Abstract
The aim of our research is to implement and evaluate Organic Computing methods in a real vehicle environment and thus increase the reliability of the vehicle’s steering system. For this purpose, the steering system is simulated using an Organic Computing approach, which is extended with explicit fault diagnosis capabilities. In order to achieve the goals set by the demands of the automotive industry, several development steps are necessary. This paper outlines the research gaps and potentials as well as the approaches we envision to tackle these challenges. The starting point is a general concept involving three Electronic Control Units (ECUs) that act as active redundancy in the event of a component failure, e.g., an ECU failure. Next, the Organic Computing middleware in terms of an Artificial Hormone System in combination with Artificial DNA will be implemented and examined on real automotive hardware, resulting in a highly-reliable, resource- and cost-efficient fail-operational system. Beyond the classical working principles of Organic Computing to overcome ECU failures, our project implements system-level fault diagnosis to additionally monitor ECU performance in the context of the steering system. Forecasting, detecting and identifying faults in the system enables fault-specific recovery actions, e.g., preventive service migration or degradation. Overall, we combine promising relative work to an Organic Computing approach for a vehicle steering system with an industrial partnership with the goal of a functional ECU. After successful deployment, tests will be documented with suitable vehicle hardware in a corresponding simulation environment. This paper provides insight into the early stages of our research project.
Supported by Federal Ministry for Economic Affairs and Climate Action.
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Kisselbach, T., Meckel, S., Pacher, M., Brinkschulte, U., Obermaisser, R. (2022). Organic Computing to Improve the Dependability of an Automotive Environment. In: Schulz, M., Trinitis, C., Papadopoulou, N., Pionteck, T. (eds) Architecture of Computing Systems. ARCS 2022. Lecture Notes in Computer Science, vol 13642. Springer, Cham. https://doi.org/10.1007/978-3-031-21867-5_14
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