Real-time Driver Drowsiness Detection System

Recent advancements in artificial intelligence (AI) and edge computing have revolutionized driver drowsiness detection (DDD) systems. This paper presents an enhanced DDD framework that integrates multi-modal data (facial, physiological, and vehicle-based) using hybrid AI models. The proposed system leverages Convolutional Neural Networks (CNNs) for facial feature analysis, Recurrent Neural Networks (RNNs) for temporal pattern recognition, and edge computing for real-time processing. A novel load-balancing algorithm optimizes resource allocation, ensuring scalability and efficiency. Experimental results demonstrate a 15% improvement in accuracy and reduced latency compared to existing systems.