Developing Trajectory Planning with Behavioral Cloning and Proximal Policy Optimization for Path-Tracking and Static Obstacle Nudging

End-to-end approaches with Reinforcement Learning (RL) and Imitation Learning (IL) have gained increasing popularity in autonomous driving. However, they do not involve explicit reasoning like classic robotics workflow, nor planning with horizons, leading strategies implicit and myopic. In this paper, we introduce our trajectory planning method that uses Behavioral Cloning (BC) for path-tracking and Proximal Policy Optimization (PPO) bootstrapped by BC for static obstacle nudging. It outputs lateral offset values to adjust the given reference trajectory, and performs modified path for different controllers. Our experimental results show that the algorithm can do path-tracking that mimics the expert performance, and avoiding collision to fixed obstacles by trial and errors. This method makes a good attempt at planning with learning-based methods in trajectory planning problems of autonomous driving.