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A Model-Based Approach to Dynamic Self-assessment for Automated Performance and Safety Awareness of Cyber-Physical Systems

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10437)

Abstract

Modern automotive vehicles represent one category of CPS (Cyber-Physical Systems) that are inherently time- and safety-critical. To justify the actions for quality-of-service adaptation and safety assurance, it is fundamental to perceive the uncertainties of system components in operation, which are caused by emergent properties, design or operation anomalies. From an industrial point of view, a further challenge is related to the usages of generic purpose COTS (Commercial-Off-The-Shelf) components, which are separately developed and evolved, often not sufficiently verified and validated for specific automotive contexts. While introducing additional uncertainties in regard to the overall system performance and safety, the adoption of COTS components constitutes a necessary means for effective product evolution and innovation. Accordingly, we propose in this paper a novel approach that aims to enable advanced operation monitoring and self-assessment in regard to operational uncertainties and thereby automated performance and safety awareness. The emphasis is on the integration of several modeling technologies, including the domain-specific modeling framework EAST-ADL, the A-G contract theory and Hidden Markov Model (HMM). In particular, we also present some initial concepts in regard to the usage performance and safety awareness for quality-of-service adaptation and dynamic risk mitigation.

Keywords

Cyber-physical systems Commercial-Off-The-Shelf Uncertainties EAST-ADL Contract Probabilistic inference Safety Performance 

References

  1. 1.
    SAE International, SAE Information Report: (J3016) Taxonomy and definitions for terms related to on-road motor vehicle automated driving systemsGoogle Scholar
  2. 2.
    European Commission: Intelligent transport systems. https://ec.europa.eu/transport/themes/its_en
  3. 3.
    PwC Semiconductor Report: Spotlight on Automotive. PwC, September 2013Google Scholar
  4. 4.
    ISO: ISO 26262 Road vehicles – Functional safetyGoogle Scholar
  5. 5.
    Chen, D., et al.: A knowledge-in-the-loop approach to integrated safety & security for cooperative system-of-systems. In: IEEE 7th International Conference on Intelligent Computing and Information Systems, ICICIS 2015, Cairo, Egypt, 12–14 December 2015Google Scholar
  6. 6.
    EAST-ADL: EAST-ADL Domain Model Specification, Version M.2.1.12 (2014)Google Scholar
  7. 7.
    Kolagari, R., et al.: Model-based analysis and engineering of automotive architectures with EAST-ADL: revisited. Int. J. Conceptual Struct. Smart Appl. (IJCSSA) 3(2), 25–70 (2015). IGI Global Publishing, Hershey, USACrossRefGoogle Scholar
  8. 8.
    Benveniste, A., et al.: Multiple viewpoint contract-based specification and design. In: 6th International Symposium on Formal Methods for Components and Objects, FMCO 2007 (2007)Google Scholar
  9. 9.
    Benveniste, A., et al.: Contracts for system design. Report RR-8147, Inria, November 2012Google Scholar
  10. 10.
    Maler, O., et al.: Monitoring temporal properties of continuous signals. In: Formal Techniques, Modelling and Analysis of Timed and Fault-Tolerant Systems, Joint International Conference on FORMATS/FTRTFT (2004)Google Scholar
  11. 11.
    Anthony, R., et al.: Context-aware adaptation in DySCAS. Electronic Communications of the EASST: Context-Aware Adaptation Mechanism for Pervasive and Ubiquitous Services (CAMPUS), vol. 19. European Association of Software Science and Technology (EASST) (2009). ISSN 1863-2122Google Scholar
  12. 12.
    Ghahramani, Z.: An Introduction to Hidden Markov Models and Bayesian Networks. Hidden Markov Models: Applications in Computer Vision. World Scientific Publishing Co. Inc., River Edge (2001)Google Scholar
  13. 13.
    Liu, Y., et al.: A calculus for stochastic QoS analysis. Perform. Eval. 64(6), 547–572 (2007)CrossRefGoogle Scholar
  14. 14.
    Jiang, Y., Liu, Y.: Stochastic Network Calculus. Springer Publishing Company, Heidelberg (2008)zbMATHGoogle Scholar
  15. 15.
    Vesely, W.E.: Fault Tree Handbook. US Nuclear Regulatory Committee Report NUREG-0492, US NRC, Washington, DC (1981)Google Scholar
  16. 16.
    Palady, P.: Failure Modes and Effects Analysis. PT Publications, West Palm Beach (1995). ISBN: 0-94545-617-4Google Scholar
  17. 17.
    Chen, D., et al.: Integrated safety and architecture modeling for automotive embedded systems. e&i Elektrotechnik und Informationstechnik 128(6), 196–202 (2011). doi: 10.1007/s00502-011-0007-7. ISSN: 0932-383XCrossRefGoogle Scholar
  18. 18.
    Chen, D., et al.: Systems modeling with EAST-ADL for fault tree analysis through HiP-HOPS. In: 4th IFAC Workshop on Dependable Control of Discrete Systems, York, U.K., 4–6 September 2013Google Scholar
  19. 19.
    Papadopoulos, Y., McDermid, J.A.: Hierarchically performed hazard origin and propagation studies. In: Felici, M., Kanoun, K. (eds.) SAFECOMP 1999. LNCS, vol. 1698, pp. 139–152. Springer, Heidelberg (1999). doi: 10.1007/3-540-48249-0_13 CrossRefGoogle Scholar
  20. 20.
    Sadigh, D., Kapoor, A.: Safe control under uncertainty with probabilistic signal temporal logic. Robotics: Science and Systems (RSS), June 2016Google Scholar
  21. 21.
    SysML: OMG Systems Modeling Language (OMG SysML™), OMGGoogle Scholar
  22. 22.
    Feiler, P.H., Gluch, D.P.: Model-Based Engineering with AADL: An Introduction to the SAE Architecture Analysis & Design Language. SEI Series in Software Engineering series. Addison-Wesley Professional, Boston (2012). ISBN: 10: 0-321-88894-4Google Scholar
  23. 23.
    Silva, E., et al.: A mission-oriented approach for designing system-of-systems. In: Proceedings of the 10th System-of-Systems Engineering Conference (SoSE), pp. 346–351, May 2015Google Scholar
  24. 24.
    Bryans, J., et al.: SysML contracts for systems of systems. In: IEEE Systems of Systems Engineering Conference 2014, June 2014Google Scholar
  25. 25.
    Althoff, M., et al.: Online verification of automated road vehicles using reachability analysis. IEEE Trans. Robot. 30(4), 903–918 (2014)CrossRefGoogle Scholar
  26. 26.
    Meinke, K., Sindhu, M.A.: Incremental learning-based testing for reactive systems. In: Gogolla, M., Wolff, B. (eds.) TAP 2011. LNCS, vol. 6706, pp. 134–151. Springer, Heidelberg (2011). doi: 10.1007/978-3-642-21768-5_11. IEEE Trans. Robot. 30(4), 903-918 (2014)CrossRefGoogle Scholar
  27. 27.
    Meel, A.: Plant-specific dynamic failure assessment using Bayesian theory. Chem. Eng. Sci. 61, 7036–7056 (2006)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Mechatronics, Machine Design, School of ITMKTH Royal Institute of TechnologyStockholmSweden
  2. 2.Electronics and Embedded Systems, School of ICTKTH Royal Institute of TechnologyKistaSweden

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