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  • Strategic Resilience Evaluation of Neural Networks Within Autonomous VehicleSoftwareAnna Schmedding, Philip Schowitz, Xugui Zhou, Yiyang Lu, Lishan Yang, Homa Alemzadeh, and Evgenia Smirni
Title: Strategic Resilience Evaluation of Neural Networks Within Autonomous VehicleSoftwareAnna Schmedding, Philip Schowitz, Xugui Zhou, Yiyang Lu, Lishan Yang, Homa Alemzadeh, and Evgenia Smirni
Simulation-basedFaultInjection(FI)ishighlyrecommended by functional safety standards in the automotive and aerospace domains, in order to “support the argumentation of completeness and correctness of a system architectural design with respect to faults” (ISO 26262). We argue that a library of failure models facilitates this process. Such a library, firstly, supports completeness claims through, e.g., an extensive and systematic collection process. Secondly, we argue why failure model specifications should be executable—to be implemented as FI operators within a simulation framework—and parametrizable—to be relevant and accurate for different systems. Given the distributed nature of automo- tive and aerospace development processes, we moreover argue that a data-flow-based definition allows failure models to be applied to black- box components. Yet, existing sources for failure models provide frag- mented, ambiguous, incomplete, and redundant information, often meet- ing neither of the above requirements. We therefore introduce a library of 18 executable and parameterizable failure models collected with a sys- tematic literature survey focusing on automotive and aerospace Cyber- Physical Systems (CPS). To demonstrate the applicability to simulation- based FI, we implement and apply a selection of failure models to a real- world automotive CPS within a state-of-the-art simulation environment, and highlight their impact.  more » « less
Award ID(s):
2402942
PAR ID:
10592175
Author(s) / Creator(s):
; ; ; ; ; ;
Editor(s):
Cecarelli, Andrea; Trapp, Mario; Bondavalli, Andrea; Bitsch, Friedemann
Publisher / Repository:
Lecture Notes in Computer Science 14988, 43rd International Conference on Computer Safety, Reliability, and SecurityC, SAFECOMP 2024
Date Published:
ISSN:
0302-9743
ISBN:
978-3-031-68605-4
Page Range / eLocation ID:
33-50
Format(s):
Medium: X
Location:
Florence, Italy
Sponsoring Org:
National Science Foundation
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