Data Augmentation Technology Driven By Image Style Transfer in Self-Driving Car Based on End-to-End Learning
Dongjie Liu1, Jin Zhao1, *, Axin Xi2, Chao Wang1, Xinnian Huang1, Kuncheng Lai1, Chang Liu1
CMES-Computer Modeling in Engineering & Sciences, Vol.122, No.2, pp. 593-617, 2020, DOI:10.32604/cmes.2020.08641
- 09 February 2020
Abstract With the advent of deep learning, self-driving schemes based on deep learning
are becoming more and more popular. Robust perception-action models should learn
from data with different scenarios and real behaviors, while current end-to-end model
learning is generally limited to training of massive data, innovation of deep network
architecture, and learning in-situ model in a simulation environment. Therefore, we
introduce a new image style transfer method into data augmentation, and improve the
diversity of limited data by changing the texture, contrast ratio and color of the image,
and then it is extended to the scenarios… More >