Monitoring and Diagnosability of Perception Systems

05/24/2020
by   Pasquale Antonante, et al.
0

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.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
05/22/2022

Monitoring of Perception Systems: Deterministic, Probabilistic, and Learning-based Fault Detection and Identification

This paper investigates runtime monitoring of perception systems. Percep...
research
08/17/2021

PerceMon: Online Monitoring for Perception Systems

Perception algorithms in autonomous vehicles are vital for the vehicle t...
research
08/01/2022

Safe Perception – A Hierarchical Monitor Approach

Our transportation world is rapidly transforming induced by an ever incr...
research
11/24/2021

Fault-Tolerant Perception for Automated Driving A Lightweight Monitoring Approach

While the most visible part of the safety verification process of automa...
research
12/15/2022

Runtime Monitoring for Out-of-Distribution Detection in Object Detection Neural Networks

Runtime monitoring provides a more realistic and applicable alternative ...
research
02/07/2022

Evaluation of Runtime Monitoring for UAV Emergency Landing

To certify UAV operations in populated areas, risk mitigation strategies...
research
05/11/2020

Online Monitoring for Neural Network Based Monocular Pedestrian Pose Estimation

Several autonomy pipelines now have core components that rely on deep le...

Please sign up or login with your details

Forgot password? Click here to reset