知覚システムの実行時監視と故障診断可能性
Monitoring and Diagnosability of Perception Systems
マルチプロセッサの自己診断可能性理論を拡張し、異種出力や時間次元を扱うグラフ理論的アプローチで知覚システムの実行時故障検出と検出可能な故障モジュール数の上限計算を可能にした。さらにトポス理論で定式化し任意時間区間での診断可能性を示した。
著者: Pasquale Antonante, David I. Spivak, Luca Carlone
分類: cs.RO, cs.AI
原文アブストラクト
Perception is a critical component of high-integrity applications of robotics and autonomous systems, such as self-driving cars. In these applications, failure of perception systems may put human life at risk, and a broad adoption of these technologies relies on the development of methodologies to guarantee and monitor safe operation as well as detect and mitigate failures. Despite the paramount importance of perception systems, currently there is no formal approach for system-level monitoring. In this work, we propose a mathematical model for runtime monitoring and fault detection of perception systems. Towards this goal, we draw connections with the literature on self-diagnosability for multiprocessor systems, and generalize it to (i) account for modules with heterogeneous outputs, and (ii) add a temporal dimension to the problem, which is crucial to model realistic perception systems where modules interact over time. This contribution results in a graph-theoretic approach that, given a perception system, is able to detect faults at runtime and allows computing an upper-bound on the number of faulty modules that can be detected. Our second contribution is to show that the proposed monitoring approach can be elegantly described with the language of topos theory, which allows formulating diagnosability over arbitrary time intervals.
