Driver Drowsiness Detection

Driver drowsiness detection aims to automatically identify when a driver is becoming drowsy, thereby preventing accidents caused by fatigue. Current research heavily utilizes deep learning, particularly convolutional neural networks (CNNs) and vision transformers, often incorporating techniques like multi-attention fusion to improve robustness against varying lighting and occlusion. These models analyze visual cues (facial features, eye movements) or physiological signals (EEG, PPG) to classify drowsiness states, with ensemble methods and federated learning approaches showing promise for enhanced accuracy and data privacy. The successful development of accurate and reliable drowsiness detection systems holds significant potential for improving road safety and informing the design of advanced driver-assistance systems.