CoL-GAN: Plausible and Collision-Less Trajectory Prediction by Attention-Based GAN
文献类型：期刊论文
| 作者 | Liu, Shaohua2,3; Liu, Haibo3; Bi, Huikun1; Mao, Tianlu1 |
| 刊名 | IEEE ACCESS
 |
| 出版日期 | 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 |
| WOS记录号 | WOS:000546406500047 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 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. |
入库方式： OAI收割
来源：计算技术研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。