End to End Autonomous Driving

End-to-end autonomous driving aims to directly map raw sensor data to driving actions, bypassing the traditional modular pipeline of perception, prediction, and planning. Current research emphasizes improving efficiency and robustness through sparse scene representations, leveraging transformer networks and deep reinforcement learning, and incorporating multi-modal data fusion (e.g., camera, LiDAR, V2X). This approach promises safer and more efficient autonomous systems by streamlining the decision-making process and reducing reliance on expensive data annotation, ultimately advancing the field of autonomous vehicle technology.