Autonomous Driving Domain

Autonomous driving research aims to develop safe and reliable self-driving systems through advancements in perception, decision-making, and control. Current efforts focus on improving the robustness and generalizability of machine learning models, particularly through techniques like vision-language models, Decision Transformers, and Neural Radiance Fields, often leveraging large datasets and simulation environments to address challenges like the sim-to-real gap and distributional shift. This field is crucial for enhancing road safety, improving traffic efficiency, and advancing the broader field of robotics and AI through the development of novel algorithms and rigorous verification methods.