Interval type-2 fuzzy system in personalized driving cycle forecasting

Adrienn Dineva, Balázs Tusor, István Vajda

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

For intelligent driver assistance systems the prediction of the future driving cycle is fundamental. Recent recommendations for driving cycle standards does not provide precise information on the expected intermittent operations of the vehicle and can not be applied directly in an intelligent energy management and driving assistance system. Latest driver assistance systems require more sophisticated solutions which are able to incorporate the personal driving style. The Interval-type 2 Fuzzy System is proved to be a higly efficient tool for modeling uncertainties. In contrast to conventional Type-1 fuzzy modeling an IT2 Fuzzy System has the ability to deal with flexible with the various types of uncertainties and modeling errors simultaneously and approximates better real-life systems. This paper presents an IT2 Fuzzy System for personalized driving cycle forecasting from the measured velocity and acceleration data. The proposed method applies a Mamdani type IT2 fuzzy inference technique. The fuzzy sets and rules are built up by extracting knowledge from real driving dataset. Simulation results have shown that the presented technique is efficient and ensures satisfactory performance.

Original languageEnglish
Title of host publicationMathematical Methods and Computational Techniques in Science and Engineering II
EditorsNikos Bardis
PublisherAmerican Institute of Physics Inc.
ISBN (Electronic)9780735416987
DOIs
Publication statusPublished - Jul 27 2018
Event2nd International Conference on Mathematical Methods and Computational Techniques in Science and Engineering - Cambridge, United Kingdom
Duration: Feb 16 2018Feb 18 2018

Publication series

NameAIP Conference Proceedings
Volume1982
ISSN (Print)0094-243X
ISSN (Electronic)1551-7616

Other

Other2nd International Conference on Mathematical Methods and Computational Techniques in Science and Engineering
CountryUnited Kingdom
CityCambridge
Period2/16/182/18/18

ASJC Scopus subject areas

  • Physics and Astronomy(all)

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  • Cite this

    Dineva, A., Tusor, B., & Vajda, I. (2018). Interval type-2 fuzzy system in personalized driving cycle forecasting. In N. Bardis (Ed.), Mathematical Methods and Computational Techniques in Science and Engineering II [020027] (AIP Conference Proceedings; Vol. 1982). American Institute of Physics Inc.. https://doi.org/10.1063/1.5045433