The assimilation of path planning and motion control is a crucial capability for autonomous vehicles. Pure pursuit controllers are a prevalent class of path tracking algorithms for front wheel steering cars. Nonetheless, their performance is rather limited to relatively low speeds. In this paper, we propose a model predictive active yaw control implementation of pure pursuit path tracking that accommodates the vehicle’s steady state lateral dynamics to improve tracking performance at high speeds. A comparative numerical analysis was under taken between the proposed strategy and the traditional pure pursuit controller scheme. Tests were conducted for three different paths at iteratively increasing speeds from 1 m/s up to 20 m/s. The traditional pure pursuit controller was incapable of maintaining the vehicle stable at speeds upwards of 5m/s. The results show that implementing receding horizon strategy for pure pursuit tracking improves their performance. The contribution is apparent by preserving a relatively constant controller effort and consequently maintaining vehicle stability for speeds up to 100Km/h in different scenarios. A Matlab implementation of the proposed controller and datasets of the experimental paths are provided to supplement this work.