软件定义车辆实现的可预测建模与分析
Predictable Modelling and Analysis of Software-defined Vehicle Implementations
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中文总结 AI 辅助
本文提出结合概率时序分析与Kuksa实现的实验框架，验证SDV架构抽象在不同中间件负载下的代表性，并支持增量时序验证。
中文摘要 AI 辅助
软件定义车辆（SDVs）依赖基于中间件的通信和硬件抽象机制，这些机制引入了影响端到端时序保证的时间不确定性。先前的工作提出了用于早期时序分析的概率架构模型，但这些抽象相对于SDV实现是否具有代表性仍不清楚。本文提出了一个实验框架，将概率设计时时序分析与受监控的基于Kuksa的实现相结合。相同的反应时间分析同时应用于仿真和实现轨迹，从而能够直接比较预测的时序行为与观察到的时序行为。我们还引入了一种比较方法，将保守覆盖与时序分布的预测保真度区分开来。结果表明，所提出的抽象在不同中间件负载条件下仍保持代表性，同时保留保守的时序保证，支持SDV平台的增量时序验证方法。
英文摘要
Software-Defined Vehicles (SDVs) rely on middleware-based communication and hardware abstraction mechanisms that introduce temporal uncertainty affecting end-to-end timing guarantees. Previous work proposed probabilistic architectural models for early timing analysis, but the representativeness of these abstractions with respect to SDV implementations remained unclear. This paper presents an experimental framework combining probabilistic design-time timing analysis with a monitored Kuksa-based implementation. The same reaction-time analysis is applied both to simulation and implementation traces, enabling direct comparison between predicted and observed timing behaviour. We additionally introduce a comparison methodology separating conservative coverage from predictive fidelity of timing distributions. The results show that the proposed abstractions remain representative under different middleware load conditions while preserving conservative timing guarantees, supporting incremental timing verification approaches for SDV platforms.
发表机构
- Inria（法国国家信息与自动化研究所）
机构由 AI 辅助整理，请以论文原文为准。
