A hierarchical model predictive controller for autonomous vehicle control is presented. The controller generates a path of shortest distance by optimising lateral coordinates on a fixed longitudinal grid, while respecting road bounds. This static path is parameterised by a re-planning module, which then optimises the path to restrict the tangential and normal acceleration and jerk values along the trajectories’ arc length. The optimised trajectory is then tracked using a nonlinear model predictive control scheme using a bicycle plant model to optimise the steer angle and longitudinal slip of the tyre. The hierarchical controller is evaluated in simulation during a double lane change manoeuvre, demonstrating its ability to track the reference trajectory while observing the road boundaries and acceleration limits.