🤖 AI Summary
Centralized high-performance computing (HPC) platforms for software-defined vehicles require hybrid-criticality software architectures that simultaneously guarantee real-time performance, functional safety, and scalability—yet existing research lacks systematic empirical validation.
Method: This paper pioneers the application of a rigorous systematic literature review (SLR) methodology to this domain, establishing a reproducible and transparent review protocol. We conduct functional-domain modeling, constraint extraction, and integration-pattern identification to synthesize key architectural practices.
Contribution/Results: We propose a practice-oriented architectural guideline tailored for HPC platforms and design an implementable, microprocessor-based SoC-level hybrid-criticality software reference architecture. Our work bridges the methodological gap between theoretical frameworks and industrial deployment, providing empirically grounded foundations for standardizing automotive software development and offering industrially viable adaptation strategies.
📝 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.