Problem
Research questions and friction points this paper is trying to address.
Software-Defined Vehicles
temporal uncertainty
end-to-end timing guarantees
probabilistic architectural models
representativeness
Predictable Modelling and Analysis of Software-defined Vehicle Implementations
🤖 AI Summary
研究通过结合概率设计时延分析与Kuksa实现监控，解决了软件定义车辆中因中间件通信导致的时间不确定性问题。
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Probabilistic Timing Analysis
Kuksa-based Implementation
Incremental Timing Verification
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