发表机构
University of Wisconsin–Madison（威斯康星大学麦迪逊分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出一种基于条件风险百分位控制的场景生成方法，利用扩散模型实现对抗性场景的精确校准，在highD上达到98.75%的请求实现率。
AI 中文摘要
在仿真环境中生成具有适当对抗性的角案例场景，对于在部署前测试自动驾驶汽车（AV）软件栈的安全性能至关重要。现有的自动驾驶场景生成器可以强制执行特定的行为、对抗性或可行性条件，但它们对生成的场景相对于同一交通情境中合理未来的极端程度提供的控制有限。本研究将生成场景的对抗性表示为其在给定观测历史条件下未来风险条件分布中的百分位。这一视角支持对两个问题进行校准回答：生成的角案例场景有多“角”，以及如何微调其“角程度”。为此，我们提出历史条件风险百分位请求，并学习一个参考风险分布，将每个请求的百分位映射到物理风险目标。然后，我们使用带采样时间风险引导的百分位条件联合扩散模型生成多智能体未来，并采用基于参考的准则来评估百分位实现。实验使用自我车辆与其周围车辆之间的最小后侵入时间（PET）作为高D（highD）数据集上的风险替代指标。在主要评估集上，我们的方法在0.05百分位容差内实现了1440个请求中的1422个（98.75%），平均百分位误差为0.00673，PET目标误差为0.00991秒。由此产生的接口通过共同的风险尺度连接了情境相对风险规范、物理实现和评估。项目网站和生成的场景视频可在此https URL获取。
英文摘要
Generating corner-case scenarios with appropriate adversity in a simulation environment is critical for testing an autonomous vehicle (AV) software stack's safety performance before deployment. Existing autonomous-driving scenario generators can enforce specific behavior, adversity, or feasibility conditions, but they provide limited control over how extreme a generated scenario is relative to plausible futures in the same traffic context. This study represents the adversity of a generated scenario as its percentile in the conditional distribution of future risk given the observed history. This view supports calibrated answers to two questions: how "corner" a generated corner-case scenario is and how its "cornerness" can be fine-tuned. To this end, we formulate history-conditioned risk-percentile requests and learn a reference risk distribution that maps each requested percentile to a physical risk target. We then use a percentile-conditioned joint diffusion model with sampling-time risk guidance to generate multi-agent futures, together with a reference-based criterion for evaluating percentile realization. Experiments use the minimum post-encroachment time (PET) between the ego and its surrounding vehicles as the risk surrogate on highD. On the primary evaluation set, our method realizes 1,422 of 1,440 requests within a 0.05 percentile tolerance (98.75%), with mean percentile error 0.00673 and PET-target error 0.00991 seconds. The resulting interface connects context-relative risk specification, physical realization, and evaluation through a common risk scale. Project website and videos of generated scenarios are available at https://hhj233.github.io/CornerPercentile/.
Comments30 pages, 13 figures, 8 tables. Project page with videos: https://hhjj233.github.io/CornerPercentile/ Code: https://github.com/hhjj233/P-control-SG
