Driver Drowsiness

Driver drowsiness detection aims to prevent accidents by automatically identifying when drivers are becoming fatigued. Current research focuses on developing robust algorithms using various data sources, including electroencephalography (EEG) signals, heart rate variability (HRV), and video analysis of facial features, employing machine learning models like convolutional neural networks (CNNs), graph convolutional networks (GCNs), and vision transformers. These advancements leverage techniques such as multi-attention fusion and adversarial training to improve accuracy and reliability, even under challenging conditions like partial occlusions or low lighting. Successful implementation of these systems holds significant potential for enhancing road safety and reducing traffic accidents caused by driver fatigue.