Software-defined vehicle timing models accurately predict real-world performance
Predictable Modelling and Analysis of Software-defined Vehicle Implementations
Logic in Computer Science
Summary
Software-defined vehicles use software layers that can make timing predictions less certain. This paper shows a way to test if timing predictions based on models match what actually happens in real vehicle software. The authors built and monitored a vehicle system to compare predictions with real data and found the models still reliably predict timing even under different network loads. This helps verify vehicle software timing in a practical, step-by-step way.
What this means in practice
- •For automotive software engineers: Use the combined model and monitored framework to verify timing guarantees of vehicle software under varying communication loads before deployment.
- •For industrial embedded system developers: Adopt the timing comparison methodology to evaluate timing predictability of middleware-based systems beyond automotive applications.
Authors
Pavlo Tokariev, Yosri Ayari, Julien Deantoni
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.