Autonomous vehicles (AVs) can solve a lot of problems related to traffic safety, comfort and congestion. While the sensors used by these vehicles are getting cheaper, more accurate, and software is improving, the first completely automated vehicle does not exist yet. When AVs start gradually driving on the roads, there will be a mixed traffic situation consisting of AVs and human driven vehicles. To understand the implications on the traffic performance, this thesis will implement a Model Predictive Controller (MPC) and the behavioural models for AVs into a numerical simulation in SUMO. The MPC can be used to control multiple AVs in larger traffic simulations to analyse the effects of certain penetration rates and traffic densities when changing the AVs driving behaviour. Both the Post-Encroachment Time (PET) and Time To Collision (TTC) will be used for decision making in overtaking and collision avoidance strategies. In this thesis, the MPC which is solving a quadratic programming problem has proven to successfully overtake other vehicles in an on- and off-ramp highway scenario which results in a smaller average travel time for the AVs than for the human drivers. It is shown that the decrease in average travel time for the AVs was not at the cost of the Human Vehicles (HVs). This is done by decreasing the TTC used by the AVs as a headway for longitudinal overtaking behaviour, and the PET used for lateral lane changing behaviour, independently to a minimum of 0.1 seconds to guaranty safety, and measure the average travel times for both HVs and AVs. Multiple simulations using different penetration rates ranging from 0 to 40 percent also show that the decrease in average travel time for the AVs was not at the cost of the HVs. We can conclude that using an MPC integrated together with the behavioural rules of human drivers into numerical simulation can give a good indication of the implications that AVs have on traffic performance.