Complex Track Maneuvering using Real-Time MPC Control for Autonomous Driving

Abstract

This work proposes a framework to design, formulate and implement a path tracker for self-driving cars (SDC) based on nonlinear model-predictive-control (NMPC) approach. The presented methodology is developed to be used by designers in the industrial sector, practitioners and academics. Therefore, it is straight forward, flexible, and comprehensive as well. It allows the designer to easily integrate multiple objective terms in the cost function either opposing or correlating. The proposed design of the controller not only targets accurate tracking but also comfortable ride and fast travel time as well by introducing several sub-objective terms in the main cost function to satisfy these goals. These sub-objective terms are weighted according to their contribution to the optimization problem. The SDC-NMPC framework is developed using the high-performance language C++ and utilizes highly optimized math and optimization libraries for best real-time performance. This makes the SDC-MPC well suited for use in both ADAS and self-driving cars. Extensive simulation studies featuring complex tracks with many sharp turns have been carried out to evaluate the performance of the proposed SDC-NMPC at different speeds. The presented analysis shows that the proposed controller with its tuning technique outperforms that of the PID-based controller.