Knowledge Commons of Institute of Automation,CAS
AST-GNN: An attention-based spatio-temporal graph neural network for Interaction-aware pedestrian trajectory prediction | |
Zhou, Hao1,4; Ren, Dongchun2; Xia, Huaxia2; Fan, Mingyu2,3; Yang, Xu4; Huang, Hai1 | |
发表期刊 | NEUROCOMPUTING |
ISSN | 0925-2312 |
2021-07-20 | |
卷号 | 445页码:298-308 |
通讯作者 | Yang, Xu(xu.yang@ia.ac.cn) ; Huang, Hai(haihus@163.com) |
摘要 | Predicting pedestrian trajectories in the future is a basic research topic in many real applications, such as video surveillance, self-driving cars, and robotic systems. There are two major challenges in this task, the complex interaction modeling among pedestrians and the unique motion pattern extraction for each pedestrian. Regarding the two challenges, an attention-based interaction-aware spatio-temporal graph neural network is proposed for predicting pedestrian trajectories. There are two components in the proposed method: spatial graph neural network for interaction modeling, and temporal graph neural network for motion feature extraction. Spatial graph neural network uses an attention mechanism to capture the spatial interactions among all the pedestrians at each time step. Meanwhile, temporal graph neural network uses an attention mechanism to capture the temporal motion pattern of each pedestrian. Finally, a time-extrapolator convolutional neural network is used in the temporal dimension of the aggregated graph features to predict the future trajectories. Experimental results on two benchmark pedestrian trajectory prediction datasets demonstrate the competitive performances of the proposed method in terms of both the final displace error and the average displacement error metrics as compared with state-of-the-art trajectory prediction methods. (C) 2021 Elsevier B.V. All rights reserved. |
关键词 | Pedestrian trajectory prediction Graph neural networks Spatio-temporal graph Graph attention |
DOI | 10.1016/j.neucom.2021.03.024 |
收录类别 | SCI |
语种 | 英语 |
资助项目 | Beijing Nova Program[Z201100006820046] ; National Naturral Science Foundation of China[61973301] ; National Naturral Science Foundation of China[61972020] ; National Naturral Science Foundation of China[61633009] ; National Naturral Science Foundation of China[61772373] ; National Naturral Science Foundation of China[51579053] ; National Naturral Science Foundation of China[U1613213] ; National Key R&D Program of China[2016YFC0300801] ; National Key R&D Program of China[2017YFB1300202] ; Field Fund of the 13th FiveYear Plan for Equipment Preresearch Fund[61403120301] ; Key Basic Research Project of Shanghai Science and Technology Innovation Plan[15JC1403300] ; Beijing Science and Technology Project[Z181100008918018] ; Meituan Open RD Fund |
项目资助者 | Beijing Nova Program ; National Naturral Science Foundation of China ; National Key R&D Program of China ; Field Fund of the 13th FiveYear Plan for Equipment Preresearch Fund ; Key Basic Research Project of Shanghai Science and Technology Innovation Plan ; Beijing Science and Technology Project ; Meituan Open RD Fund |
WOS研究方向 | Computer Science |
WOS类目 | Computer Science, Artificial Intelligence |
WOS记录号 | WOS:000652811700007 |
出版者 | ELSEVIER |
七大方向——子方向分类 | 无人系统 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/45212 |
专题 | 多模态人工智能系统全国重点实验室_机器人理论与应用 |
通讯作者 | Yang, Xu; Huang, Hai |
作者单位 | 1.Harbin Engn Univ, Natl Key Lab Sci & Technol Underwater Vehicle, Harbin, Peoples R China 2.Meituan, Beijing, Peoples R China 3.Wenzhou Univ, Coll Comp Sci & Artificial Intelligence, Wenzhou, Peoples R China 4.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing, Peoples R China |
第一作者单位 | 中国科学院自动化研究所 |
通讯作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Zhou, Hao,Ren, Dongchun,Xia, Huaxia,et al. AST-GNN: An attention-based spatio-temporal graph neural network for Interaction-aware pedestrian trajectory prediction[J]. NEUROCOMPUTING,2021,445:298-308. |
APA | Zhou, Hao,Ren, Dongchun,Xia, Huaxia,Fan, Mingyu,Yang, Xu,&Huang, Hai.(2021).AST-GNN: An attention-based spatio-temporal graph neural network for Interaction-aware pedestrian trajectory prediction.NEUROCOMPUTING,445,298-308. |
MLA | Zhou, Hao,et al."AST-GNN: An attention-based spatio-temporal graph neural network for Interaction-aware pedestrian trajectory prediction".NEUROCOMPUTING 445(2021):298-308. |
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