Static-dynamic global graph representation for pedestrian trajectory prediction
文献类型：期刊论文
| 作者 | Zhou, Hao1,2; Yang, Xu1 ; Fan, Mingyu4; Huang, Hai2; Ren, Dongchun3; Xia, Huaxia3
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| 刊名 | KNOWLEDGE-BASED SYSTEMS
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| 出版日期 | 2023-10-09 |
| 卷号 | 277页码:13 |
| 关键词 | Trajectory prediction Social interaction Global graph representation |
| ISSN号 | 0950-7051 |
| DOI | 10.1016/j.knosys.2023.110775 |
| 通讯作者 | Fan, Mingyu(fanmingyu@dhu.edu.cn) ; Huang, Hai(haihus@163.com) |
| 英文摘要 | Effectively understanding social interactions among pedestrians plays a significant role in accurate pedestrian trajectory prediction. Previous works used either distance-based or data-driven methods to model interactions. However, the distance-based method has difficulty modeling complex interactions and ignores interactive pedestrians that are beyond a certain distance. The data-driven method models interactions among all pedestrians in a scene and introduces noninteractive pedestrians into the model due to the lack of proper supervision. To overcome these limitations, we first propose a novel global graph representation, which considers the spatial distance (from near to far) and the motion state (from static to dynamic), to explicitly model the social interactions among pedestrians. The global graph representation consists of two subgraphs: the static and the dynamic graph representations, where the static graph considers only the nearby pedestrians within a certain distance threshold, and the dynamic graph considers the interactive pedestrians that will likely collide soon. The proposed graph representation explicitly models the interaction by incorporating both the static (location) and dynamic states (velocity) in a distance-based manner. Then, based on the global graph representation, a novel data driven graph encoding network is proposed to extract the interaction features. It adopts two independent LSTMs and an attention module to encode the interaction feature from the perspective of the ego-pedestrian. Finally, the proposed prediction method is evaluated on two benchmark pedestrian trajectory prediction datasets, and comparisons are made with the state-of-the-arts. Experimental results demonstrate the effectiveness of the proposed method.& COPY; 2023 Elsevier B.V. All rights reserved. |
| WOS关键词 | BEHAVIOR ; MODEL |
| 资助项目 | Beijing Nova Program, China[Z201100006820046] ; National Natural Science Foundation of China[61973301] ; National Natural Science Foundation of China[61972020] ; National Natural Science Foundation of China[61633009] ; National Natural Science Foundation of China[61772373] ; National Natural Science Foundation of China[51579053] ; National Natural Science Foundation of China[U1613213] ; National Key Ramp;D Program of China[2016YFC0300801] ; 13th Five-Year Plan for Equipment Pre-research Fund ; Key Basic Research Project of Shanghai Science and Technology Innovation Plan ; Beijing Science and Technology Project ; Meituan Open Ramp;D Fund ; [2017YFB1300202] ; [61403120301] ; [15JC1403300] ; [Z181100008918018] |
| WOS研究方向 | Computer Science |
| 语种 | 英语 |
| WOS记录号 | WOS:001048520100001 |
| 出版者 | ELSEVIER |
| 资助机构 | Beijing Nova Program, China ; National Natural Science Foundation of China ; National Key Ramp;D Program of China ; 13th Five-Year Plan for Equipment Pre-research Fund ; Key Basic Research Project of Shanghai Science and Technology Innovation Plan ; Beijing Science and Technology Project ; Meituan Open Ramp;D Fund |
| 源URL | [http://ir.ia.ac.cn/handle/173211/54014]  |
| 专题 | 多模态人工智能系统全国重点实验室 |
| 通讯作者 | Fan, Mingyu; Huang, Hai |
| 作者单位 | 1.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China 2.Harbin Engn Univ, Coll Shipbldg Engn, Harbin 150001, Peoples R China 3.Intelligent Transportat Div, Beijing 100102, Peoples R China 4.Donghua Univ, Inst Artificial Intelligence, Shanghai 200051, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhou, Hao,Yang, Xu,Fan, Mingyu,et al. Static-dynamic global graph representation for pedestrian trajectory prediction[J]. KNOWLEDGE-BASED SYSTEMS,2023,277:13. |
| APA | Zhou, Hao,Yang, Xu,Fan, Mingyu,Huang, Hai,Ren, Dongchun,&Xia, Huaxia.(2023).Static-dynamic global graph representation for pedestrian trajectory prediction.KNOWLEDGE-BASED SYSTEMS,277,13. |
| MLA | Zhou, Hao,et al."Static-dynamic global graph representation for pedestrian trajectory prediction".KNOWLEDGE-BASED SYSTEMS 277(2023):13. |
入库方式： OAI收割
来源：自动化研究所
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