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| Format: | Recurso digital |
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Zenodo
2026
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| Matèries: | |
| Accés en línia: | https://doi.org/10.5281/zenodo.20029486 |
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- <p>Road Guardian is an AI-based multimodal framework for real-time driver drowsiness detection and automated safety response. The system combines facial analysis — Eye Aspect Ratio (EAR), Mouth Aspect Ratio (MAR), and head pose estimation via MediaPipe Face Mesh — with Steering Wheel Angle (SWA) monitoring to classify driver state as Normal, Warning, or Critical using a 10-minute temporal sliding window. Upon confirming a Critical unresponsive state, it initiates automated speed reduction, safe pull-over, and emergency contact notification with live GPS coordinates. Built on a lightweight MobileNet CNN, the system runs at 15–30 FPS entirely on-device, projecting 99% visual detection accuracy and a 96% temporal F1-score.</p> <p><em>This work was conducted at Arab International University (AIU), Syria. The official website of the university is: </em><a href="https://www.aiu.edu.sy"><em><span>https://www.aiu.edu.sy</span></em></a></p>