Model Predictive Control of Autonomous Driving using Unscented Kalman Filter with Sparse Spectrum Gaussian Processes

In this paper, a model predictive control (MPC) approach that combines sparse spectrum Gaussian processes model and unscented Kalman Filter is proposed for path tracking task in autonomous driving. To tackle the difficulty of balancing control performance and computational cost in MPC with Gaussian processes model, the proposed approach employs the sparse spectrum Gaussian processes (SSGP) to efficiently model the vehicle, and utilizes unscented Kalman filter (UKF) to naturally propagate model uncertainties during multiple step prediction of MPC. The proposed approach is evaluated in both a numerical driving simulation and a mature driving simulation CARLA. The results indicate that the proposed method achieves a robust driving performance with a significant reduction of computational complexity.