Lateral Control Design for Autonomous Vehicles Using a Big Data-Based Approach

Abstract

In the paper an improved Model Predictive Control (MPC) design is presented for autonomous vehicles. The improvement of the control design is based on big data analysis of the lateral vehicle dynamics. In the big data analysis, the decision tree algorithm, C4.5 is used to determine the stable regions of the vehicle. Moreover, C4.5 is extended with the MetaCost algorithm, which is able to weight the percentages of certain misclassifications. In this way, the safe motion of the vehicle can be guaranteed. The results of the big data analysis are states-sets, which are used as constraints in the MPC control design.