CoL-GAN: Plausible and Collision-Less Trajectory Prediction by Attention-Based GAN

doi:10.1109/ACCESS.2020.2987072

CORC > 计算技术研究所 > 中国科学院计算技术研究所 > 中国科学院计算技术研究所期刊论文 > 英文

	CoL-GAN: Plausible and Collision-Less Trajectory Prediction by Attention-Based GAN
	Liu, Shaohua 2,3; Liu, Haibo 3; Bi, Huikun 1; Mao, Tianlu 1
刊名	IEEE ACCESS
	2020
卷号	8 页码:101662-101671
关键词	Trajectory Generative adversarial networks Gallium nitride Predictive models Collision avoidance Decoding Generators Trajectory prediction generative adversarial network deep learning
ISSN号	2169-3536
DOI	10.1109/ACCESS.2020.2987072
英文摘要	Predicting plausible and collisionless trajectories is critical in various applications, such as robotic navigation and autonomous driving. This is a challenging task due to two major factors. First, it is difficult for deep neural networks to understand how pedestrians move to avoid collisions and how they react to each other. Second, given observed trajectories, there are multiple possible and plausible trajectories followed by pedestrians. Although an increasing number of previous works have focused on modeling social interactions and multimodality, the trajectories generated by these methods still lead to many collisions. In this work, we propose CoL-GAN, a new attention-based generative adversarial network using a convolutional neural network as a discriminator, which is able to generate trajectories with fewer collisions. Through experimental comparisons with prior works on publicly available datasets, we demonstrate that Col-GAN achieves state-of-the-art performance in terms of accuracy and collision avoidance.
资助项目	National Key Research and Development Program of China[2017YFC0804900] ; National Natural Science Foundation of China[61532002] ; Major Program of National Natural Science Foundation of China[91938301] ; National Defense Equipment Advance Research Shared Technology Program of China[41402050301-170441402065] ; Sichuan Science and Technology Major Project on New Generation Artificial Intelligence[2018GZDZX0034]
WOS研究方向	Computer Science ; Engineering ; Telecommunications
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:000546406500047
内容类型	期刊论文
源URL	[http://119.78.100.204/handle/2XEOYT63/15117]
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Mao, Tianlu
作者单位	1.Chinese Acad Sci, Inst Comp Technol, Beijing Key Lab Mobile Comp & Pervas Device, Beijing 100190, Peoples R China 2.Univ Elect Sci & Technol China, Inst Elect & Informat Engn Guangdong, Dongguan 523808, Peoples R China 3.Beijing Univ Posts & Telecommun, Sch Elect Engn, Beijing 100876, Peoples R China
推荐引用方式 GB/T 7714	Liu, Shaohua,Liu, Haibo,Bi, Huikun,et al. CoL-GAN: Plausible and Collision-Less Trajectory Prediction by Attention-Based GAN[J]. IEEE ACCESS,2020,8:101662-101671.
APA	Liu, Shaohua,Liu, Haibo,Bi, Huikun,&Mao, Tianlu.(2020).CoL-GAN: Plausible and Collision-Less Trajectory Prediction by Attention-Based GAN.IEEE ACCESS,8,101662-101671.
MLA	Liu, Shaohua,et al."CoL-GAN: Plausible and Collision-Less Trajectory Prediction by Attention-Based GAN".IEEE ACCESS 8(2020):101662-101671.