Obstacle avoidance in real time with Nonlinear Model Predictive Control of autonomous vehicles

A Nonlinear Model Predictive Controller (NMPC) for trajectory tracking of autonomous vehicles is presented in this paper. This controller is tested under several constrained scenarios including static obstacle avoidance and avoidance of obstacles with more complex constraints. In the latter case the real life necessary constraint of remaining on the road while performing the obstacle avoidance manoeuvers is implemented. The resulting controllers are applied and tested in a simulation environment and the required CPU time is analyzed to evaluate the ability to implement these schemes in real-time using both cold and warm starts for the embedded optimization problem. In order to simplify the vehicle dynamics, a bicycle model is used for the prediction of future vehicle states in the NMPC framework. A fully nonlinear CarSim vehicle model is used to evaluate the vehicle performance in the simulations. Results show that the NMPC controller provides satisfactory online tracking performance in a realistic scenario at normal road speeds while still satisfying the real-time constraints.