Methodology for Real-World Automated Function Development: From Virtual to On-Vehicle Implementation

The automotive industry is increasingly facing challenges stemming from growing system complexities, shortened development cycles, and the demand for rapid time-to-market transitions. Reinforcement learning (RL) has emerged as a promising approach to developing advanced control functions due to its adaptive and autonomous nature. Although it has already successfully demonstrated its viability in virtualised X-in-the-Loop (XiL) environments, its application to real-world vehicle systems is inhibited by safety concerns, real-time constraints, and the integration into established software toolchains. This paper introduces a comprehensive methodology for developing such control functions with RL: starting in a virtual environment, training then transitions to a Hardware-in-the-Loop (HiL) setup, and ultimately proceeds to a real vehicle. Utilising the open-source framework LExCI, the proposed approach facilitates seamless training across multiple development stages and showcases RL's ability to develop strategies which outperform conventional controllers in realistic scenarios. This work underlines the practical benefits of RL in function development and explains how the presented methodology is easily applicable to a wide range of control problems, all while complying with industry standards for safety and reliability.