让端到端自动驾驶更安全：通过精细推理预测未来碰撞风险
SafeDrive: Fine-Grained Safety Reasoning for End-to-End Driving in a Sparse World
- 用轨迹条件化的稀疏世界模型构建动态交互表示
- 在NAVSIM上仅0.5%碰撞率，闭环测试得分87.5
- 适合关注自动驾驶安全验证与可解释性的研究者
端到端（E2E）驾驶框架将传感器输入直接映射为驾驶决策，具备统一建模与可扩展性优势，但安全保障仍是核心挑战。本文提出SafeDrive，一种基于轨迹条件化稀疏世界模型的E2E规划框架，实现显式且可解释的安全推理。系统包含两个互补网络：稀疏世界网络（SWNet）构建轨迹依赖的稀疏世界，模拟关键动态对象与道路实体的未来行为，生成以交互为中心的表征；精细推理网络（FRNet）评估特定代理的碰撞风险及对可行驶区域的时间依从性，精准识别未来各时间步的安全关键事件。SafeDrive在开环与闭环基准上均达领先性能：在NAVSIM上取得PDMS 91.6、EPDMS 87.5，12,146个场景中仅发生61次碰撞（0.5%）；在Bench2Drive上获得66.8%驾驶评分。
原文摘要 · Abstract (English)
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. SafeDrive comprises two complementary networks: the Sparse World Network (SWNet) and the Fine-grained Reasoning Network (FRNet). SWNet constructs trajectory-conditioned sparse worlds that simulate the future behaviors of critical dynamic agents and road entities, providing interaction-centric representations for downstream reasoning. FRNet then evaluates agent-specific collision risks and temporal adherence to drivable regions, enabling precise identification of safety-critical events across future timesteps. SafeDrive achieves state-of-the-art performance on both open-loop and closed-loop benchmarks. On NAVSIM, it records a PDMS of 91.6 and an EPDMS of 87.5, with only 61 collisions out of 12,146 scenarios (0.5%). On Bench2Drive, SafeDrive attains a 66.8% driving score.
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