Model-Based Analysis and Engineering of Automotive Architectures with EAST-ADL

Model-Based Analysis and Engineering of Automotive Architectures with EAST-ADL

Sara Tucci-Piergiovanni (CEA, LIST, 91191 Gif-sur-Yvette CEDEX , France), DeJiu Chen (KTH Royal Institute of Technology, Sweden), Chokri Mraidha (CEA, LIST, 91191 Gif-sur-Yvette CEDEX , France), Henrik Lönn (Volvo Technology, Sweden), Nidhal Mahmud (University of Hull, UK), Mark-Oliver Reiser (Technische Universität Berlin, Germany), Ramin Tavakoli Kolagari (Nuremberg Institute of Technology G. S. Ohm, Germany), Nataliya Yakymets (CEA, LIST, 91191 Gif-sur-Yvette CEDEX , France), Renato Librino (4S s.r.l., Italy), Sandra Torchiaro (Centro Ricerche Fiat, Italy) and Agnes Lanusse (CEA, LIST, 91191 Gif-sur-Yvette CEDEX , France)
Copyright: © 2014 |Pages: 41
DOI: 10.4018/978-1-4666-6194-3.ch010
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Modern cars have turned into complex high-technology products, subject to strict safety and timing requirements, in a short time span. This evolution has translated into development processes that are not as efficient, flexible, and agile as they could or should be. Model-based design offers many potential solutions to this problem. This chapter presents the main aspects and capabilities of a rich model-based design framework, founded on EAST-ADL, and developed during the MAENAD project. EAST-ADL is an architecture description language specific to the automotive domain and complemented by a methodology compliant with the ISO26262 standard. The language and the methodology set the stage for a high-level of automation and integration of advanced analyses and optimization capabilities to effectively improve development processes of modern cars.
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Commercial automobiles have become complex high-technology products in a relatively short time span. Different factors contribute to this complexity. One of them is the increasing number of vehicle functionalities supported by software, electronics and mechatronic technologies; a trend that does not seem to slow down. The involvement of carmakers in the development of these functionalities differs from one vehicle domain to the other (chassis, body, powertrain), ranging from black box integration to white-box developments. Another factor is the way in which car manufacturers have evolved from their historical mechanical and manufacturing background to the intricate organizations that develop the automobile products of today. The advent of the electrical vehicle makes this last two factors even more evident, not only because of the “untraditional” technologies that carmakers need to master, but also because the arrival of new stakeholders, actors and interests around the electrical vehicle mean that the traditional scope of the automobile has changed.

Generally, this evolution has translated into development processes that are not as efficient, flexible and agile as they could or should be (Chale, Gaudre & Tucci-Piergiovanni, 2012). The need to master these different complexity-inducing factors and improve the efficiency of product development, plus the arrival of the ISO 26262 standard (which besides from safety-related aspects, also raises issues concerning development processes of automotive systems, currently under-formalized) have motivated the adoption of model-based system engineering. Model-based system engineering advocates the use of models, conforming to a common semantic meta-model, all along the system development process. The meta-model specifies a common unambiguous semantics formalizing system engineering terminology and then providing a common language for system descriptions, i.e. models. Models, produced along the development process, provide system descriptions at different abstraction levels. Abstraction levels help human reasoning and analysis capabilities allowing system specifications to be refined and incrementally validated as long as the comprehension of the system increases. The meta-model approach is also attractive for system development as meta-models and their related models can be easily extended to support an open ended evolution of domain specific concepts.

But model-based system engineering is not only about meta-models, with their possibility to provide unambiguous system descriptions at different abstraction levels. Indeed, models, when formalized through a meta-model, provide the sufficient level of precision to be computer-interpreted. This feature allows providing a computer assisted system engineering process that formalizes and automates system design activities.

