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A Context-Aware, Confidence-Disclosing and Fail-Operational Dynamic Risk Assessment Architecture

 
: Feth, Patrik; Adler, Rasmus; Schneider, Daniel

:
Preprint urn:nbn:de:0011-n-5214383 (332 KByte PDF)
MD5 Fingerprint: 6e1a457567d03ced8b071fdcf1a904e7
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Created on: 8.3.2019


Institute of Electrical and Electronics Engineers -IEEE-:
14th European Dependable Computing Conference, EDCC 2018 : Iaşi, Romania, 10-14 September 2018
Piscataway, NJ: IEEE, 2018
ISBN: 978-1-5386-8060-5
ISBN: 978-1-5386-8061-2
pp.190-194
European Dependable Computing Conference (EDCC) <14, 2018, Iasi>
European Commission EC
H2020-ECSEL-2017-2 - RIA; 783119; SECREDAS
Cyber Security for Cross Domain Reliable Dependable Automated Systems
English
Conference Paper, Electronic Publication
Fraunhofer IESE ()
risk management; vehicle dynamic; measurement; safety; computer architecture; resilience

Abstract
Future automotive systems will be highly automated and they will cooperate to optimize important system qualities and performance. Established safety assurance approaches and standards have been designed with manually controlled stand-alone systems in mind and are thus not fit to ensure safety of this next generation of systems. We argue that, given frequent dynamic changes and unknown contexts, systems need to be enabled to dynamically assess and manage their risks. In doing so, systems become resilient from a safety perspective, i.e. they are able to maintain a state of acceptable risk even when facing changes. This work presents a Dynamic Risk Assessment architecture that implements the concepts of context-awareness, confidence-disclosure and fail-operational. In particular, we demonstrate the utilization of these concepts for the calculation of automotive collision risk metrics, which are at the heart of our architecture.

: http://publica.fraunhofer.de/documents/N-521438.html