Pedestrian Trajectory Prediction Based on Deep Convolutional LSTM Network
Song, Xiao1; Chen, Kai1; Li, Xu2; Sun, Jinghan3; Hou, Baocun4; Cui, Yong1; Zhang, Baochang1; Xiong, Gang5; Wang, Zilie6
发表期刊IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
ISSN1524-9050
2021-06-01
卷号22期号:6页码:3285-3302
通讯作者Chen, Kai(chenkaivisual@buaa.edu.cn)
摘要Pedestrian trajectory prediction is vital for transportation systems. Generally we can divide pedestrian behavior modeling into two categories, i.e., knowledge-driven and data-driven. The former might bring expert bias, and it sometimes generates unrealistic pedestrian movement due to unnecessary repulsive forces. The latter approach is popular nowadays but most existing neural networks, including fully connected long short-term memory (LSTM) networks, use a 1D vector to model their input and state. The shortcoming is that these works cannot learn spatial information about pedestrians, especially in a dense crowd. To tackle this, we propose to use tensors to represent essential environment features of pedestrians. Accordingly, a convolutional LSTM is designed and deepened to predict spatiotemporal trajectory sequences. As the tensor and convolution can learn better spatiotemporal interactions among pedestrians and environments, experimental results show that the proposed network can estimate more realistic trajectories for a dense crowd in evacuation and counterflow.
关键词Trajectory Predictive models Neural networks Force Mathematical model Feature extraction Tensors Pedestrian behavior convolution long short-term memory neural network
DOI10.1109/TITS.2020.2981118
关键词[WOS]CROWD DYNAMICS ; EVACUATION ; MANAGEMENT ; SIMULATION ; MODELS
收录类别SCI
语种英语
资助项目National Key Research and Development Program of China[2018YFB1702703] ; Open Fund of China State Key Laboratory of Intelligent Manufacturing System Technology ; Fundamental Research Funds for the Central Universities
项目资助者National Key Research and Development Program of China ; Open Fund of China State Key Laboratory of Intelligent Manufacturing System Technology ; Fundamental Research Funds for the Central Universities
WOS研究方向Engineering ; Transportation
WOS类目Engineering, Civil ; Engineering, Electrical & Electronic ; Transportation Science & Technology
WOS记录号WOS:000658360600006
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计
被引频次:67[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/173211/45344
专题复杂系统管理与控制国家重点实验室_平行智能技术与系统团队
通讯作者Chen, Kai
作者单位1.Beihang Univ, Sch Automat, Beijing 100083, Peoples R China
2.China State Key Lab Intelligent Mfg Syst Technol, Beijing 100854, Peoples R China
3.Univ Illinois, Dept Comp Sci, Champaign, IL 61820 USA
4.Beijing Aerosp Smart Mfg Technol Dev Co Ltd, Beijing, Peoples R China
5.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
6.Beihang Univ, Sch Software, Beijing 100083, Peoples R China
推荐引用方式
GB/T 7714
Song, Xiao,Chen, Kai,Li, Xu,et al. Pedestrian Trajectory Prediction Based on Deep Convolutional LSTM Network[J]. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS,2021,22(6):3285-3302.
APA Song, Xiao.,Chen, Kai.,Li, Xu.,Sun, Jinghan.,Hou, Baocun.,...&Wang, Zilie.(2021).Pedestrian Trajectory Prediction Based on Deep Convolutional LSTM Network.IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS,22(6),3285-3302.
MLA Song, Xiao,et al."Pedestrian Trajectory Prediction Based on Deep Convolutional LSTM Network".IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 22.6(2021):3285-3302.
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