Integration of Motion Prediction with End-to-end Latent RL for Self-Driving Vehicles

The field of self-driving vehicles (SDVs) is going viral among researchers from a broad spectrum of specialties. SDVs are expected to have profound impacts on the world once fully developed and deployed on roads. Hence, researchers are working assiduously together to accomplish this project. In this paper, we propose integrating motion prediction with sequential latent maximum entropy reinforcement learning, end-to-end, to train an agent to navigate autonomously in a simulated urban environment. The fusion of motion prediction for surrounding vehicles enhances traffic efficiency and safety. A novel network specialized in joint perception and motion prediction, named MotionNet, is selected in our paper to supply us with motion predictions. Our proposed system demonstrates that adding motion prediction enhances performance even further. Furthermore, our system relies merely on LIDAR sensor. CARLA simulator is used to conduct our experiments and extract outcomes.