Autonomous vehicles and Advanced Driver Assistance Systems (ADAS) applications are emerging fields of research and development that require specialized controllers to ensure safe and efficient functionality. However, testing these controllers in real-world scenarios can be costly and risky, making the use of simulators necessary for their design and validation. This thesis presents a framework for the control and simulation of fundamental ADAS applications for autonomous vehicles, using Simulink as controller design environment and Carla as simulation environment. Firstly, the thesis describes how these tools were integrated. Then, the focus shifts to the development and testing of two control methods: PID and Nonlinear Model Predictive Control (NMPC).