Speed Planning

Speed planning for autonomous vehicles focuses on generating safe, smooth, and efficient velocity profiles for navigating dynamic environments, avoiding collisions, and optimizing travel time. Current research emphasizes methods like quadratic programming and deep reinforcement learning to achieve optimal speed profiles, often incorporating obstacle avoidance strategies and inter-vehicle interaction models. These advancements are crucial for the safe and efficient deployment of autonomous vehicles, impacting both the robotics and transportation sectors by improving traffic flow and reducing accident rates. The development of robust and adaptable speed planning algorithms is a key challenge in realizing fully autonomous driving systems.