Motion Planning and Robust Output-Feedback Trajectory Tracking Control for Multiple Intelligent and Connected Vehicles in Unsignalized Intersections
Fernando Viadero‐Monasterio, Miguel Meléndez‐Useros, Nianhua Zhang, Hui Zhang, Beatriz L. Boada, María Jesús López Boada
Abstract
This paper addresses the problem of integrated motion planning and trajectory tracking for multiple intelligent vehicles in unsignalized intersections. A depth-first spanning tree method is employed to determine the optimal sequence for vehicle traversal across the intersection. It must be acknowledged that the strategy presented here does not guarantee the prevention of collisions. Therefore, a centralized planning algorithm is designed for collision-free planning of speed profiles for each vehicle. It is essential that vehicles are able to follow the designated routes with precision to avoid the creation of additional conflict points at intersections. To address this issue, a novel trajectory tracking method is introduced. Given the inherent limitations in measuring each vehicle state, and the necessity of constraining both the input and the vehicle states to ensure system stability, this work derives the linear matrix inequality conditions for the design of a robust static output- feedback trajectory tracking controller, taking into account the aforementioned constraints. In order to validate the proposed method, cooperative driving in intersections has been evaluated under co-simulations with Simulink and Unreal Engine. In comparison to alternative state-of-the-art methodologies, the proposed method has the potential to reduce the time of arrival of the last vehicle by up to 25.63%. Furthermore, the lateral deviation and velocity tracking errors have been reduced by up to 50%.