Details

Autor(en) / Beteiligte

• Koch, Lucas
• Roeser, Dennis
• Badalian, Kevin
• Lieb, Alexander
• Andert, Jakob

Titel

Cloud-Based Reinforcement Learning in Automotive Control Function Development

Ist Teil von

• Vehicles, 2023-08, Vol.5 (3), p.914-930

Ort / Verlag

Basel: MDPI AG

Erscheinungsjahr

2023

Link zum Volltext

Quelle

EZB Free E-Journals

Beschreibungen/Notizen

• 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.