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공지사항
논문명 |
Driving data generation using affinity propagation, data augmentation, and convolutional neural network in communication system |
논문종류 |
SCI |
저자 |
Weiqiang Zhang, Phuong Minh Chu, Kaisi Huang, Kyungeun Cho |
Impact Factor |
1.319 |
게재학술지명 |
International Journal of Communication Systems |
게재일 |
2021.01 |
In vehicle‐driving simulation‐based communication systems, vehicles are
always driven according to predefined driving styles. However, in the real
world, various driving styles exist. To simulate various types of drivers in driving simulation systems, a new driving‐data generation method is required. This
paper proposes a method that generates a realistic vehicle‐driving model. The
data augmentation method is utilized to expand the driving dataset, and then
the expanded driving data are clustered into several groups. The clustered driving data are inputted into a convolutional neural network to train a driving
model. The driving model is utilized to classify another driving dataset into
some categories. The driving data within the same categories are utilized to
generate new driving data by combining the properties of the driving data.
The new driving data thus generated is applied to a vehicle, which can be utilized in virtual driving simulation systems |
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