Year: 2025
Type: article
Abstract: 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.
Authors: Monika S, Kiran Shelke, Atharv Sakhare, Dayanand Argade
Language: English
Subfield: Experimental and Cognitive Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: hybrid
OpenAlex ID: W4409617162