  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.
