Small Scale, Big Impact: Experiences from a Miniature ViL Testbed and Digital Twin Development

Kurzfassung

The concept of Digital Twin (DT) has gained enormous momentum over the past years in many fields with a variety of purposes. We investigated the usage of DTs for the development and testing of automated driving functions. In this context, we wanted to train an agent to challenge the automated driving function of a vehicle via reinforcement learning (RL). For this, we build both, a miniature Vehicle in a loop (ViL) testbed and its digital shadow. The idea is to use the digital shadow as the training environment for the agent resulting in reduced cost and time for training. We decided specifically to build a miniature version of a testbed to accelerate development, reduce resource consumption and increase adaptability. This paper contributed to the engineering of DTs by reporting our approach regarding the development of a digital shadow of a miniature ViL testbed and the lessons learned. First, we motivate the decision for a miniature testbed. Secondly, we describe the high-level architecture and the technical implementation including both the digital shadow and its physical counterpart. Third, we describe the application of the DT for RL and the experiments enabled by our setup. We conclude with the lessons learned. The main takeaway is that DTs are an excellent means to develop, disseminate and present new methods for the validation of automated vehicles. Here the benefits outweigh the effort of DT construction.