The development of Advanced Driver Assistance Systems (ADAS) represents a rapidly evolving field, focused on enhancing vehicle safety and efficiency through the automation of key driving functions, including steering, acceleration, and braking. These systems serve as the foundation for autonomous vehicles, which aim to operate without human intervention through the integration of sensors, control algorithms, and real-time decision making. However, evaluating control strategies in real-world conditions is often costly and impractical, thereby highlighting the need of using simulation environments for rigorous testing and validation. In this context, this thesis presents a simulation framework for evaluating control strategies in autonomous vehicle applications, integrating Simulink for controller design with the CARLA simulator for realistic testing scenarios.