Fatigue Driving Detection Based on Very Deep Convolutional Network with Continuous Learning Strategy
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Fatigue driving is an important factor leading to traffic accidents so driver fatigue detection is crucial for improving road safety. The prevailing learning-based fatigue driving detection methods have the defect of lacking continuous learning ability resulting in low accuracy in unlearned situations. In this paper, we propose a very deep convolutional network with a continuous learning (CL-VDCN) strategy to achieve fatigue driving detection. To enhance the network performance in unlearned situations, a new sampling strategy, a replay buffer-based weight updating strategy, and a dynamic learning rate are proposed to enable the convolutional neural network with continuous learning ability. Experiments on the YAWDD dataset show that the proposed model outperforms comparison methods, achieving a classification accuracy of 92.82% in the test. Additionally, the model maintains high detection accuracy and robustness under different lighting conditions, with a classification accuracy of 87.08% across various brightness levels. The experimental results demonstrate the CL-VDCN model’s superior performance in terms of detection accuracy, robustness, and continuous learning capabilities.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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 it