上下文变化后复用运行证据：自动驾驶汽车安全的保守贝叶斯框架
Reusing Operational Evidence After Context Changes: A Conservative Bayesian Framework for Autonomous Vehicle Safety
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中文总结 AI 辅助
本文针对自动驾驶汽车在不同目标运行域（TOD）部署时的运行证据复用问题，提出保守贝叶斯推理（CBI）框架，实现旧TOD证据的保守透明转移，支持新TOD的可靠性主张。
中文摘要 AI 辅助
运行证据即无故障运行的证据，是对服役系统安全性或可靠性建立置信度的重要组成部分，但收集成本高昂。当运行上下文发生变化时，先前收集的运行证据的相关性变得不明确，例如在一个目标运行域（TOD）安全运行的自动驾驶汽车部署到不同但相关的TOD时就会出现该问题。现有实践常以全有或全无的方式处理此类证据：要么完全复用，要么丢弃。当有充分理由相信新上下文不劣于旧上下文但该信念本身不确定时，两种处理方式均不理想。本文研究如何结合两个TOD的证据，采用保守贝叶斯推理（CBI），通过对一组可接受先验的约束，将新上下文的证据与加权的旧上下文证据及部分先验知识相结合，从而得到关注量的保守后验界。数值示例展示了如何将来自旧TOD的现有证据以保守且透明的方式转移，以支持变化后TOD的可靠性主张。
英文摘要
Operational evidence, i.e., evidence of operation without failure is an important component of confidence in the safety or reliability of a system in service, but it is costly to collect. When the context of operation changes, the relevance of previously collected operational evidence becomes unclear. This problem arises, for example, when an autonomous vehicle that has operated safely in one operational environment (the Target Operational Domain, TOD) is deployed in a different but related TOD. Existing practice often treats such evidence in an all-or-nothing manner: either it is fully reused, or it is discarded. Neither position is satisfactory when there are good reasons to believe that the new context is no worse than the previous one, but that belief is itself uncertain. This paper studies how to make post-change reliability claims by combining evidence from two TODs using Conservative Bayesian Inference (CBI), which combines the evidence from the new context with a weighted amount of previous context and partial prior knowledge through constraints on a set of admissible priors. This yields conservative posterior bounds on quantities of interest. A numerical example illustrates how pre-existing evidence from a previous TOD can be transferred, conservatively and transparently, to support reliability claims in the changed TOD.
