Towards Mixed-Criticality Software Architectures for Centralized HPC Platforms in Software-Defined Vehicles: A Systematic Literature Review
June 06, 2025 Β· Declared Dead Β· π European Conference on Software Architecture
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Authors
Lucas Mauser, Eva Zimmermann, Pavel NedvΔdickΓ½, Tobias Eisenreich, Moritz WΓ€schle, Stefan Wagner
arXiv ID
2506.05822
Category
cs.SE: Software Engineering
Citations
0
Venue
European Conference on Software Architecture
Last Checked
4 months ago
Abstract
Centralized electrical/electronic architectures and High-Performance Computers (HPCs) are redefining automotive software development, challenging traditional microcontroller-based approaches. Ensuring real-time, safety, and scalability in software-defined vehicles necessitates reevaluating how mixed-criticality software is integrated into centralized architectures. While existing research on automotive SoftWare Architectures (SWAs) is relevant to the industry, it often lacks validation through systematic, empirical methods. To address this gap, we conduct a systematic literature review focusing on automotive mixed-criticality SWAs. Our goal is to provide practitioner-oriented guidelines that assist automotive software architects and developers design centralized, mixed-criticality SWAs based on a rigorous and transparent methodology. First, we set up a systematic review protocol grounded in established guidelines. Second, we apply this protocol to identify relevant studies. Third, we extract key functional domains, constraints, and enabling technologies that drive changes in automotive SWAs, thereby assessing the protocol's effectiveness. Additionally, we extract techniques, architectural patterns, and design practices for integrating mixed-criticality requirements into HPC-based SWAs, further demonstrating the protocol's applicability. Based on these insights, we propose an exemplary SWA for a microprocessor-based system-on-chip. In conclusion, this study provides a structured approach to explore and realize mixed-criticality software integration for next-generation automotive SWAs, offering valuable insights for industry and research applications.
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