Automated vehicle control provides advantages in transport efficiency, redundancy, human-safety, flexibility, and parallelism. Extensive research has been dedicated to direct-following lateral control methods, using a single preview point as reference signal. However, these methods give rise to significant tracking errors. As alternative to a single point, a continuous path can be used as reference signal for vehicle control. Using a continuous reference path, accurate and comfortable control actions can be computed, while avoiding reference tracking errors. Therefore, robust reference path generation is an essential part of lateral vehicle control. The goal of this research is to develop a generic, robust reference path generator for lateral vehicle control. Currently in path generation, the state-of-the-art method is based on repetitive polynomial fitting. This method inherently contains two main weaknesses. Firstly, it is not robust to sensor noise and other real-world disturbances. Secondly, as a result of the repetitive fitting, a discontinuous path is generated. This is undesirable, because it leads to an unfeasible reference path, since vehicles can only produce and track continuous trajectories. Moreover, a discontinuous reference path results in the need for path smoothing when used in comfortable lateral vehicle control. Fundamentally, this smoothing leads to inaccurate control, caused by manipulation of the original, discontinuous reference path. To overcome these weaknesses, a new Model-based Path Generation method is presented. The development of a new, generic method for robust path generation is the main contribution of this research. This new path generation method is capable of producing feasible vehicle trajectories based on unfeasible waypoints. The performance of this method is evaluated in simulations and experiments, benchmarking it against polynomial fitting path generation. Furthermore, path generation robustness is assessed based on the outcome of a disturbance sensitivity analysis. The results are in accordance with the hypothesis stating that the new method outperforms the benchmark method in terms of path accuracy, robustness, continuity, and general applicability.