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Exploiting Adaptation Behavior of an Autonomous Vehicle to Achieve Fail-Safe Reconfiguration

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Commercial Vehicle Technology 2020/2021

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Abstract

Autonomous Vehicles (AV) operate in a safety-critical dynamic environment. Frequently occurring environmental uncertainties and random component failures might result into hazardous events, sometimes even into an accident, if left undetected. Moreover, in the event of random errors, highly integrated automotive systems might suffer from the butterfly effect, where a small failure in a component can lead to an unexpected unsafe behavior within the entire system. While an AV operates in a dynamic environment, traditional safety assurance mechanisms like Fault Tree Analysis (FTA), Failure Mode Effect and Criticality Analysis (FMECA), etc. are plays a vital role, but not sufficient enough to ensure safety (at runtime) as they are based on static worst-case assumptions. One possible way to overcome this is through runtime monitoring, where the AV and its behavior can be monitored during operation. In case of an unplanned behavior or safety-critical deviation appear, the monitor can manage the system to assume a safe state via reconfiguration process. We present our work where we exploit the adaptation behavior of AV, to ensure its safe operation at runtime. At this end, we investigate the adaptation behavior of an Adaptive Cruise Control (ACC) of an AV, followed by implementing different possible adaptation techniques.

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References

  1. Bhardwaj, N., Liggesmeyer, P.: A runtime risk assessment concept for safe reconfiguration in open adaptive systems. In: International Conference on Computer Safety, Reliability, and Security. pp. 309–316. Springer (2017)

    Google Scholar 

  2. Bhardwaj, N., Liggesmeyer, P.: A conceptual framework for safe reconfiguration in open system of systems. In: 2018 IEEE/ACM 6th International Workshop on Software Engineering for Systems-of-Systems (SESoS). pp. 17–20. IEEE (2018)

    Google Scholar 

  3. Bhardwaj Haupt, N., Liggesmeyer, P.: A runtime safety monitoring approach for adaptable autonomous systems. In: Computer Safety, Reliability, and Security - SAFECOMP 2019 Workshops, ASSURE, DECSoS, SASSUR, STRIVE, and WAISE, Turku, Finland, September 10, 2019, Proceedings. vol. 11699, pp. 166–177. Springer (2019)

    Google Scholar 

  4. Bhardwaj Haupt, N., Liggesmeyer, P.: Systematic specification of a service safety monitor for autonomous vehicles. In: 5th Workshop on Critical Automotive Applications: Robustness & Safety, CARS 2019 (EDCC Workshop). ACM (2019) 5. Computing, A., et al.: An architectural blueprint for autonomic computing. IBM White Paper 31(2006), 1–6 (2006)

    Google Scholar 

  5. Eskandarian, A.: Handbook of intelligent vehicles, vol. 2. Springer (2012)

    Google Scholar 

  6. Goodloe, A.E., Pike, L.: Monitoring distributed real-time systems: A survey and future directions (2010)

    Google Scholar 

  7. Haddadin, S., Suppa, M., Fuchs, S., Bodenm¨uller, T., Albu-Schäffer, A., Hirzinger, G.: Towards the robotic co-worker. In: Robotics Research, pp. 261–282. Springer (2011)

    Google Scholar 

  8. Handte, M., Schiele, G., Matjuntke, V., Becker, C., Marr´on, P.J.: 3pc: System support for adaptive peer-to-peer pervasive computing. ACM Transactions on Autonomous and Adaptive Systems (TAAS) 7(1), 10 (2012)

    Google Scholar 

  9. Heffernan, D., MacNamee, C.: Runtime observation of functional safety properties in an automotive control network. Journal of Systems Architecture 68, 38–50 (2016) 11. ISO26262: Road vehicles-functional safety. International Standard ISO/FDIS 26262 (2011)

    Google Scholar 

  10. Jones, A., Kong, Z., Belta, C.: Anomaly detection in cyber-physical systems: A formal methods approach. In: 53rd IEEE Conference on Decision and Control. pp. 848–853. IEEE (2014)

    Google Scholar 

  11. Klein, P.: The safety-bag expert system in the electronic railway interlocking system elektra. In: Operational Expert System Applications in Europe, pp. 1–15. Elsevier (1991)

    Google Scholar 

  12. Koopman, P.: Challenges in representing cps safety. In: Workshop on developing dependable and secure automotive cyber-physical systems from components (2011)

    Google Scholar 

  13. Kramer, J., Magee, J.: Dynamic configuration for distributed systems. IEEE Transactions on Software Engineering (4), 424–436 (1985)

    Google Scholar 

  14. Krupitzer, C., Breitbach, M., Roth, F.M., VanSyckel, S., Schiele, G., Becker, C.: A survey on engineering approaches for self-adaptive systems (extended version) (2018)

    Google Scholar 

  15. Martin, H., Tschabuschnig, K., Bridal, O., Watzenig, D.: Functional safety of automated driving systems: Does iso 26262 meet the challenges? In: Automated Driving, pp. 387–416. Springer (2017)

    Google Scholar 

  16. McKinley, P.K., Sadjadi, S.M., Kasten, E.P., Cheng, B.H.: A taxonomy of compositional adaptation. Rapport Technique num´eroMSU-CSE-04-17 (2004)

    Google Scholar 

  17. Reichle, R., Khan, M.U., Geihs, K.: How to combine parameter and compositional adaptation in the modeling of self-adaptive applications. PIK-Praxis der Informationsverarbeitung und Kommunikation 31(1), 34–38 (2008)

    Google Scholar 

  18. SAE On-Road Automated Vehicle Standards Committee and others: Taxonomy and definitions for terms related to driving automation systems for on-road motor vehicles. SAE International: Warrendale, PA, USA (2018)

    Google Scholar 

  19. Salehie, M., Tahvildari, L.: Self-adaptive software: Landscape and research challenges. ACM transactions on autonomous and adaptive systems (TAAS) 4(2), 14 (2009)

    Google Scholar 

  20. Walderyd, F.: Hazard identification and safety goals on power electronics in hybrid vehicles. Chalmers University of Technology (2010)

    Google Scholar 

Download references

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Correspondence to Anil Ranjitbhai Patel .

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© 2021 Der/die Autor(en), exklusiv lizenziert durch Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature

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Patel, A.R., Haupt, N.B., Liggesmeyer, P. (2021). Exploiting Adaptation Behavior of an Autonomous Vehicle to Achieve Fail-Safe Reconfiguration. In: Berns, K., Dressler, K., Kalmar, R., Stephan, N., Teutsch, R., Thul, M. (eds) Commercial Vehicle Technology 2020/2021. Proceedings. Springer Vieweg, Wiesbaden. https://doi.org/10.1007/978-3-658-29717-6_26

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