The work aims at investigating a possible alternative solution to the control systems currently utilized for Driving Automation, through the implementation of a system based on Reinforcement learning for the control of a level 4 oriented shuttle solution for dedicated roads. The Driving problem will be, therefore, structured as a Markov Decision Process, in order to comply with the RL problem formulation. A model-based simulation will be implemented in MATLAB and Simulink, in order to build a feasible and reliable training environment. The study will introduce the Driving Automation paradigm, as well as the Reinforcement Learning approach. The employed DDPG RL Actor will be, then, analyzed, along with the state of the art of RL application to AD.