Enhanced Identification of Driver Fatigue System Using Deep Learning

Abstract

Driver fatigue is a critical factor contributing to road accidents, emphasizing the need for robust fatigue detection systems. This research presents a novel approach to enhance the identification of driver fatigue using deep learning techniques. Leveraging state-of-the-art neural network architectures, our model integrates diverse data sources, including physiological signals and driving behavior patterns. The system's effectiveness is evaluated through comprehensive training and testing procedures, showcasing its potential to outperform existing methods. The study not only addresses the limitations of current fatigue detection systems but also offers insights into the integration of deep learning for advanced real-time monitoring. The findings pave the way for improved road safety measures and open avenues for the research on intelligent transportation systems in the future.