Literature Database Entry

koch2023cloud-based

Lucas Koch, Dennis Roeser, Kevin Badalian, Alexander Lieb and Jakob Andert, "Cloud-Based Reinforcement Learning in Automotive Control Function Development," Vehicles, vol. 5 (3), pp. 914–930, August 2023.

Abstract

Automotive control functions are becoming increasingly complex and their development is becoming more and more elaborate, leading to a strong need for automated solutions within the development process. Here, reinforcement learning offers a significant potential for function development to generate optimized control functions in an automated manner. Despite its successful deployment in a variety of control tasks, there is still a lack of standard tooling solutions for function development based on reinforcement learning in the automotive industry. To address this gap, we present a flexible framework that couples the conventional development process with an open-source reinforcement learning library. It features modular, physical models for relevant vehicle components, a co-simulation with a microscopic traffic simulation to generate realistic scenarios, and enables distributed and parallelized training. We demonstrate the effectiveness of our proposed method in a feasibility study to learn a control function for automated longitudinal control of an electric vehicle in an urban traffic scenario. The evolved control strategy produces a smooth trajectory with energy savings of up to 14%. The results highlight the great potential of reinforcement learning for automated control function development and prove the effectiveness of the proposed framework.

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Lucas Koch
Dennis Roeser
Kevin Badalian
Alexander Lieb
Jakob Andert

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