End to End Driving

End-to-end driving aims to directly learn the mapping from sensor data (like camera images and LiDAR point clouds) to driving commands, bypassing the need for explicitly designed perception and planning modules. Current research emphasizes improving the robustness and safety of these systems, focusing on techniques like attention mechanisms to guide model focus, incorporating chain-of-thought reasoning for better interpretability, and adversarial training to handle unexpected scenarios. These advancements are crucial for building safer and more reliable autonomous vehicles, addressing limitations in existing modular approaches and ultimately impacting the development and deployment of self-driving technology.