CSR: Cascade Conditional Variational Auto Encoder with Socially-aware Regression for Pedestrian Trajectory Prediction

doi:10.1016/j.patcog.2022.109030

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 机器人理论与应用

	CSR: Cascade Conditional Variational Auto Encoder with Socially-aware Regression for Pedestrian Trajectory Prediction
	Zhou, Hao 1,4; Ren, Dongchun 2; Yang, Xu4 ; Fan, Mingyu 2,3; Huang, Hai 1
发表期刊	PATTERN RECOGNITION
ISSN	0031-3203
	2023
卷号	133 页码:10
通讯作者	Fan, Mingyu(fanmingyu@wzu.edu.cn) ; Huang, Hai(haihus@163.com)
摘要	Pedestrian trajectory prediction is a key technology in many real applications such as video surveillance, social robot navigation, and autonomous driving, and significant progress has been made in this research topic. However, there remain two limitations of previous studies. First, the losses of the last time steps are heavier weighted than that of the beginning time steps in the objective function at the learning stage, causing the prediction errors generated at the beginning to accumulate to large errors at the last time steps at the inference stage. Second, the prediction results of multiple pedestrians in the prediction horizon might be socially incompatible with the interactions modeled by past trajectories. To overcome these limitations, this work proposes a novel trajectory prediction method called CSR, which consists of a cascaded conditional variational autoencoder (CVAE) module and a socially-aware regression module. The CVAE module estimates the future trajectories in a cascaded sequential manner. Specifically, each CVAE concatenates the past trajectories and the predicted location points so far as the input and predicts the adjacent location at the following time step. The socially-aware regression module generates offsets from the estimated future trajectories to produce the corrected predictions, which are more reasonable and accurate than the estimated trajectories. Experiments results demonstrate that the proposed method exhibits significant improvements over state-of-the-art methods on the Stanford Drone Dataset (SDD) and the ETH/UCY dataset of approximately 38.0% and 22.2%, respectively. The code is available at https: //github.com/zhouhao94/CSR . (c) 2022 Elsevier Ltd. All rights reserved.
关键词	Pedestrian trajectory prediction Socially -aware model Conditional variational autoencoder (CVAE)
DOI	10.1016/j.patcog.2022.109030
收录类别	SCI
语种	英语
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[Z20110 0 0 06820 046] ; Meituan Open RD Fund
项目资助者	National Natural Science Foundation of China ; Beijing Nova Program ; Meituan Open RD Fund
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS记录号	WOS:000861386400001
出版者	ELSEVIER SCI LTD
引用统计	被引频次：5[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/50407
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	Fan, Mingyu; Huang, Hai
作者单位	1.Harbin Engn Univ, Natl Key Lab Sci & Technol Underwater Vehicle, Harbin, Peoples R China 2.Meituan, Res Ctr Autonomous Vehicles, Beijing, Peoples R China 3.Wenzhou Univ, Coll Comp Sci, 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
推荐引用方式 GB/T 7714	Zhou, Hao,Ren, Dongchun,Yang, Xu,et al. CSR: Cascade Conditional Variational Auto Encoder with Socially-aware Regression for Pedestrian Trajectory Prediction[J]. PATTERN RECOGNITION,2023,133:10.
APA	Zhou, Hao,Ren, Dongchun,Yang, Xu,Fan, Mingyu,&Huang, Hai.(2023).CSR: Cascade Conditional Variational Auto Encoder with Socially-aware Regression for Pedestrian Trajectory Prediction.PATTERN RECOGNITION,133,10.
MLA	Zhou, Hao,et al."CSR: Cascade Conditional Variational Auto Encoder with Socially-aware Regression for Pedestrian Trajectory Prediction".PATTERN RECOGNITION 133(2023):10.