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Research Article | Open Access

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

Lan Yang, Jiaqi Yuan( ), Xiangmo Zhao, Shan Fang, Zeyu He, Jiahao Zhan, Zhiqiang Hu, Xia Li
School of Information Engineering, Chang’an University, Xi’an 710064, China
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Abstract

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 scenario coverage. Therefore, scenario-based autonomous vehicle simulation testing has emerged. Many scenarios form the basis of simulation testing. Generating additional scenarios from an existing scenario library is a significant problem. Taking the scenarios of a proceeding vehicle cutting into an adjacent lane on highways as an example, based on an autoencoder and a generative adversarial network (GAN), a method that combines Transformer to capture the features of a long-time series, called SceGAN, is proposed to model and generate scenarios of autonomous vehicles on highways. An evaluation system is established to analyze the reliability of SceGAN using discriminative and predictive scores and further evaluate the effect of scenario generation in terms of similarity and coverage. Experiments showed that compared with TimeGAN and AEGAN, SceGAN is superior in data fidelity and availability, and their similarity increased by 27.22% and 21.39%, respectively. The coverage increased from 79.84% to 93.98% as generated scenarios increased from 2,547 to 50,000, indicating that the proposed method has a strong generalization capability for generating multiple trajectories, providing a basis for generating test scenarios and promoting autonomous vehicle testing.

Keywords

transformergenerative adversarial network (GAN)scenario generationautonomous vehicles testingcut-in

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Journal of Intelligent and Connected Vehicles
Volume 6 Issue 4,
December 2023
Pages 264-274
DOI: 10.26599/JICV.2023.9210023
Cite this article:
Yang L, Yuan J, Zhao X, et al. SceGAN: A method for generating autonomous vehicle cut-in scenarios on highways based on deep learning. Journal of Intelligent and Connected Vehicles, 2023, 6(4): 264-274. https://doi.org/10.26599/JICV.2023.9210023

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Received: 08 September 2023
Revised: 14 September 2023
Accepted: 18 September 2023
Published: 30 December 2023
© The author(s) 2023.

This is an open access article under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
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