Fényes, Dániel (MTA SZTAKI Institute for Computer Science and Control), Nemeth, Balazs (MTA SZTAKI), Gaspar, Peter (MTA SZTAKI)

A Predictive Control for Autonomous Vehicles Using Big Data Analysis

Scheduled for presentation during the Regular Session "Autonomous Vehicle Control" (TuAT1), Tuesday, June 25, 2019, 10:30−10:50, Chambord

9th IFAC International Symposium on Advances in Automotive Control, June 23-27, 2019, Orléans, France

This information is tentative and subject to change. Compiled on April 25, 2024

Abstract

Big data analysis has an increasing importance in the field of the autonomous vehicles. It is related to the vehicular networks and the individual control. The paper proposes the improvement of the lateral autonomous vehicle control design through the big data analysis on the measured signals. Based on the data a decision tree has been generated by using the C4.5 and the MetaCost algorithms. It results in the regions of vehicle dynamic states and the path following of the autonomous vehicle. The lateral control problem is formed in an MPC (Model Predictive Control) structure, in which the results of the big data analysis are built as constraints. The efficiency of the proposed method is illustrated through a comparative simulation example through a high-fidelity vehicle control software.