Thanks to these capabilities, the adoption of model-based design has several benefits including an improvement of quality, through a more rigorous and costless traceability between requirements, design, analysis and testing. While the benefits of model-based design are widely understood, there is no COTS solution today providing a full-fledged model-based environment for automotive systems. The first problem is that many commercial solutions use proprietary meta-models that scarcely fit automotive design needs. Moreover, ideally, the meta-model should be shared in the entire automotive domain, and then proprietary languages should be avoided opting instead for standard languages. UML extensions as SysML, could be an option, but SysML, per se, does not support many concepts of vital importance for the automotive domain, as for instance, concepts for safety analysis, timing analysis and variability. To support these concepts UML needs to be specialized through specific profiles. Even though some efforts have been spent in that direction in literature – e.g. for safety (Cancila, Terrier, Belmonte, Dubois, Espinoza, Gerard, & Cuccuru, 2009), for timing (OMG MARTE, 2011) – we did not reach the stage in which these efforts are unified and integrated in SyML.

Key Terms in this Chapter

Schedulability Analysis: Schedulability analysis for a real time system consists of checking whether all tasks can be finished within their deadlines.

Model Checking: model checking aka property checking refers to the following problem: Given a model of a system, exhaustively and automatically check whether this model meets a given specification. Typically, one has hardware or software systems in mind, whereas the specification contains safety requirements such as the absence of deadlocks and similar critical states that can cause the system to crash. Model checking is a technique for automatically verifying correctness properties of finite-state systems.

UML Profile: UML profile provides a generic extension mechanism to customizing UML models for particular domains and platforms. Extension mechanisms allow refining standard semantics in strictly additive manner, preventing them from contradicting standard UML semantics.

Model-Based Systems Engineering: Model-based systems engineering (MBSE) is the formalized application of modeling to support system requirements, design, analysis, verification and validation activities beginning in the conceptual design phase and continuing throughout development and later life cycle phases. A model usually offers different views in order to serve different purposes. A view is a representation of a system from the perspective of related concerns or issues.

Failure Mode and Effects Analysis: Failure mode and effects analysis (FMEA) is an inductive reasoning (forward logic) single point of failure analysis and is a core task in reliability engineering, safety engineering and quality engineering. A successful FMEA activity helps to identify potential failure modes based on experience with similar products and processes. FMEAs can be performed at the system, subsystem, assembly, subassembly or part level.

SysML: The OMG systems Modeling Language (OMG SysML™) is a general-purpose graphical modeling language for specifying, analyzing, designing complex systems. In particular, the language provides graphical representations with a semantic foundation for modeling system requirements, behavior, structure, and equations, which is used to integrate with other engineering analysis models.

UML: UML or Unified Modeling Language is a general-purpose modeling language in the field of software engineering, which is designed to provide a standard way to visualize the design of a system.

EAST-ADL: EAST-ADL is an Architecture Description Language (ADL) for automotive embedded systems, developed in several European research projects. It is designed to complement AUTOSAR with descriptions at higher level of abstractions. Aspects covered by EAST-ADL include vehicle features, functions, requirements, variability, software components, hardware components and communication. Currently, it is maintained by the EAST-ADL Association in cooperation with the European FP7 MAENAD project.

AUTOSAR: AUTOSAR (AUTomotive Open System ARchitecture) is an open and standardized automotive software architecture, jointly developed by automobile manufacturers, suppliers and tool developers. It is a partnership of automotive OEMs, suppliers and tool vendors whose objective is to create and establish open standards for automotive E/E (Electrics/Electronics) architectures that will provide a basic infrastructure to assist with developing vehicular software, user interfaces and management for all application domains.

Modeling and Analysis of Real Time and Embedded Systems Profile: Modeling and Analysis of Real Time and Embedded systems profile or MARTE profile in short is the OMG standard for modeling real-time and embedded applications with UML2.

Fault Tree Analysis: Fault tree analysis (FTA) is a top down, deductive failure analysis in which an undesired state of a system is analyzed using Boolean logic to combine a series of lower-level events. This analysis method is mainly used in the fields of safety engineering and reliability engineering to understand how systems can fail, to identify the best ways to reduce risk or to determine (or get a feeling for) event rates of a safety accident or a particular system level (functional) failure. FTA is used in the aerospace, nuclear power, chemical and processpharmaceutical, petrochemical and other high-hazard industries; but is also used in fields as diverse as risk factor identification relating to social service system failure.

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