Ongoing research in autonomous driving currently focuses on creating new applications for autonomous vehicles (AV) and connected autonomous vehicles (CAV). Specifically, motion planning and control solutions are being developed based on the combination of Artificial Potential Functions (APF) with economic Model Predictive Control (eMPC). These two methods are integrated into a new Comprehensive Predictive Control (CPC) strategy. Although preliminary research shows promising results, a performance analysis of this approach, both for AV and CAV, has not yet been published. Therefore this thesis studies the capabilities of this novel APF-eMPC framework by carrying out numerical simulations. Multiple manoeuvres and varying amounts of white noise are utilized to test the controller's limitations. For the AV part, multiple basic driving manoeuvres are simulated: lane-keeping, car-following and lane-changing. The results show that an AV based on this framework can execute these different manoeuvres without precise measurements. The CAV concept is simulated using a platoon scenario. The gap-closing behaviour of the multiple CAVs in a platoon is examined. The state-of-the-art gap-closing APF is compared with an APF based on inter-molecular dynamics and fitted on actual traffic. Various experiments are carried out using a constant time-headway in combination with different time gaps between the vehicles. The results show that the resulting behaviour by the inter-molecular APF better matches human driving behaviour and results in less dangerous gap-closing behaviour than the quadratic platoon APF. The latter has a more considerable change of lateral instability occurring. Therefore the APF based on inter-molecular dynamics and fitted on actual traffic data outperforms the APF based on a quadratic function. Lastly, it was found that the coupling between the longitudinal and lateral dynamics, often neglected in literature, cannot be ignored during platoon stability analysis.