Abstract
Autonomous vehicles are increasingly being equipped with a large number of Electronic Control Units. This trend results in systems becoming more complex, which in turn raises the likelihood of failures and unforeseen errors. To address these challenges, this article presents the integration of the Artificial DNA (ADNA)-based Organic Computing (OC) approach into the CAR Learning to Act (CARLA) simulator. CARLA is a powerful tool for the automotive industry to explore autonomous driving in a cost-efficient way. It therefore offers an ideal environment for testing innovative solutions from the field of OC. The research objective is to implement and evaluate OC methods in a vehicle environment in order to increase the reliability of vehicle functions. Thanks to the ADNA-based OC approach, the self-* properties of vehicles are available, and their driving behaviour can be researched. The first experiments will be presented as case study of this proof of concept in which the vehicle is controlled both manually and autonomously entirely by ADNA-based OC.
Supported by Federal Ministry for Economic Affairs and Climate Action of Germany.
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Kisselbach, T., Wörner, P., Pacher, M., Brinkschulte, U. (2024). An Organic Computing Approach for CARLA Simulator. In: Fey, D., Stabernack, B., Lankes, S., Pacher, M., Pionteck, T. (eds) Architecture of Computing Systems. ARCS 2024. Lecture Notes in Computer Science, vol 14842. Springer, Cham. https://doi.org/10.1007/978-3-031-66146-4_10
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