The mobility sector has been revolutionized in the latest years. One of the most interesting aspects is the autonomous driving challenge that many big players in the automotive field are trying to solve. A good autonomous driving system must accomplish the driving task in a safe and effective way, to correctly replace the human behavior and possibly also to improve it. In this thesis, we propose different approaches to implement a high level autonomous driving support system, capable of understanding the state of the ego vehicle and suggesting, or directly applying, the most desirable behavior in different scenarios. The final model was developed using Reinforcement Learning to train an agent over a Markov Decision Process framework.