Design and Assessment of Reinforcement Learning Algorithms for End-to-End Autonomous Driving Learning

End-to-end autonomous driving learning refers to the process of mapping the original sensor data (such as camera images, radar signals, etc.) directly to driving decisions or control instructions, without the need to manually design complex feature extraction and rule making. Reinforcement learning (RL) shows great potential in this field because of its powerful decision-making optimization ability. The goal of this study is to design an intelligent and adaptable end-to-end autopilot algorithm by combining the advantages of RL and Transformer architecture. In this study, CARLA autopilot simulator is used as the experimental platform to simulate a variety of complex road environments and traffic scenes. By comparing with several typical Transformer-based end-to-end algorithms, the performance of RL algorithm in autonomous driving task is analyzed. The results show that RL algorithm is superior to other comparison algorithms in all assessment indexes, especially in dealing with emergencies and congested road sections. This discovery verifies the application value of RL in the field of autonomous driving, and provides strong evidence for its popularization in practical scenes.