Enhancing Driver Distraction Recognition Using Generative Adversarial Networks
Bibliographic record
Abstract
Distracted driving is among the primary causes for serious car accidents. Among the leading cause of death among teenagers today are traffic accidents and major part of them are related to distracted driving. We propose here an end-to-end Convolutional Neural Network-based driver distraction recognition (DDR) system that can generalize to diverse driving conditions. The proposed method consists of two steps: developing generative models to produce images of different driving scenarios and developing a discriminative model for image classification. Unlike traditional methods based on image data-sets collected by simulation experiments, we collect a diverse data-set of drivers in different driving conditions and activity patterns from the Internet and train generative models for multiple driving scenarios. By sampling from these generative models, we augment the collected data-set with new training samples and train a Convolutional Neural Network for distraction recognition. We demonstrate that the generative models are able to generate images of drivers in different driving scenarios. With augmentative images, the DDR system achieves an improvement of 11.45% on image classification performance in a driving simulation environment. Moreover, we demonstrate how the trained DDR systems can be integrated within a driver monitoring system.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".