A predictive control for autonomous vehicles using big data analysis

Dániel Fényes, Balázs Németh, Péter Gáspar

Research output: Contribution to journalConference article

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.

Original languageEnglish
Pages (from-to)191-196
Number of pages6
JournalIFAC-PapersOnLine
Volume52
Issue number5
DOIs
Publication statusPublished - Jan 1 2019
Event9th IFAC Symposium on Advances in Automotive Control, AAC 2019 - Orléans, France
Duration: Jun 23 2019Jun 27 2019

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Keywords

  • autonomous vehicle control
  • big data analysis
  • decision tree
  • MPC control design

ASJC Scopus subject areas

  • Control and Systems Engineering

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