Hier finden Sie wissenschaftliche Publikationen aus den Fraunhofer-Instituten.

System Health Indicators in Mixed Criticality E/E Systems in Automated Driving Context

: Dollinger, Friederike; Asmus, Rinat; Dreiser, Marc

Postprint urn:nbn:de:0011-n-6059006 (274 KByte PDF)
MD5 Fingerprint: a13205c84eb3e9c77ce2b9e6f92473fa
The original publication is available at
Created on: 4.11.2020

Muccini, Henry (ed.):
Software Architecture. 14th European Conference, ECSA 2020. Proceedings : Tracks and Workshops, L’Aquila, Italy, September 14-18, 2020
Cham: Springer Nature, 2020 (Communications in computer and information science 1269)
ISBN: 978-3-030-59154-0 (Print)
ISBN: 978-3-030-59155-7 (Online)
European Conference on Software Architecture (ECSA) <14, 2020, Online>
International Workshop on Automotive System/Software Architecture (WASA) <6, 2020, Online>
Bayerisches Staatsministerium für Wirtschaft, Landesentwicklung und Energie StMWi
Leistungszentrum Sichere intelligente Systeme
Conference Paper, Electronic Publication
Fraunhofer IKS ()
saftey; automated driving system; health indicator

One problem standing in the way of fully automated vehicles is the question of how to ensure vehicle safety and the safety of all traffic participants. Standards like ISO 26262 and ISO/PAS 21448 tackle those issues from different viewpoints by defining safety measures and mechanisms. While ISO 26262 focuses on safety hazards arising from malfunctioning of E/E systems, ISO/PAS 21448 stresses hazards due to technological limitations. However, it is an open challenge how system-wide safety can be monitored and validated at run-time. To complement those safety specifications we propose a system-wide run-time safety analysis. Our System Health Management concept is based on so-called Health Indicators (HIs) to propagate knowledge about detected errors and trigger appropriate error reactions. We analyze probable information sources to define meaningful HIs in automated driving context and investigate influence factors, of both ISO 26262 and ISO/PAS 21448. We apply our approach to a case study demonstrating its applicability in an automated driving scenario.