Automatic Deduction of the Impact of Context Variability on System Safety Goals

Andreas Kreutz, Gereon Weiss, Mario Trapp

Publikation: Beitrag in Buch/Bericht/KonferenzbandKonferenzbeitragBegutachtung

Abstract

Autonomous systems, such as trains with a high grade of automation, need to function safely in their operational context. One hindrance to the development of such systems is the high degree of variability of this context: Different context variants can have a substantial impact on the safety goals the system must fulfill to function with sufficiently low residual risk.In this paper, we propose a method for modeling and reasoning about the context variability of an autonomous system and its impact on the system's safety. We build upon contextual goal models to model the refinement of safety goals and their dependence on the environment. By introducing an explicit model of the context variability to be expected, we transform the challenge of safety in variable environments to a satisfaction modulo theories problem. This allows us to find inconsistencies and check whether a concrete context variant would allow for safe operation of the system. We demonstrate our approach with a use case from the railway domain and show its applicability to an automatic train operation system in different contexts based on map data.

OriginalspracheEnglisch
TitelProceedings - 2024 19th European Dependable Computing Conference, EDCC 2024
Herausgeber (Verlag)Institute of Electrical and Electronics Engineers Inc.
Seiten1-8
Seitenumfang8
ISBN (elektronisch)9798350360684
DOIs
PublikationsstatusVeröffentlicht - 2024
Veranstaltung19th European Dependable Computing Conference, EDCC 2024 - Leuven, Belgien
Dauer: 8 Apr. 202411 Apr. 2024

Publikationsreihe

NameProceedings - 2024 19th European Dependable Computing Conference, EDCC 2024

Konferenz

Konferenz19th European Dependable Computing Conference, EDCC 2024
Land/GebietBelgien
OrtLeuven
Zeitraum8/04/2411/04/24

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