Autonomous Racing

Autonomous racing research focuses on developing algorithms enabling vehicles to navigate race tracks at high speeds and compete against opponents or achieve optimal lap times. Current research emphasizes robust perception using sensor fusion (e.g., LiDAR, cameras, radar), advanced control strategies incorporating model predictive control (MPC) and reinforcement learning (RL), and efficient trajectory planning methods often utilizing splines or neural networks. This field contributes significantly to the advancement of autonomous driving technologies by pushing the boundaries of perception, control, and decision-making in challenging, high-speed environments.