发表机构
Tsinghua University（清华大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对端到端自动驾驶缺乏显式风险感知的问题，提出数据驱动的风险场框架DRiF，通过相对风险排序监督连接安全先验与规划，在Bench2Drive上提升驾驶分数、成功率和碰撞指标。
AI 中文摘要
安全性是自动驾驶的基本要求，然而现有的端到端驾驶模型仍然缺乏显式的风险感知学习能力。现有的基于规则的风险模型提供了可解释的安全先验，但其绝对风险评分依赖于手工设计的函数、系数和阈值。基于学习的风险表示减少了部分手工设计，但其监督信号通常依赖于占用率衍生的标签或启发式成本值，这可能无法捕捉以自车为条件的规划风险。在本文中，我们提出了DRiF，一种数据驱动的风险场框架，用于更安全的端到端自动驾驶。DRiF学习一个共享的BEV特征，用于静态地图分割、动态风险预测和车辆规划。对于动态风险学习，DRiF将基于规则的安全先验转换为成对风险标签，并训练风险场以保持相对风险排序，而不是回归手工设计的绝对分数。在Bench2Drive上的实验表明，DRiF取得了具有竞争力的整体性能，在驾驶分数、成功率和碰撞相关指标上均有持续改进。这些结果确立了相对风险监督作为连接显式安全结构与端到端规划的有效方式。数据和代码将公开提供。
英文摘要
Safety is a fundamental requirement for autonomous driving, yet existing end-to-end driving models still lack explicit risk-aware learning capacities. Existing rule-based risk models provide interpretable safety priors, yet their absolute risk scores depend on handcrafted functions, coefficients, and thresholds. Learning-based risk representations reduce part of this manual design, but their supervision often relies on occupancy-derived labels or heuristic cost values, which may not capture ego-conditioned planning risk. In this paper, we propose DRiF, a data-driven risk-field framework for safer end-to-end autonomous driving. DRiF learns a shared BEV feature with static map segmentation, dynamic risk prediction, and vehicle planning. For dynamic risk learning, DRiF converts rule-based safety priors into pairwise risk labels, and trains the risk field to preserve relative risk ordering instead of regressing handcrafted absolute scores. Experiments on Bench2Drive show that DRiF achieves competitive overall performance, with consistent improvements in driving score, success rate, and collision-related metrics. These results establish relative risk supervision as an effective way to connect explicit safety structure with end-to-end planning. The data and code will be publicly available.
