Autonomous Race Car

Autonomous race car research focuses on developing AI systems capable of controlling vehicles at high speeds and in dynamic, competitive environments, aiming to surpass human performance and improve safety. Current research emphasizes robust vehicle dynamic modeling, often using hybrid physics-informed neural networks and Kalman filtering to handle noisy sensor data, coupled with advanced planning algorithms like reinforcement learning and game-theoretic approaches (e.g., iLQGames) for trajectory optimization and overtaking maneuvers. This field significantly advances AI and control systems by pushing the boundaries of perception, planning, and control in highly demanding scenarios, with direct implications for the development of safer and more efficient autonomous vehicles in general.