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Systematic Model-based Design of a Reinforcement Learning-based Neural Adaptive Cruise Control System

In this paper, the systematic model-based design of a reinforcement learning-based neuronal adaptive cruise control is described. Starting with an introduction and a summary of current fundamentals, design methods for intelligent driving functions are presented. The focus is on the first-time presentation of a novel design methodology for artificial neural networks in control engineering. This methodology is then applied and fully validated using the example of an adaptive cruise control system.