Driving Context

Driving context research aims to improve autonomous vehicle (AV) safety and performance by incorporating a richer understanding of the surrounding environment and driver behavior. Current research focuses on integrating diverse data sources (e.g., camera images, GPS, driver gaze, EEG) into models that predict driver actions, reconstruct driving scenes, and reason about complex scenarios, often employing deep learning architectures like convolutional neural networks, recurrent neural networks, and large language models. This work is crucial for enhancing AV decision-making, improving human-AV interaction, and ultimately leading to safer and more reliable autonomous systems.