Gaussian Process based Model Predictive Control for Overtaking Scenarios at Highway Curves

Abstract

Model predictive control (MPC) is a commonly applied vehicle control technique, but its performance depends highly on how accurate the model captures the vehicle dynamics. It is disreputable hard to get a precise vehicle model in complex situations. The unmodeled dynamic will cause the uncertainty of the prediction which brings the risk while overtaking. To address this issue, Gaussian process (GP) regression is employed to acquire the unexplored discrepancy between the nominal vehicle model and the real vehicle dynamics which can result in a more accurate model. To achieve safe overtaking at highway curves, the constraint conditions are carefully designed. The implementation of GP-based MPC including approximate uncertainty propagation and safety constraints ensures that the ego vehicle overtakes the obstacles without collision. Simulation results show that GP-based MPC has a strong adaptability to different scenarios and outperforms MPC in overtaking control.