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.

通过引入一种建立数据和需求导向的感知合同精化算法 (DaRePC)，本文分析了两个使用多阶段异构的机器学习增强感知的 6 维和 12 维飞行控制系统，提供了可测试的合同以确定飞机是否可以安全降落在跑道上以及无人机是否能够安全通过一系列的门，同时也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，低地平线的太阳）。

完善感知合同: 基于视觉的安全自动着陆案例研究