Autonomous Driving Method

Autonomous driving methods aim to create systems capable of safely navigating complex environments without human intervention. Current research heavily focuses on end-to-end approaches, often employing deep learning architectures like sparse query-centric models and generative models, to directly map sensor data to driving actions, incorporating elements like uncertainty modeling and reasoning capabilities from large language models. These advancements aim to improve safety, efficiency, and robustness, particularly in handling challenging scenarios and unforeseen events, ultimately contributing to the development of reliable and widely deployable autonomous vehicles.