A Model-Agnostic Hierarchical Framework Towards Trajectory Prediction

doi:10.1007/s11390-023-3013-4

	A Model-Agnostic Hierarchical Framework Towards Trajectory Prediction
	Qian, Tang-Wen 1,2; Wang, Yuan 1,2; Xu, Yong-Jun 1,2; Zhang, Zhao 1,2; Wu, Lin 1,2; Qiu, Qiang 2,3; Wang, Fei 1,2
	2025-03-01
发表期刊	JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY
ISSN	1000-9000
卷号	40 期号:2 页码:322-339
摘要	Predicting the future trajectories of multiple agents is essential for various applications in real life, such as surveillance systems, autonomous driving, and social robots. The trajectory prediction task is influenced by many factors, including the individual historical trajectory, interactions between agents, and the fuzzy nature of the observed agents' motion. While existing methods have made great progress on the topic of trajectory prediction, they treat all the information uniformly, which limits the effectiveness of information utilization. To this end, in this paper, we propose and utilize a model-agnostic framework to regard all the information in a two-level hierarchical view. Particularly, the first-level view is the inter-trajectory view. In this level, we observe that the difficulty in predicting different trajectory samples varies. We define trajectory difficulty and train the proposed framework in an "easy-to-hard" schema. The second-level view is the intra-trajectory level. We find the influencing factors for a particular trajectory can be divided into two parts. The first part is global features, which keep stable within a trajectory, i.e., the expected destination. The second part is local features, which change over time, i.e., the current position. We believe that the two types of information should be handled in different ways. The hierarchical view is beneficial to take full advantage of the information in a fine-grained way. Experimental results validate the effectiveness of the proposed model-agnostic framework.
关键词	spatial-temporal data mining trajectory prediction hierarchical framework model-agnostic
DOI	10.1007/s11390-023-3013-4
收录类别	SCI
语种	英语
资助项目	Youth Innovation Promotion Association of Chinese Academy of Sciences[2023112] ; National Natural Science Foundation of China[62206266] ; China Postdoctoral Science Foundation[2021M703273]
WOS研究方向	Computer Science
WOS类目	Computer Science, Hardware & Architecture ; Computer Science, Software Engineering
WOS记录号	WOS:001483026900016
出版者	SPRINGER SINGAPORE PTE LTD
引用统计
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/40635
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Wang, Fei
作者单位	1.Chinese Acad Sci, Inst Comp Technol, Domain Oriented Intelligent Syst Res Ctr, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100190, Peoples R China 3.Chinese Acad Sci, Inst Comp Technol, CAS Key Lab Network Data Sci & Technol, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Qian, Tang-Wen,Wang, Yuan,Xu, Yong-Jun,et al. A Model-Agnostic Hierarchical Framework Towards Trajectory Prediction[J]. JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY,2025,40(2):322-339.
APA	Qian, Tang-Wen.,Wang, Yuan.,Xu, Yong-Jun.,Zhang, Zhao.,Wu, Lin.,...&Wang, Fei.(2025).A Model-Agnostic Hierarchical Framework Towards Trajectory Prediction.JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY,40(2),322-339.
MLA	Qian, Tang-Wen,et al."A Model-Agnostic Hierarchical Framework Towards Trajectory Prediction".JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY 40.2(2025):322-339.