Advances in automated driving are creating new challenges for product development in the automotive industry and continuously driving up the cost of product verification and validation. Modern automated driving systems (ADS) must safely handle a considerable number of driving scenarios in compliance with the Safety of the Intended Functionality (SOTIF) standard. While model-based systems engineering (MBSE) has successfully proven itself in the automotive industry as an enabler for complex system and test design, common procedures are neither scenario-based nor do they consider SOTIF. It is yet to be shown, how MBSE approaches can meet these specific requirements of ADS development and, what advantages they can offer over non-model-based methods.In this paper, an extended variant of the established feature-driven MBSE procedure CUBE is presented that includes the analysis of use cases and scenarios. Use-case-specific logical scenarios and the corresponding expected behavior and system architecture are specified using SysML profile extensions. It is demonstrated, how specification model artifacts are used for identifying potentially hazardous scenarios and functional deficiencies and how SOTIF analysis results flow back into the specification process by means of the function “Multi-Story Car Park Chauffeur”. The SysML model is linked to a safety argumentation created using the Goal Structuring Notation to integrate the system specification and the evidence from the SOTIF analysis in a single procedure and toolchain, ensuring full traceability.

Systems engineering, in particular in the automotive domain, needs to cope with the massively increasing numbers of requirements that arise during the development process. The language in which requirements are written is mostly informal and highly individual. This hinders automated processing of requirements as well as the linking of requirements to models. Introducing formal requirement notations in existing projects leads to the challenge of translating masses of requirements and the necessity of training for requirements engineers. In this paper, we derive domain-specific language constructs helping us to avoid ambiguities in requirements and increase the level of formality. The main contribution is the adoption and evaluation of few-shot learning with large pretrained language models for the automated translation of informal requirements to structured languages such as a requirement DSL.

Model-driven systems engineering relies on software languages that support different stakeholders. These languages often operate in different technological spaces. Checking consistency, tracing, and change propagation of models developed by different stakeholders, thus demands methods to bridge the gaps between these spaces. Research on the integration of heterogeneous software languages often considers heterogeneity within specific technological spaces only. We outline a systematic method to translate grammars between the technological spaces of the MontiCore and Xtext language workbench (LWB) and report observations on general grammar translation challenges. We have realized this translation in an automated toolchain and present lessons learned along the way. This can significantly facilitate bridging different technological spaces and, thus, improve model-driven systems engineering