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    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

    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 >

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