The autonomous transit market is on the rise. 2getthere is one of the lead suppliers of automated people moving systems, located in the Netherlands. Their vehicles can be classified as SAE level 4 automation (Highly Automated) vehicles. This thesis is concerned with the dynamic trajectory generation for 2getthere’s Group Rapid Transport (GRT) mini-bus in case deviation from a nominal trajectory is required.

Trajectory generation can be achieved using several planning approaches. However, only few of these approaches can take into account the vehicle’s dynamics, as well as comfort and safety constraints. Moreover a degree of optimality of the deviation is required in order to keep the vehicle as close as possible to the nominal trajectory. A model predictive control (MPC) based trajectory planner is designed to solve a constrained optimal control problem. Using the ACADO toolkit, highly efficient self contained C code is generated to solve the trajectory generation problem very rapidly.

A single-track model with front and rear wheel steering is used to predict the four-wheel-steered vehicle’s manoeuvring capabilities in every consecutive initial state. This enables the planner to take into account the GRT’s dynamics, without overcomplicating the optimization problem. The performance of the planner is tested in Simulink.