Smart driving with AI: A review of CNN approaches to drowsiness detection

Drowsy driving is widespread and a significant cause of traffic accidents, and thus poses a serious threat to life and property around the globe. Therefore, real-time driver drowsiness detection has emerged as a primary study area, particularly due to the current advancements that incorporate artificial intelligence (AI) into automobiles. Convolutional Neural Networks (CNNs) have recently been very effective in handling image data and feature extraction for detecting drowsiness based on facial and eye movement patterns. This review paper focuses on the different CNN architectures and models that exist in the field of driver drowsiness detection and their strengths and limitations. Models like VGGNet, ResNet, and Inception V3 that are used in CNN are elaborated using pseudocode for an easy understanding of how they can be implemented practically. This paper also examines new trends in lightweight CNNs for edge computing as a solution to demands for real-time analytics in constrained environments such as vehicles. Moreover, important issues like data bias, model overfitting, and computational constraints are discussed. Additionally, future perspectives are provided to address these challenges, such as the integration of hybrid models and fusion of multimodal data. This review aims to provide a comprehensive understanding of CNN-based drowsiness detection and assist in developing safe and reliable automotive applications.