细化感知契约：基于视觉的安全自动着陆案例研究
Refining Perception Contracts: Case Studies in Vision-based Safe Auto-landing
AI总结：
本文提出DaRePC算法，用于细化感知契约，以评估基于视觉的飞行控制系统的安全性，并在6维和12维的自动着陆和无人机导航案例中验证了其有效性。
摘要：
感知契约为评估使用机器学习进行感知的控制系统的安全性提供了一种方法。感知契约是用于测试机器学习组件的规范，并提供了一种证明端到端系统级安全要求的方法。基于契约的测试和保证的可行性早先在直线车道保持的背景下得以确立：这是一个具有相对简单动力学的三维系统。本文分析了两个6维和12维的飞行控制系统，这些系统使用多阶段、异构的、基于机器学习的感知。本文通过引入一种用于构建数据和需求引导的感知契约细化算法（DaRePC）来推进方法论。所得分析提供了可测试的契约，这些契约确立了飞机能够安全降落在跑道和无人机能够安全通过一系列门的状态和环境条件。它还可以发现可能违反基于视觉的控制系统安全性的条件（例如，低地平线阳光）。
英文摘要：
Perception contracts provide a method for evaluating safety of control systems that use machine learning for perception. A perception contract is a specification for testing the ML components, and it gives a method for proving end-to-end system-level safety requirements. The feasibility of contract-based testing and assurance was established earlier in the context of straight lane keeping: a 3-dimensional system with relatively simple dynamics. This paper presents the analysis of two 6 and 12-dimensional flight control systems that use multi-stage, heterogeneous, ML-enabled perception. The paper advances methodology by introducing an algorithm for constructing data and requirement guided refinement of perception contracts (DaRePC). The resulting analysis provides testable contracts which establish the state and environment conditions under which an aircraft can safety touchdown on the runway and a drone can safely pass through a sequence of gates. It can also discover conditions (e.g., low-horizon sun) that can possibly violate the safety of the vision-based control system.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
