A Comparative Study of Driver Drowsiness Detection and Fatigue Analysis using CNN, GAN, and Hardware Models

Driver drowsiness and fatigue are significant road safety risks, leading to numerous accidents globally. This research presents a comparative analysis of Convolutional Neural Network (CNN) and Generative Adversarial Network (GAN) approaches for real-time driver drowsiness detection. We developed both hardware models, integrating sensors with vehicle dashboards, and software models using deep learning architectures for feature extraction and analysis. Extensive datasets were used to train and evaluate the models under varying conditions. Results demonstrate both approaches’ effectiveness, with nuanced differences in detection accuracy, sensitivity, and computational efficiency. Insights into the strengths and limitations of each method guide the selection of suitable techniques for real-world applications, with recommendations for future enhancements including model optimization, ADAS integration, and deployment in autonomous vehicles.