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Journal Article Development of System for Collecting User-specified Training Data for Autonomous Driving Based on Virtual Road Environment
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Authors
Min-Soo Kim, In-Sung Jang
Issue Date
2022-12
Citation
Sensors and Materials, v.34, no.12(5), pp.4813-4825
ISSN
0914-4935
Publisher
MYU K.K.
Language
English
Type
Journal Article
DOI
https://dx.doi.org/10.18494/SAM3966
Abstract
Deep learning technologies that use road images to recognize autonomous driving environments have been actively developed. Such deep-learning-based autonomous driving technologies need a large amount of training data that can represent various road, traffic, and weather environments. However, there have been many difficulties in terms of time and cost in collecting training data that can represent various road environments. Therefore, in this study, we attempt to build a virtual road environment and develop a system for collecting training data based on the virtual environment. To build a virtual environment identical to the real world, we convert and use two kinds of existing geospatial data: high-definition 3D buildings and high-definition roads. We also develop a system for collecting training data running in the virtual environment. The implementation results of the proposed system show that it is possible to build a virtual environment identical to the real world and to collect specific training data quickly and at any time from the virtual environment with various user-specified settings.
KSP Keywords
3D buildings, Amount of training data, Development of system, Geospatial Data, High definition, Learning-based, Real-world, Virtual environment, autonomous driving, deep learning(DL), driving environment
This work is distributed under the term of Creative Commons License (CCL)
(CC BY)
CC BY