The concept of motion planning for a path following task is highly relevant. Having a comfortable reference motion is an advantage for automated vehicles, since this leads to more higher comfort for the passengers, in turn leading to higher adoption of ADAS. Defining a speed profile can be done in three different ways. First is a heuristics based methods, such as seen in IPG CarMaker's driver model. Secondly, line-of-sight based methods use the knowledge that blind corners can contain potential moving obstacles, leading to slower speeds to compensate for the braking distance. Finally, optimization based methods, such as laptime optimization, can be used by defining dynamics, constraints and a cost function to find the optimal motion for the given scenario. Comfort is primarily a function of acceleration and jerk. The RMS of the acceleration should be as low as possible for more comfort, while jerk lower than a determined value can be ignored from a comfort point of view. Finally, the scope and the contributions to the state of the art have been brought forward. A curvilinear approach in combination with a distance domain approach is utilized, since this allows for only the curvature of the road to be used as a reference. To generate a speed profile longitudinal point mass dynamics are utilized with the jerk as an input to the optimization. The maximum speed is determined beforehand using the curvature of the road and assuming steady-state cornering. For each point along the road a speed is now determined that should not be exceeded. Generating a speed profile using a linear optimization can not be done accurately. However, an assumption can be made that the vehicle takes the same time for each distance step, leading to an approximate solution. The non-linear optimization is able to overcome the shortcomings of the linear optimization, by including the time as a state in the solution. By incorporating the lateral dynamics in the EOM the predetermined maximum speed is no longer needed. The lateral dynamics are simplified by assuming the vehicle is always aligned with the reference, therefore ignoring yaw dynamics. Leading to a complete description of the longitudinal and lateral motion that can be solved using a non-linear optimization method. ACADO is used for the non-linear optimization. A benchmark is performed against a state of the art literature example based on a non-linear optimization method that is able to minimize motion sickness based on vibrations experienced by the passengers. The speed profile generation based on linear optimization is not able to reproduce the resulting speed profile as seen in the example. For the non-linear optimization method the speed profile obtained for the time optimal cost function is very close, however for the comfortable motion, the speed through the corners was still too high to be considered comfortable. By minimizing the acceleration and jerk the motion planning system including the lateral motion is able to accurately reproduce similar speed profiles for the three different cost functions of the example. In Simcenter Prescan a complex 10 degrees of freedom vehicle model containing both sprung and unsprung mass has been presented. A driver model, based on two PD controllers for longitudinal control and the Prescan built-in path follower for lateral control, has been introduced. The motion planning containing both lateral and longitudinal motion has been used to define a comfortable reference speed and trajectory. The simulation results show that the reference motion is comparable to the resulting motion, both in the longitudinal and the lateral direction. Indicating that the motion planning system produces accurate results. On the other hand it is also observed that the longitudinal control of the vehicle needs a more complex control scheme. For the lateral control a predictive control scheme is needed to be able to follow the road with low lateral offset.