Adaptively Managing Reliability of Machine Learning Perception under Changing Operating Conditions

Aniket Salvi, Gereon Weiss, Mario Trapp

Publikation: Beitrag in Buch/Bericht/KonferenzbandKonferenzbeitragBegutachtung

3 Zitate (Scopus)

Abstract

Autonomous systems are deployed in various contexts, which makes the role of the surrounding environment and operational context increasingly vital, e.g., for autonomous driving. To account for these changing operating conditions, an autonomous system must adapt its behavior to maintain safe operation and a high level of autonomy. Machine Learning (ML) components are generally being introduced for perceiving an autonomous system's environment, but their reliability strongly depends on the actual operating conditions, which are hard to predict. Therefore, we propose a novel approach to learn the influence of the prevalent operating conditions and use this knowledge to optimize reliability of the perception through self-adaptation. Our proposed approach is evaluated in a perception case study for autonomous driving. We demonstrate that our approach is able to improve perception under varying operating conditions, in contrast to the state-of-the-art. Besides the advantage of interpretability, our results show the superior reliability of ML-based perception.

OriginalspracheEnglisch
TitelProceedings - 2023 IEEE/ACM 18th Symposium on Software Engineering for Adaptive and Self-Managing Systems, SEAMS 2023
Herausgeber (Verlag)IEEE Computer Society
Seiten79-85
Seitenumfang7
ISBN (elektronisch)9798350311921
DOIs
PublikationsstatusVeröffentlicht - 2023
Veranstaltung18th IEEE/ACM Symposium on Software Engineering for Adaptive and Self-Managing Systems, SEAMS 2023 - Melbourne, Australien
Dauer: 15 Mai 202316 Mai 2023

Publikationsreihe

NameICSE Workshop on Software Engineering for Adaptive and Self-Managing Systems
Band2023-May
ISSN (Print)2157-2305
ISSN (elektronisch)2156-7891

Konferenz

Konferenz18th IEEE/ACM Symposium on Software Engineering for Adaptive and Self-Managing Systems, SEAMS 2023
Land/GebietAustralien
OrtMelbourne
Zeitraum15/05/2316/05/23

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