Data-Driven Reachability Analysis for the Reconfiguration of Vehicle Control Systems

Dániel Fényes, Balázs Németh, P. Gáspár

Research output: Contribution to journalArticle

Abstract

The paper presents a reconfigurable control strategy for the lateral stability of autonomous vehicles. The control strategy is based on the analysis of big data, which are provided by the sensor networks of autonomous vehicles. The core of the analysis method is a machine learning algorithm, with which the impacts of various vehicle signals on the lateral dynamics have been examined. In the analysis several scenarios with faults in the steering and in-wheel systems are considered using a high-fidelity simulation software. The results of the examination are built into the fault-tolerant reconfiguration strategy.

Original languageEnglish
Pages (from-to)831-836
Number of pages6
JournalIFAC-PapersOnLine
Volume51
Issue number24
DOIs
Publication statusPublished - Jan 1 2018

Fingerprint

Control systems
Learning algorithms
Sensor networks
Learning systems
Wheels
Big data

Keywords

  • autonomous vehicle systems
  • big data analysis
  • reconfiguration strategy

ASJC Scopus subject areas

  • Control and Systems Engineering

Cite this

Data-Driven Reachability Analysis for the Reconfiguration of Vehicle Control Systems. / Fényes, Dániel; Németh, Balázs; Gáspár, P.

In: IFAC-PapersOnLine, Vol. 51, No. 24, 01.01.2018, p. 831-836.

Research output: Contribution to journalArticle

Fényes, Dániel ; Németh, Balázs ; Gáspár, P. / Data-Driven Reachability Analysis for the Reconfiguration of Vehicle Control Systems. In: IFAC-PapersOnLine. 2018 ; Vol. 51, No. 24. pp. 831-836.
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