Driver Observation

Driver observation, the automated analysis of driver behavior using various sensors, aims to improve vehicle safety and human-machine interaction in autonomous driving systems. Current research focuses on developing robust models, often employing deep learning architectures like Inflated 3D ConvNets, that can accurately classify driver actions and states from diverse data sources including video, physiological signals, and vehicle sensor data, while addressing challenges like transferring models from simulated to real-world environments and calibrating confidence estimates for reliable decision-making. This field is crucial for advancing autonomous driving technology by enabling safer and more intuitive interactions between humans and vehicles, and improving the overall reliability of autonomous systems.