Publications

Engineering of today’s complex automotive systems relies on heterogeneous, model-based development toolchains to compute various performance measures to demonstrate product quality and support decision making.

Despite advances in System Modelling and simulation, their integration often hits a bottleneck due to cumbersome, expensive and error-prone process of manual transformation of information regarding verification criteria from authoritative System Models into domain specific simulation toolchains. To overcome these obstacles, we introduce a novel methodology that facilitates integration of System Models, built using System Modelling Language (SysML), with simulation, to enable evaluation of quantitative Key Performance Indicators (KPIs) at every system life cycle stage.

This is demonstrated in a five-step process using an automotive Adaptive Cruise Control (ACC) application. First, we develop a comprehensive SysML model to analyse the system in its intended context. Second, we extract the KPIs from system requirements written in natural language and formalize them in Signal Temporal Logic (STL) to define constraint parameters. Third, we capture the atomic propositions of STL KPIs to establish traceability between constraint parameters, system properties and interfaces. Fourth, we establish correspondence between the System Architecture and domain-specific models. Fifth, simulation capabilities are leveraged to evaluate temporal, quantitative KPIs, providing insight into the system performance in achieving the desired task.

This novel integration eliminates the need for manual transformation of information from System Models to simulation toolchains, reduces opportunity for error, and enhances scalability to streamline the development process of automotive systems using Model Based Systems Engineering (MBSE).

Verification, validation, and testing of complex cyber-physical systems, such as autonomous vehicles, have traditionally relied on document-intensive processes and physical prototypes. However, accelerated development timelines increasingly challenge the feasibility of these approaches. Model-based systems engineering (MBSE) offers a model-centric alternative that enables new forms of system analysis. 

This article presents a graph-based methodology for evaluating system modelling language system models using functional flow block diagrams and interface definitions. By transforming system functions into a functional dependence graph and applying community detection techniques, we derive three structural metrics: system complexity, system modularity, and system test effort.

The methodology is applied to three case studies, an academic autonomous mobile robot and two industrial automotive propulsion systems, to explore its potential utility. Across these examples, the graph-based approach yielded improvements in all three metrics compared to traditional subject-matter-expert-based partitioning.

The findings suggest that graph-based evaluation may support early-stage architectural reasoning and model refactoring in MBSE workflows, with further validation needed to confirm its broader applicability and practical impact.