Communication Dans Un Congrès Année : 2025

Driver Drowsiness Detection Using CNN

Résumé

The condition and emotional state of a driver can be evaluated using advanced machine learning algorithms, including neural networks, contributing to enhanced road safety. Leveraging artificial intelligence, these systems can autonomously learn and adapt without explicit programming. Driving health and alertness can be assessed through bioindicators, driving behaviors, and facial expressions. This paper presents a comprehensive review of recent advancements in driver drowsiness detection and alert systems, highlighting the application of machine learning techniques such as YOLO, CNN, and OpenCV.

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Dates et versions

hal-05516097 , version 1 (18-02-2026)

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Maya Ghezzawi, Pio Jighalian, Sarah Khalil, Gaby Abou Haidar, Michel Owayjan, et al.. Driver Drowsiness Detection Using CNN. 2025 Sixth International Conference on Advances in Computational Tools for Engineering Applications (ACTEA), Sep 2025, Zouk Mosbeh, Lebanon. pp.1-6, ⟨10.1109/ACTEA66485.2025.11189920⟩. ⟨hal-05516097⟩
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