Real-Time Driver Drowsiness Detection and Classification on Embedded Systems Using Machine Learning Algorithms
Bibliographic record
Abstract
Traffic accidents caused by driver drowsiness are a significant concern.An automatic, contactless device capable of early detection and identification of a driver's drowsy state could greatly enhance their safety.This study presents a real-time driver drowsiness detection and classification system implemented on Jetson Nano and Jetson TX2 embedded systems using machine learning algorithms, including Logistic Regression, Naive Bayes, K-Nearest Neighbors (KNN), Decision Tree, Random Forest, and Multi-Layer Perceptron (MLP).Feature extraction is performed on images obtained from video segments within the dataset, followed by a normalization process.The normalized features are classified using machine learning algorithms, and the results are reported.A 10-fold cross-validation model is employed during the experiment, and the grid search hyperparameter optimization (GSHPO) method is used to fine-tune the classifier algorithms' parameters for the proposed system.The MLP classifier outperformed the other classifiers, achieving an accuracy, F1score, and AUC (the area under the receiver operating characteristic curve (ROC)) of 0.91, 0.91, and 0.90, respectively.The developed system is implemented on Jetson Nano and Jetson TX2 embedded systems, and the frames per second (FPS) results are provided for comparison.The high accuracy of this hardware-based system in detecting drowsy driving, along with its portability for in-vehicle use, is a critical aspect of this work.
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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 | 0.001 | 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 | 0.001 |
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 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".