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CSIR: Cascaded Sliding CVAEs With Iterative Socially-Aware Rethinking for Trajectory Prediction

文献类型：期刊论文

作者Zhou, Hao1,2; Yang, Xu2; Ren, Dongchun3; Huang, Hai1; Fan, Mingyu4
刊名IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
出版日期2023-08-11
页码13
ISSN号1524-9050
关键词Cascaded prediction sliding sequence prediction iterative social-aware rethinking trajectory prediction
DOI10.1109/TITS.2023.3300730
通讯作者Huang, Hai(haihus@163.com) ; Fan, Mingyu(fanmingyu@amss.ac.cn)
英文摘要Pedestrian trajectory prediction is a hot research topic in many applications, such as video surveillance and autonomous driving. Although many efforts have been done on this topic, there are still many challenges, including accumulated prediction errors, insufficient training data usage, and future-past incompatibility. To overcome these challenges, we propose a novel trajectory prediction method, called CSIR, which consists of a cascaded sliding conditional variational autoencoder (CS-CVAE) module and an iterative future-past social compatible rethinking (I-SCR) module. The CS-CVAE module reduces the accumulated prediction errors by using cascaded prediction models for the early future time steps. In this way, the training losses of the early time steps are separately considered and minimized from the later losses. For the following time steps in CS-CVAE, a sliding prediction model with a longer observation time span is used and additional data from the future time span can be collected for training. On the other hand, the I-SCR module generates offsets to improve the predictions iteratively by checking the interaction compatibility between the predicted trajectories and the past trajectories, which resembles with the human rethinking mechanism in motion planning. Experiments results on two widely explored pedestrian trajectory prediction datasets, Stanford Drone Dataset (SDD) and ETH/UCY, show that the proposed method surpasses previous state-of-the-art methods by notable margins.
资助项目National Natural Science Foundation of China[U21A20490] ; National Natural Science Foundation of China[61633009] ; National Natural Science Foundation of China[61973301] ; National Natural Science Foundation of China[61972020] ; National Natural Science Foundation of China[61772373] ; National Natural Science Foundation of China[51579053] ; National Natural Science Foundation of China[U1613213] ; Beijing Nova Program[Z201100006820046]
WOS研究方向Engineering ; Transportation
语种英语
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号WOS:001054597800001
资助机构National Natural Science Foundation of China ; Beijing Nova Program
源URL[http://ir.ia.ac.cn/handle/173211/54113]  
专题多模态人工智能系统全国重点实验室
通讯作者Huang, Hai; Fan, Mingyu
作者单位1.Harbin Engn Univ, Natl Key Lab Sci & Technol Underwater Vehicle, Harbin 150001, Peoples R China
2.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
3.Meituan, Res Ctr Autonomous Vehicles, 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,Ren, Dongchun,et al. CSIR: Cascaded Sliding CVAEs With Iterative Socially-Aware Rethinking for Trajectory Prediction[J]. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS,2023:13.
APA Zhou, Hao,Yang, Xu,Ren, Dongchun,Huang, Hai,&Fan, Mingyu.(2023).CSIR: Cascaded Sliding CVAEs With Iterative Socially-Aware Rethinking for Trajectory Prediction.IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS,13.
MLA Zhou, Hao,et al."CSIR: Cascaded Sliding CVAEs With Iterative Socially-Aware Rethinking for Trajectory Prediction".IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2023):13.

入库方式： OAI收割

来源：自动化研究所

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