Please use this identifier to cite or link to this item:https://hdl.handle.net/20.500.12259/45620
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): Vidugirienė, Aušra;Tamošiūnaitė, Minija
Title: Parameter anglysis for steering angle prediction using neural networks
Is part of: Transport means 2007 : 11th international conference, October 18-19, 2007, Kaunas: proceedings. Kaunas : Kaunas University of Technology, 2007, no. 7
Extent: p. 111-113
Date: 2007
Keywords: Sreering angle prediction;Road curvature;Country road;Driving assistance systems
Abstract: The paper presents analysis of steering angle prediction using neural networks on a curved country road. Data from real car driving on a country road is used both for training and testing of a neural network. Among parameters current steering angle, current speed of the car, and curve in front of the car are analyzed. The study aims to add to development of driver assistance systems for country road driving. Steering rules for flat and sharp curves are compared. Results show that predictions of sharp curve steering on a test set is much more accurate if learning was performed on a sharp curve, and predictions for the flat curve are not so sensitive to the learning set, but is to some extent better if learning is made using flat curve data. Further, steering prediction parameters for flat and sharp curves are compared. For flat curves “looking ahead” of 1s works best, while for sharp curves “looking ahead” of 0.5 provides the optimal prediction. Yet, the differences in error in the flat curve case were not large, and the optimum was shallow, while for the sharp curve it was much sharper
Internet: https://hdl.handle.net/20.500.12259/45620
Affiliation(s): Informatikos fakultetas
Vytauto Didžiojo universitetas
Appears in Collections:Universiteto mokslo publikacijos / University Research Publications

Files in This Item:
marc.xml10.98 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)

87
checked on May 1, 2021

Download(s)

8
checked on May 1, 2021

Google ScholarTM

Check


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