This thesis evaluates two automated driving approaches in several scenarios to compare their performance for standard driving maneuvers. Both approaches are based on the use of Artificial Potential Fields (APF) and Model Predictive Control (MPC), which computes the optimal control strategy by including the physical constraints of the vehicle. For one controller, the MPC computes directly the vehicle inputs in terms of steering angle and longitudinal acceleration. However, this approach is computationally demanding, primarily when implemented onboard the vehicle control units in a path planner, due to the intrinsic complexity of the underlying optimization problem. Thus, a second approach is considered, where a simplified MPC controller acts as a trajectory planner.