Driving Planning

Driving planning for autonomous vehicles focuses on generating safe and efficient trajectories, considering complex interactions with other road users and environmental factors. Current research emphasizes improving the realism and robustness of driving simulations through adversarial training and incorporating diverse data sources, including V2X communication and large language models, to handle long-tail scenarios. Key algorithmic approaches involve reinforcement learning, imitation learning, and probabilistic methods like Kalman filtering and diffusion models, often integrated within end-to-end frameworks. These advancements are crucial for validating autonomous driving systems and ensuring their safe and reliable deployment in real-world environments.