2024/04/18 by Hyunwoo Park, Park, Hyunwoo · 1 citation
Computer Science · Engineering · #Artificial intelligence #Autonomous Vehicle Technology and Safety #Computer science #Control (management) #Engineering #FOS: Computer and information sciences #Iterative learning control #Reinforcement #Reinforcement Learning in Robotics #Reinforcement learning #Robotics (cs.RO) #Structural engineering #Trajectory
paper · pdf · doi:10.48550/arxiv.2404.12079
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2024/04/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Traditional trajectory planning methods for autonomous vehicles have several limitations. For example, heuristic and explicit simple rules limit generalizability and hinder complex motions. These limitations can be addressed using reinforcement learning-based trajectory planning. However, reinforcement learning suffers from unstable learning, and existing reinforcement learning-based trajectory planning methods do not consider the uncertainties. Thus, this paper, proposes a reinforcement learning-based trajectory planning method for autonomous vehicles. The proposed method involves an iterative reward prediction approach that iteratively predicts expectations of future states. These predicted states are then used to forecast rewards and integrated into the learning process to enhance stability. Additionally, a method is proposed that utilizes uncertainty propagation to make the reinforcement learning agent aware of uncertainties. The proposed method was evaluated using the CARLA simulator. Compared to the baseline methods, the proposed method reduced the collision rate by 60.17 %, and increased the average reward by 30.82 times. A video of the proposed method is available at https://www.youtube.com/watch?v=PfDbaeLfcN4.