Convolutional Neural Network in Turn Recognition Tasks for Electric Transport Safety
2017 IEEE 58th International Scientific Conference on Power and Electrical Engineering of Riga Technical University (RTUCON 2017) 2017
Anna Beinaroviča, Mihails Gorobecs, Anatolijs Ļevčenkovs

This study is dedicated to solve a turn recognition task by Convolutional neural network (CNN) application. System proposed in this paper is a part of the big research aimed at system, for the safe transportation process, development. Road turn is an important parameter in road circumstances analysing, collision possibility calculating and decision making for the accident prevention. System structure for turn recognition process and CNN algorithm with training is proposed in this paper. The experimental results validate the effectiveness of proposed algorithm. Experiments show that CNN correctly defines turn after training is done.


Atslēgas vārdi
convolutional, neural network, algorithm, transportation, safety, object recognition, electric vehicles, vehicle safety, collision avoidance
DOI
10.1109/RTUCON.2017.8124785
Hipersaite
http://ieeexplore.ieee.org/document/8124785/

Beinaroviča, A., Gorobecs, M., Ļevčenkovs, A. Convolutional Neural Network in Turn Recognition Tasks for Electric Transport Safety. No: 2017 IEEE 58th International Scientific Conference on Power and Electrical Engineering of Riga Technical University (RTUCON 2017), Latvija, Riga, 12.-13. oktobris, 2017. Piscataway: IEEE, 2017, 231.-236.lpp. ISBN 978-1-5386-3847-7. e-ISBN 978-1-5386-3846-0. Pieejams: doi:10.1109/RTUCON.2017.8124785

Publikācijas valoda
English (en)
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