Fast Online Computation of MPC-Based Integrated Decision Control for Autonomous Vehicles

Abstract

In the paper, an integrated decision control (IDC) architecture has been introduced, seamlessly integrating autonomous decision-making and motion control into a unified processing framework. This architecture primarily comprises two key modules: a static path planner and a MPC-based dynamic optimal tracker. The former exclusively utilizes static information, such as road geometry, roadside signs, and road markings, to formulate a candidate path set. Building upon this foundation, the latter autonomously determines the most suitable driving path from the candidate paths. It integrates vehicle dynamics with dynamic information, including traffic participants and traffic lights, to design a constrained trajectory tracking controller for achieving precise motion control. Furthermore, from an engineering practice perspective, a dimension reduction control strategy for both control inputs and system constraints has been devised to enhance the real-time performance of the IDC system. Experimental results affirm that the proposed strategy effectively facilitates autonomous and secure driving of vehicles in open road traffic environments.