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

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 prob-lems, i.e., vehicle location information is inaccurate and lost in this paper. First, a predicted method using vehi-cle 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 track-ing algorithm more robust.