Autonomous Driving Simulation

Autonomous driving simulation aims to create realistic virtual environments for testing and validating self-driving systems, addressing the limitations and risks of real-world testing. Current research focuses on enhancing simulation realism through advanced rendering techniques like Gaussian splatting and neural radiance fields (NeRFs), incorporating more accurate and diverse agent behaviors using models such as LLMs and reinforcement learning, and bridging the "sim-to-real" gap via domain adaptation and improved data acquisition strategies. These advancements are crucial for accelerating the development of safer and more reliable autonomous vehicles, providing a controlled setting for rigorous testing and evaluation of complex driving scenarios.