SafeDrive：稀疏世界中的端到端驾驶细粒度安全推理
SafeDrive: Fine-Grained Safety Reasoning for End-to-End Driving in a Sparse World
提出轨迹条件化的稀疏世界模型，通过模拟关键动态智能体和道路实体的未来行为，为下游安全推理提供交互中心表示；这种显式、可解释的安全推理机制可迁移到其他需要预测交互风险的视觉决策任务中。
原文依据 trajectory-conditioned Sparse World Model
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论端到端（E2E）范式将传感器输入直接映射为驾驶决策，因其统一建模能力和可扩展性而备受关注。然而，确保这一统一框架中的安全性仍是最关键的挑战之一。本文提出 SafeDrive，一种端到端规划框架，旨在通过轨迹条件化的稀疏世界模型执行显式且可解释的安全推理。SafeDrive 包含两个互补网络：稀疏世界网络（SWNet）和细粒度推理网络（FRNet）。SWNet 构建轨迹条件化的稀疏世界，模拟关键动态智能体和道路实体的未来行为，为下游推理提供以交互为中心的表示。随后，FRNet 评估各智能体特定的碰撞风险以及对可行驶区域的时序遵循情况，从而精确识别未来时间步中的安全关键事件。SafeDrive 在开环和闭环基准上均达到最优性能。在 NAVSIM 上，其 PDMS 为 91.6，EPDMS 为 87.5，在 12,146 个场景中仅发生 61 次碰撞（0.5%）。在 Bench2Drive 上，SafeDrive 取得 66.8% 的驾驶得分。
英文原文摘要 1243 字符
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.