A robust path tracking algorithm for connected and automated vehicles under i-VICS

Abstract

Connected and automated vehicle (CAV) can obtain the precise vehicle location information via vehicle to infrastructure (V2I) communication to achieve the path tracking task under the intelligent vehicle-infrastructure cooperative system (i-VICS). However, in some special scenarios, the location information of vehicles might become inaccurate or lost, e.g., the vehicle loses contact with the road-side unit (RSU). To improve the reliability of vehicle location information, we concentrate on solving two kinds of tracking problems, i.e., vehicle location information is inaccurate and lost in this paper. First, a predicted method using vehicle dynamics is presented to recognize the scenarios in which the vehicle location information becomes inaccurate when the vehicle moves. Then, a modified Kalman filter is employed to adapt to a more complex scenario with variable position error. After that, a multilayer perceptron (MLP) model is designed to predict vehicle location information when vehicle location information is lost. At last, simulation results demonstrate that the proposed methods have a marked influence on handling the driving scenario and make our path tracking algorithm more robust.