Towards a Reliable and Context-Based System Architecture for Autonomous Vehicles

Authors Tobias Kain , Philipp Mundhenk, Julian-Steffen Müller, Hans Tompits , Maximilian Wesche, Hendrik Decke

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Author Details

Tobias Kain
  • Volkswagen AG, Wolfsburg, Germany
Philipp Mundhenk
  • Autonomous Intelligent Driving GmbH, München, Germany
Julian-Steffen Müller
  • Volkswagen AG, Wolfsburg, Germany
Hans Tompits
  • Technische Universität Wien, Austria
Maximilian Wesche
  • Volkswagen AG, Wolfsburg, Germany
Hendrik Decke
  • Volkswagen AG, Wolfsburg, Germany

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Tobias Kain, Philipp Mundhenk, Julian-Steffen Müller, Hans Tompits, Maximilian Wesche, and Hendrik Decke. Towards a Reliable and Context-Based System Architecture for Autonomous Vehicles. In 2nd International Workshop on Autonomous Systems Design (ASD 2020). Open Access Series in Informatics (OASIcs), Volume 79, pp. 1:1-1:7, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)


Full vehicle autonomy excludes a takeover by passengers in case a safety-critical application fails. Therefore, the system responsible for operating the autonomous vehicle has to detect and handle failures autonomously. Moreover, this system has to ensure the safety of the passengers, as well as the safety of other road users at any given time. Especially in the initial phase of autonomous vehicles, building up consumer confidence is essential. Therefore, in this regard, handling all failures by simply performing an emergency stop is not desirable. In this paper, we introduce an approach enabling a dynamic and safe reconfiguration of the autonomous driving system to handle occurring hardware and software failures. Since the requirements concerning safe reconfiguration actions are significantly affected by the current context the car is experiencing, the developed reconfiguration approach is sensitive to context changes. Our approach defines three interconnected layers, which are distinguished by their level of awareness. The top layer, referred to as the context layer, is responsible for observing the context. These context observations, in turn, imply a set of requirements, which constitute the input for the reconfiguration layer. The latter layer is required to determine reconfiguration actions, which are then executed by the architecture layer.

Subject Classification

ACM Subject Classification
  • Computer systems organization → Reconfigurable computing
  • autonomous driving
  • fail-operational systems
  • context-based architecture
  • application placement
  • optimization
  • monitoring


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  1. Irwan Bello, Hieu Pham, Quoc V. Le, Mohammad Norouzi, and Samy Bengio. Neural combinatorial optimization with reinforcement learning. CoRR, abs/1611.09940, 2016. URL:
  2. Gerhard Brewka, Thomas Eiter, and Mirosław Truszczyński. Answer set programming at a glance. Communications of the ACM, 54(12):92-103, 2011. Google Scholar
  3. Karel A Brookhuis, Dick de Waard, and Wiel H. Janssen. Behavioural impacts of advanced driver assistance systems-An overview. European Journal of Transport and Infrastructure Research, 1(3), 2019. Google Scholar
  4. Martin Gebser, Benjamin Kaufmann, André Neumann, and Torsten Schaub. clasp: A conflict-driven answer set solver. In Proceedings of 9th International Conference on Logic Programming and Nonmonotonic Reasoning (LPNMR 2007), volume 4483 of Lecture Notes in Computer Science, pages 260-265. Springer, 2007. Google Scholar
  5. Fatma Ben Jemaa, Guy Pujolle, and Michel Pariente. QoS-aware VNF placement optimization in edge-central carrier cloud architecture. In Proceedings of the 2016 IEEE Global Communications Conference (GLOBECOM 2016), pages 1-7, 2016. Google Scholar
  6. Tobias Kain, Hans Tompits, Julian-Steffen Müller, Philipp Mundhenk, Maximilan Wesche, and Hendrik Decke. Fdiro: A general approach for a fail-operational system design, 2020. Submitted draft. Abstract accepted for presentation at 30th European Safety and Reliability Conference (ESREL 2020). Google Scholar
  7. Nicola Leone, Gerald Pfeifer, Wolfgang Faber, Thomas Eiter, Georg Gottlob, Simona Perri, and Francesco Scarcello. The DLV system for knowledge representation and reasoning. ACM Transactions on Computational Logic, 7(3):499-562, 2006. Google Scholar
  8. Bo Li, Jianxin Li, Jinpeng Huai, Tianyu Wo, Qin Li, and Liang Zhong. EnaCloud: An energy-saving application live placement approach for cloud computing environments. In Proceedings of the 2009 IEEE International Conference on Cloud Computing (CLOUD-II 2009), pages 17-24, 2009. Google Scholar
  9. Xiaoqiao Meng, Vasileios Pappas, and Li Zhang. Improving the scalability of data center networks with traffic-aware virtual machine placement. In Proceedings of the 2010 IEEE International Conference on Computer Communications (INFOCOM 2010), pages 1-9, 2010. Google Scholar
  10. Marco Mori, Fei Li, Christoph Dorn, Paola Inverardi, and Schahram Dustdar. Leveraging state-based user preferences in context-aware reconfigurations for self-adaptive systems. In Proceedings of the 9th International Conference on Software Engineering and Formal Methods (SEFM 2011), volume 7041 of Lecture Notes in Computer Science, pages 286-301. Springer, 2011. Google Scholar
  11. Andry Rakotonirainy. Design of context-aware systems for vehicles using complex system paradigms. In Proceedings of the CONTEXT 2005 Workshop on Safety and Context, volume 158 of CEUR Workshop Proceedings., 2005. Google Scholar
  12. Yi Ren, Junichi Suzuki, Athanasios Vasilakos, Shingo Omura, and Katsuya Oba. Cielo: An evolutionary game theoretic framework for virtual machine placement in clouds. In Proceedings of 2014 International Conference on Future Internet of Things and Cloud (FiCloud 2014), pages 1-8, 2014. Google Scholar
  13. SAE International. Taxonomy and Definitions for Terms Related to On-Road Motor Vehicle Automated Driving Systems. SAE Standard J3016, 2014. Google Scholar
  14. Johannes Schlatow, Mischa Möstl, Rolf Ernst, Marcus Nolte, Inga Jatzkowski, Markus Maurer, Christian Herber, and Andreas Herkersdorf. Self-awareness in autonomous automotive systems. In Proceedings of the 20th Conference &Exhibition on Design, Automation &Test in Europe (DATE 2017), pages 1050-1055. European Design and Automation Association, 2017. Google Scholar
  15. Gereon Weiss, Florian Grigoleit, and Peter Struss. Context modeling for dynamic configuration of automotive functions. In Proceedings of the 16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013), pages 839-844, 2013. Google Scholar
  16. Ali Zolghadri. Advanced model-based FDIR techniques for aerospace systems: Today challenges and opportunities. Progress in Aerospace Sciences, 53:18-29, 2012. Google Scholar
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