Model Predictive Control for Safe Autonomous Driving Applications

Book cover AI-enabled Technologies for Autonomous and Connected Vehicles pp 255–282Cite as Model Predictive Control for Safe Autonomous Driving Applications Ivo Batkovic, Mario Zanon & Paolo Falcone Chapter First Online: 08 September 2022 115 Accesses Part of the Lecture Notes in Intelligent Transportation and Infrastructure book series (LNITI) Abstract Although Model Predictive Control is widely used in motion planning and control for autonomous driving applications, accommodating closed-loop stability with respect to an arbitrary reference trajectory and avoidance of pop-up or moving obstacles is still an open problem. While it is well-known how to design a closed-loop stable MPC with respect to a reference trajectory that satisfies the system dynamics, this chapter discusses how to guarantee stability of a vehicle motion planner and controller when a user-provided arbitrary reference is used. Furthermore, the proposed MPC scheme enables recursive collision-avoidance constraint satisfaction in the presence of pop-up or moving obstacles (e.g., pedestrians, cyclists, human-driven vehicles), provided that their predicted future motion trajectory is available together with some uncertainty bound and satisfies some mild requirement. The proposed motion planner and controller is demonstrated through simulations.