A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf
.
Data Augmentation Technology Driven By Image Style Transfer in Self-Driving Car Based on End-to-End Learning
2020
CMES - Computer Modeling in Engineering & Sciences
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
doi:10.32604/cmes.2020.08641
fatcat:dsaa2jjv35fz7bzk6nfxq46jxu