发表机构
University of Southern California; Texas A&M University（南加州大学; 德克萨斯A&M大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对多智能体系统中随机不可控智能体导致的STL约束不可行问题，提出将可行性修复视为Pareto优化的MPC框架，提供违反率概率证书，并在自动驾驶场景验证其安全可行性。
AI 中文摘要
时序逻辑是一种用于推理系统随时间行为的形式化语言。特别是信号时序逻辑（STL），已被用于编码多智能体系统中控制综合的时空要求，通常假设智能体是合作的且其动力学已知。然而，现实世界的多智能体应用，如自动驾驶，通常涉及随机且不可控的智能体。近期工作探索了具有最坏情况或概率公式的鲁棒控制，但仍存在局限性，要么（1）认证STL约束的严格满足而不解决可行性恢复，要么（2）以自我为中心的目标放宽不可行的约束。在本文中，我们提出了一种模型预测控制（MPC）框架，将可行性修复视为Pareto优化问题，以明确表征智能体目标之间的权衡。我们进一步提供了关于STL违反率的概率证书，以在随机且不可控的智能体下正式量化不确定性。所提出的框架在两个自动驾驶场景中进行了评估。结果表明，该框架恢复了可行的控制，并展示了安全行为。
英文摘要
Temporal logic is a formal language for reasoning about system behaviors over time. Signal temporal logic (STL), in particular, has been used to encode spatio-temporal requirements for control synthesis in multi-agent systems, often under the assumption that agents are cooperative and their dynamics are known. However, real-world multi-agent applications, such as autonomous driving, typically involve stochastic and uncontrollable agents. Recent work explored robust control with worst-case or probabilistic formulations, but remains limited in that it either (1) certifies strict satisfaction of STL constraints without addressing feasibility recovery, or (2) relaxes infeasible constraints with ego-centric objectives. In this paper, we propose a model predictive control (MPC) framework that treats feasibility repair as a Pareto optimization problem to explicitly characterize tradeoffs among agent objectives. We further provide a probabilistic certificate on STL violation rate to formally quantify uncertainty under stochastic and uncontrollable agents. The proposed framework is evaluated on two autonomous driving scenarios. Results show that the framework recovers feasible control with demonstrated safe behaviors.
