Driver State

Driver state research focuses on understanding and predicting a driver's cognitive, physical, and behavioral state during driving, aiming to improve vehicle safety and the human-machine interaction in automated driving systems. Current research emphasizes the development of accurate driver state models using machine learning techniques, such as deep learning networks (including LSTMs and attention mechanisms) and clustering methods, often incorporating multimodal data from cameras, radar, and physiological sensors. This field is crucial for advancing the safety and acceptance of automated vehicles, informing the design of driver assistance systems, and contributing to a deeper understanding of human behavior in complex driving scenarios.