ACGRNN: Attention Convolution Gated Recurrent Neural Network with Deep and Handcrafted features for Drowsiness Detection
Bibliographic record
Abstract
Maintaining road safety and reducing accidents brought on by drowsy or exhausted driving depend heavily on the ability to detect driver fatigue.To improve road safety, it is essential to look at how drivers identify yawns.Even though a number of studies have suggested deep learning-based approaches, there is room for improvement in the creation of more accurate and efficient drowsiness detection systems that take into account behavioral factors like eye and mouth movements.In order to reliably identify sleepiness in real time using physiological and visual signals, this study suggests a deep neural network design that uses the Attention Convolution Gated Recurrent Neural Network (ACGRNN).The RMSprop optimizer, which effectively manages non-stationary goals and stabilizes the training process by dynamically adjusting the learning rate, is used to optimize the suggested system.Models are taught and assessed by contrasting them with current techniques.According to the experimental results, the suggested ACGRNN model achieves an average drowsiness detection accuracy of 95.53%.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".