A predictive control for autonomous vehicles using big data analysis

Fényes, Dániel and Németh, Balázs and Gáspár, Péter (2019) A predictive control for autonomous vehicles using big data analysis. In: 9th IFAC International Symposium on Advances in Automotive Control, AAC, 2019, 2019.06.23-2019.06.27, Orléans.

Text Apredictivecontrolforautonomousvehiclesusingbigdataanalysis.pdf Restricted to Repository staff only Download (500kB) | Request a copy

Abstract

Big data analysis has an increasing importance in the field of the autonomous vehicles. It is related to vehicular networks and individual control. The paper proposes the improvement of a lateral autonomous vehicle control design through big data analysis on the measured signals. Based on the data a decision tree is generated by using the C4.5 and the MetaCost algorithms. It results in the regions of vehicle dynamic states and guarantees the tracking 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-fidelity vehicle control software.

Actions (login required)

Edit Item