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COMPUTER-AIDED DESIGN OF FAULT-TOLERANT HARDWARE ARCHITECTURES FOR AUTONOMOUS DRIVING SYSTEMS

Published online by Cambridge University Press:  19 June 2023

Tim Maurice Julitz ,
Antoine Tordeux  and
Manuel Lower
Tim Maurice Julitz*
Affiliation:
University of Wuppertal
Antoine Tordeux
Affiliation:
University of Wuppertal
Manuel Lower
Affiliation:
University of Wuppertal
*
Julitz, Tim, Maurice, University of Wuppertal, Germany, [email protected]

Abstract

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Fault-tolerant hardware architectures for autonomous vehicles can be implemented through redundancy, diversity, separation, self-diagnosis, and reconfiguration. These approaches can be coupled with majority redundancy through M-out-of-N independent system architectures. The development of fault- tolerant systems is of central importance in the launch of autonomous driving systems from level 4. The increasing complexity of electrical and electronic systems is challenging for the design of safety-critical systems. This work aims to develop a method to manage this complexity in product development and to use it to compare different types of architectures. The basis is a system consisting of sensors and microcontrollers. The reliability of all possible MooN configurations of the system is calculated automatically by numerically solving the master equation of the corresponding Markov chain. Subsequently, a software-based fault tree analysis enables more detailed modeling of the component structure. The results show that four-line architectures can provide suitable results and that the development effort for 2-ECU systems is higher than for 1-ECU systems with respect to the ISO 26262 target values.

Keywords

Autonomous drivingFail-operationalProduct architectureComputational design methodsNumerical methods
Type
Article
Information
Proceedings of the Design Society , Volume 3: ICED23 , July 2023 , pp. 1047 - 1056
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2023. Published by Cambridge University Press

References

Baleani, M., Ferrari, A., Mangeruca, L., Sangiovanni-Vincentelli, A., Peri, M. and Pezzini, S. (2003), “Fault- tolerant platforms for automotive safety-critical applications”, in: Moreno, J., Murthy, P., Conte, T. and Faraboschi, P. (Editors), Proceedings of the international conference on Compilers, architectures and synthesis for embedded systems - CASES ‘03, ACM Press, New York, New York, USA, p. 170, http://doi.org/10.1145/951710.951734.Google Scholar
Daily, M., Medasani, S., Behringer, R. and Trivedi, M. (2017), “Self-driving cars”, Computer, Vol. 50 No. 12, pp. 18–23, http://doi.org/10.1109/MC.2017.4451204.CrossRefGoogle Scholar
Esser, K. and Kurte, J. (2018), “Autonomous driving. current status, potentials and impact analysis. study for the association of german chambers of industry and commerce e.v. (translated)”, [online]. https://www.dihk.de/resource/blob/3924/b1d16ab3418ee25133fe2efdfa04c832/studie-autonomes-fahren-data.pdfGoogle Scholar
Gottschalk, H., Rottmann, M. and Saltagic, M. (2022), “Does redundancy in AI perception systems help to test for super-human automated driving performance?”, Deep Neural Networks and Data for Automated Driving, pp. 81–106.CrossRefGoogle Scholar
Ishigooka, T., Honda, S. and Takada, H. (2018), “Cost-effective redundancy approach for fail-operational autonomous driving system”, in: 2018 IEEE 21st International Symposium on Real-Time Distributed Computing (ISORC), IEEE, pp. 107–115, http://doi.org/10.1109/IS0RC.2018.00023.CrossRefGoogle Scholar
ISO 26262-5 (2018), “Road vehicles—functional safety—part 5: Product development at the hardware level”, International Standard.Google Scholar
Kohn, A., Kabmeyer, M., Schneider, R., Roger, A., Stellwag, C. and Herkersdorf, A. (2015), “Fail-operational in safety-related automotive multi-core systems”, in: 10th IEEE International Symposium on Industrial Embedded Systems (SIES), IEEE, pp. 1–4, http://doi.org/10.1109/SIES.2015.7185051.CrossRefGoogle Scholar
Laissy, J.C., Lyon, V., Le Mouellic, M., Walus, S., Alt, L. and Giraud, R. (2022), “How to deal with exponential complexity in automotive engineering”, LinkedIn Pulse. https://www.linkedin.com/pulse/how-deal-exponential-complexity-automotive-jean-christophe-laissy-Google Scholar
Lin, S.C., Zhang, Y., Hsu, C.H., Skach, M., Haque, M.E., Tang, L. and Mars, J. (2018), “The architectural implications of autonomous driving”, in: Shen, X., Tuck, J., Bianchini, R. and Sarkar, V. (Editors), Proceedings of the Twenty-Third International Conference on Architectural Support for Programming Languages and Operating Systems, ACM, New York, NY, USA, pp. 751–766, http://doi.org/10.1145/3173162.3173191.CrossRefGoogle Scholar
Mobileye (2022), “True redundancy: The realistic path to deploying avs at scale”, [online], accessed 2022-11-10. https://www.mobileye.com/technology/true-redundancy/Google Scholar
National Transportation Safety Board (NTSB) (2017), “Highway accident report: Collision between a car operating with automated vehicle control systems and a tractor-semitrailer truck near williston, florida, may 7, 2016.”, NTSB/HAR-17/02.Google Scholar
0penReliability.org (2022), “Fault tree analysis on r”, [online], accessed 2022-11-10. http://www.openreliability.org/fault-tree-analysis-on-r/Google Scholar
Sari, B. (2020), Fail-operational Safety Architecture for ADAS/AD Systems and a Model-driven Approach for Dependent Failure Analysis, Springer Fachmedien Wiesbaden, Wiesbaden, http://doi.org/10.1007/ 978-3-658-29422-9.Google Scholar
Schmid, T., Schraufstetter, S., Wagner, S. and Hellhake, D. (2019), “A safety argumentation for fail-operational automotive systems in compliance with iso 26262”, in: 2019 4th International Conference on System Reliability and Safety (ICSRS), IEEE, pp. 484–493, http://doi.org/10.1109/ICSRS48664.2019.8987656.CrossRefGoogle Scholar
Tesla (2022), “Transitioning to tesla vision”, [online], accessed 2022-11-10. https://www.tesla.com/support/transitioning-tesla-visionGoogle Scholar
Trattner, A., Hvam, L., Forza, C. and Herbert-Hansen, Z.N.L. (2019), “Product complexity and operational performance: A systematic literature review”, CIRP Journal of Manufacturing Science and Technology, Vol. 25, pp. 69–83, http://doi.org/10.1016/jj.cirpj.2019.02.001.CrossRefGoogle Scholar
Zhang, L. and Wu, H. (2021), “Application of single chip technology in internet of things electronic products”, Journal of Intelligent & Fuzzy Systems, Vol. 40 No. 2, pp. 3223–3233, http://doi.org/10.3233/JIFS-189362.CrossRefGoogle Scholar