Monitoring and Diagnosability of Perception Systems

TL;DR

This paper introduces a formal, graph-theoretic framework for runtime monitoring and fault detection in perception systems of autonomous robots, enhancing safety and reliability through mathematical modeling and topos theory.

Contribution

It develops a novel, formal approach for system-level perception monitoring, extending diagnosability concepts to heterogeneous modules and temporal analysis.

Findings

Able to detect faults at runtime in perception modules

Provides an upper bound on detectable faulty modules

Uses topos theory for diagnosability over time intervals

Abstract

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.