Frenet Frame Based Local Motion Planning in Racing Environment

Local Trajectory Planning plays a pivotal role in reducing lap times during a race. It must take into account not only obstacles but also the hardware limitations of the vehicle and the performance of the controller. Moreover, in a high-speed racing competition like F1, surrounding vehicles exhibit highly dynamic movements, necessitating motion prediction for safety. However, existing studies have demonstrated good performance in environments with static objects or when avoiding racing vehicles in straight-line sections, but accidents occur in environments with large numbers of vehicles and high curvature. To address these issues, this paper proposes an algorithm that ensures stability even in complex environments by considering the uncertainty of motion prediction for multiple vehicles. By considering the uncertainty of vehicle motion, we design the cost and generate an optimized path based on sampling. The CarMaker simulator is utilized to verify the proposed algorithm.