MPM4CPS

Currently, most existing approaches for the design of Automated Driving Systems (ADS) scenarios focus on the description at one particular abstraction level — typically the most detailed one. This practice often removes information at higher levels, such that this data has to be re-synthesized if needed. As the abstraction granularity should be adapted to the task at hand, however, engineers currently have the choice between re-calculating the needed data or operating on the wrong level of abstraction. For instance, the search in a scenario database for a driving scenario with a map of a given road-shape should abstract over the lane markings, adjacent vegetation, or weather situation. Often though, the general road shape has to be synthesized (e.g. interpolated) from the precise GPS information of road boundaries. This paper outlines our vision for multi-level ADS scenario models that facilitate scenario search, generation, and design. Our concept is based on the common modelling philosophy to interact with scenarios at the most appropriate abstraction level. We identify different abstraction levels of ADS scenarios and suggest a template abstraction hierarchy. Our vision enables seamless traversal to such a most suitable granularity level for any given scenario, search and modelling task. We envision that this approach to ADS scenario modelling will have a lasting impact on the way we store, search, design, and generate ADS scenarios, allowing for a more strategic verification of autonomous vehicles in the long run.

Hybrid systems modelling remains a very popular topic within the modelling and simulation community. Its expressiveness allows for the definition of highly complex systems that merge discrete-state-based transitions systems with continuous value-evolutions for variables, so that cyber-physical systems can be modelled in all their intricacies. This expressive power, however, comes at a downside of complex models and undecidable verification problems even for small systems. In this chapter we present CREST, a novel modelling language for the definition of hybrid systems. CREST merges features from various formalisms and languages such as hybrid automata, dataflow programming and internal domain-specific modelling language (DSML) designs to create simple yet powerful language for the modelling of resource flows within small-scale CPS such as automated gardening applications and smart homes. The language provides an easy to learn graphical interface and is supported by a Python-based tool implementation that allows for the efficient modelling, simulation and verification of CPS models.

This chapter presents the ontology for Multi-Paradigm Modelling (MPM) specified using the Web Ontology Language (OWL) as introduced in Chapter 2. A thorough state of the art on MPM’s core notions including multi-formalism and model management approaches, languages and tools is presented. In particular, model management approaches have been characterized according to their modularity and incremental execution properties as required to scale for the large complex CPSs we face today. Based on this state of the art, an outline of the MPM ontology is developed by introducing its main classes and properties. The validation of the ontology is presented by showing how it can adequately model the two case studies briefly introduced in Chapter 2.

This chapter presents the Multi-Paradigm Modelling for Cyber-Physical Systems (MPM4CPS) ontology. This ontology integrates the Shared, MPM and CPS ontologies respectively introduced in Chapters 2, 3 and 4. It includes cross-cutting notions such as viewpoints, model-based development processes and modelling paradigms that together relate the formalisms and workflows (and their paradigms) to the part of CPSs developed with these formalisms. A brief state of the art on these notions is first presented, on which the MPM4CPS ontology builds. An overview of the ontology is then developed by introducing its main classes and properties. The validation of the ontology is finally presented by showing how it can adequately model the two case studies briefly introduced in Chapter 2. The chapter also discusses perspectives and future work on this integrated ontological framework, which can serve as a basis to develop model management solutions to relate and combine modelling languages and tools, in order to better develop cyber-physical systems with appropriate formalisms and workflows.

The study of concurrent and parallel systems has been a challenging research domain within cyber-physical systems community. This chapter provides a pragmatic introduction to the creation and analysis of such system models using the popular Petri nets formalism. Petri nets is a formalism that convinces through its simplicity and applicability. We offer an overview of the most important Petri nets concepts, analysis techniques and model checking approaches. Finally, we show the use of so-called High-level Petri nets for the representation of complex data structures and functionality and present a novel research approach that allows the use of Petri nets inside Functional Mock-up Units and cyber-physical system models.