SceGAN: A method for generating autonomous vehicle cut-in scenarios on highways based on deep learning

SceGAN: A method for generating autonomous vehicle cut-in scenarios on highways based on deep learning

With the increasing level of automation of autonomous vehicles, it is important to conduct comprehensive and extensive testing before releasing autonomous vehicles into the market. Traditional public road and closed-field testing failed to meet the requirements of high testing efficiency and scenari...

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Bibliographic Details
Main Authors: Lan Yang, Jiaqi Yuan, Xiangmo Zhao, Shan Fang, Zeyu He, Jiahao Zhan, Zhiqiang Hu, Xia Li
Format: Article
Language:English
Published: Tsinghua University Press 2023-12-01
Series:Journal of Intelligent and Connected Vehicles
Subjects:
scenario generation
autonomous vehicles testing
cut-in
transformer
generative adversarial network (gan)
Online Access:https://www.sciopen.com/article/10.26599/JICV.2023.9210023

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