知覚コントラクトの洗練：視覚ベース安全自動着陸のケーススタディ
Refining Perception Contracts: Case Studies in Vision-based Safe Auto-landing
機械学習を用いた知覚に基づく飛行制御システムの安全性を保証するため、データと要件に導かれた知覚コントラクト洗練アルゴリズム（DaRePC）を提案し、航空機の安全着陸とドローンのゲート通過の条件を明らかにした。
著者: Yangge Li, Benjamin C Yang, Yixuan Jia, Daniel Zhuang, Sayan Mitra
分類: cs.RO, cs.CV
原文アブストラクト
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.
