency representation through Fast Fourier Transform, Discrete Cosine Transform, and Discrete Wavelet Transform. The latter type utilizes the spectrogram, which encapsulates both temporal and frequency information through visual representation, subsequently employed within a pre-trained Convolutional Neural Network model for feature extraction. The different approaches explored in this study, using the publicly available ULg DROZY dataset, have produced a multitude of results. However, the most promising approach achieved a top accuracy of 88%.
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How this classification was reachedexpand
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 | 0.000 | 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) | 0.002 | 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 teacher head, 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".