发表机构
Iowa State University（爱荷华州立大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对含多优先级要求与评估不确定性的安全关键控制问题，提出基于规则手册的风险感知最优控制方法，开发任意时刻过滤分支定界算法并通过合成基准与高速仿真验证有效性。
AI 中文摘要
我们研究涉及多个不同优先级要求及评估不确定性的安全关键控制问题。我们用风险感知规则手册（risk-aware rulebooks）表示这些要求，其中每个要求被赋予一个风险度量和可接受阈值，且要求间定义了优先级关系。每个要求会生成一个风险评估函数，将策略映射到违反该要求的风险。我们将基于规则手册的风险感知最优控制公式化为关于超额风险的词典序优化问题，并开发了一种任意时刻（anytime）过滤与分支定界算法，该算法可逐步收紧已验证的最优性间隙，同时在每个优先级层级刻画对应的策略集合。算法返回一个策略及这些间隙，这些间隙可界定该策略的次优性。我们证明，对于任何有限计算预算，这些间隙均有效；在额外假设下，随着计算预算增加，间隙收敛至零。我们在具有已知最优解的合成基准测试，以及采用基于CVaR的碰撞、后车制动、车距和舒适性规则的现实高速公路汇入仿真中评估了该算法。
英文摘要
We consider safety-critical control problems involving multiple requirements with different priorities and uncertainty in their evaluation. We represent these requirements using risk-aware rulebooks, where each requirement is assigned a risk measure and an acceptable threshold, and a priority relation is defined among the requirements. Each requirement induces a risk-evaluation function that maps a policy to the risk associated with its violation. We formulate risk-aware optimal control with rulebooks as a lexicographic optimization problem over excess risks and develop an anytime filtering and branch-and-bound algorithm that progressively tightens the certified optimality gap while characterizing the corresponding set of policies at each priority level. The algorithm returns a policy together with these gaps, which bound its suboptimality. We prove that these gaps are valid for any finite computational budget and, under additional assumptions, converge to zero as the computational budget increases. We evaluate the algorithm on a synthetic benchmark with a known optimum and a realistic highway-merging simulation with CVaR-based collision, rear-braking, headway, and comfort rules.
