This work conducts a systematic experimental evaluation of the state-of-the-art Reference Aware Model Predictive Controller (RA-MPC) for autonomous vehicles. The RA-MPC is a path-tracking controller, that maximizes tracking accuracy and comfort. The controller uses a kinematic vehicle model with a nonlinear curvature response table that adapts the steering response online to the vehicle and operating conditions. The adaptiveness and robustness of the controller are analyzed by evaluating the performance on a highway truck, loaded and empty mining trucks, and a city bus. Moreover, highway-like and city-like scenarios are performed using the exact same implementation and parameter settings for all vehicles. The controller and model adaption achieved a very good path tracking performance in all experiments, deviating at most 25 cm from the reference path.