Tri-HGNN: Learning triple policies fused hierarchical graph neural networks for pedestrian trajectory prediction
文献类型：期刊论文
| 作者 | Zhu, Wenjun2 ; Liu, Yanghong2; Wang, Peng3; Zhang, Mengyi2; Wang, Tian1; Yi, Yang2
|
| 刊名 | PATTERN RECOGNITION
 |
| 出版日期 | 2023-11-01 |
| 卷号 | 143页码:11 |
| ISSN号 | 0031-3203 |
| 关键词 | Trajectory prediction Hierarchical policy Graph neural networks |
| DOI | 10.1016/j.patcog.2023.109772 |
| 通讯作者 | Yi, Yang(yiyang@njtech.edu.cn) |
| 英文摘要 | In complex and dynamic urban traffic scenarios, the accurate trajectory prediction of surrounding pedes-trians with interactive behaviors plays a vital role in the self-driving system. Intrinsic factors and extrinsic factors will inevitably influence the pedestrians trajectory. Intrinsic factors such as pedestrians diversified intentions bring rich and diverse multi-modal future possibilities. Besides, extrinsic factors affecting the future trajectory are accompanied by context semantics such as interactions among pedestrians. However, most of the existing methods discuss two problems (interaction and intention) separately. Considering both two factors impact the trajectory of pedestrians, a Triple Policies Fused Hierarchical Graph Neural Networks (Tri-HGNN) is proposed to model spatial and temporal interactions and intentions among the whole scene of pedestrians at each time step and predict the multiple future trajectories. Tri-HGNN con-tains three different policies: (i) Extrinsic-level policy is used to extract spatial nodes embedding from the interaction graph of pedestrian trajectories by using the Graph Attention Network. (ii) Intrinsic-level policy adopts the Graph Convolutional Network to infer the human intention for more accurate predic-tion. Moreover, human intention is influenced by the intrinsic interaction generated among pedestrians, so we fuse the interaction features to grasp the influence of the extrinsic interaction. (iii) Basic-level pol-icy then integrates the heuristic information obtained from other two policies and concatenates it with historical trajectories to make multiple predictions through Temporal Convolution Network. Experimen-tal results show that our model improves performance compared with state-of-the-art methods on the ETH/UCY and SDD benchmarks.& COPY; 2023 Elsevier Ltd. All rights reserved. |
| WOS关键词 | ATTENTION |
| 资助项目 | National Key Research and Development Program of China[2021YFB3301300] ; Natural Science Foundation of the Higher Education Institutions of Jiangsu Province of China ; Postgraduate Research amp; Practice Innovation Program of Jiangsu Province[21KJB520007] ; [KYCX23_1450] |
| WOS研究方向 | Computer Science ; Engineering |
| 语种 | 英语 |
| 出版者 | ELSEVIER SCI LTD |
| WOS记录号 | WOS:001039043900001 |
| 资助机构 | National Key Research and Development Program of China ; Natural Science Foundation of the Higher Education Institutions of Jiangsu Province of China ; Postgraduate Research amp; Practice Innovation Program of Jiangsu Province |
| 源URL | [http://ir.ia.ac.cn/handle/173211/53816]  |
| 专题 | 多模态人工智能系统全国重点实验室 |
| 通讯作者 | Yi, Yang |
| 作者单位 | 1.Beihang Univ, Inst Artificial Intelligence, Beijing 100083, Peoples R China 2.Nanjing Tech Univ, Inst Elect Engn & Control Sci, Nanjing 211816, Peoples R China 3.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhu, Wenjun,Liu, Yanghong,Wang, Peng,et al. Tri-HGNN: Learning triple policies fused hierarchical graph neural networks for pedestrian trajectory prediction[J]. PATTERN RECOGNITION,2023,143:11. |
| APA | Zhu, Wenjun,Liu, Yanghong,Wang, Peng,Zhang, Mengyi,Wang, Tian,&Yi, Yang.(2023).Tri-HGNN: Learning triple policies fused hierarchical graph neural networks for pedestrian trajectory prediction.PATTERN RECOGNITION,143,11. |
| MLA | Zhu, Wenjun,et al."Tri-HGNN: Learning triple policies fused hierarchical graph neural networks for pedestrian trajectory prediction".PATTERN RECOGNITION 143(2023):11. |
入库方式： OAI收割
来源：自动化研究所
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