Deep Learning Models for Vision-Based Driver Drowsiness Detection: A Comparative Review

Drowsy driving is a serious road safety problem which causes accidents, injuries, and deaths every year. To help prevent this, it is important to create real-time systems that can detect driver fatigue without disturbing the driver. This paper reviews eight recent studies that use deep learning to detect drowsiness from driver images or video. The methods use traditional Convolutional Neural Networks (CNNs) for analyzing images, lightweight models which can run faster, and advanced models like LSTM and GCNs that track how a driver’s behavior changes over time. It also looks closely at transformer-based models, like Vision Transformer (ViT) and Swin Transformer, which are the latest developments. The models were tested on different datasets, like the MRL Eye Dataset to more realistic driving datasets like NTHU-DDD and SUST-DDD. Results show that transformer and GCN-based models reach the highest accuracy, sometimes it goes over 99.00% in controlled tests, but their performance drops when used in real driving conditions. This review shows three main challenges for future work, one is making models more reliable, second adapting them to real-world driving, and third reducing their computational cost for easy deployment. Overall, this paper summarizes current progress and points out some challenges, and suggests directions for building practical and effective drowsiness detection systems.