Vehicle Motion Planning

Vehicle motion planning focuses on enabling autonomous vehicles to safely and efficiently navigate complex environments by generating optimal trajectories. Current research emphasizes robust and adaptable planning algorithms, including model predictive control (MPC), deep reinforcement learning (DRL), and large language models (LLMs), often integrated with advanced perception systems like lidar and cameras to handle uncertainty and diverse scenarios. These advancements are crucial for improving the safety and reliability of autonomous driving systems, impacting both the development of safer vehicles and the broader scientific understanding of intelligent control systems.