Please use this identifier to cite or link to this item:
Type of publication: research article
Type of publication (PDB): Straipsnis konferencijos medžiagoje Clarivate Analytics Web of Science ar/ir Scopus / Article in Clarivate Analytics Web of Science or Scopus DB conference proceedings (P1a)
Field of Science: Informatika / Informatics (N009)
Author(s): Demčenko, Andriejus;Tamošiūnaitė, Minija;Vidugirienė, Aušra;Saudargienė, Aušra
Title: Vehicle's steering signal predictions using neural networks
Is part of: Intelligent vehicles symposium: 2008 (IV) IEEE, Eindhoven, Netherlands, June 4-6, 2008. New York : IEEE Press, 2008
Extent: p. 338-343
Date: 2008
Series/Report no.: (IEEE Intelligent Vehicles Symposium. Vol. 1-3 1931-0587)
Keywords: Steering signal predictions;Neural networks;Back propagation;Extreme learning machine
ISBN: 9781424425686
Abstract: Back-propagation trained neural networks, as well as extreme learning machine (ELM) were used to predict car driver psila s steering behavior, based on road curvature, velocity and acceleration of a car. Predictions were performed using real-road data, obtained on a test car in a country-road scenario. We made a simplification using gyroscopically measured curvature of the road instead of visually extracted curvature measures. It was found that an optimum exists how far one has to look onto a curvature signal, according to neural network prediction accuracy. Velocity and acceleration did not improve steering signal prediction accuracy in our framework. Traditional neural networks and ELM performed similarly in terms of prediction errors
Affiliation(s): Informatikos fakultetas
Taikomosios informatikos katedra
Vytauto Didžiojo universitetas
Appears in Collections:Universiteto mokslo publikacijos / University Research Publications

Files in This Item:
marc.xml12.33 kBXMLView/Open

MARC21 XML metadata

Show full item record
Export via OAI-PMH Interface in XML Formats
Export to Other Non-XML Formats

CORE Recommender

Page view(s)

checked on May 1, 2021


checked on May 1, 2021

Google ScholarTM



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.