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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, julitz@uni-wuppertal.de

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

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