Driver Attention

Driver attention research focuses on understanding where and why drivers focus their attention while driving, aiming to improve the safety and efficiency of both human-driven and autonomous vehicles. Current research employs various deep learning models, including convolutional neural networks, transformers, and recurrent neural networks, often incorporating multi-modal data (e.g., camera images, driver gaze, physiological signals) to predict driver attention and integrate it into vehicle control systems or accident prediction models. This field is crucial for advancing autonomous driving technology by enabling vehicles to better understand the driver's intentions and anticipate potential hazards, as well as for developing advanced driver-assistance systems that enhance road safety.