AI RESEARCH
SafeDrive: Fine-Grained Safety Reasoning for End-to-End Driving in a Sparse World
arXiv CS.CV
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ArXi:2602.18887v2 Announce Type: replace The end-to-end (E2E) paradigm, which maps sensor inputs directly to driving decisions, has recently attracted significant attention due to its unified modeling capability and scalability. However, ensuring safety in this unified framework remains one of the most critical challenges. In this work, we propose SafeDrive, an E2E planning framework designed to perform explicit and interpretable safety reasoning through a trajectory-conditioned Sparse World Model.