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 |
ISSN | 1524-9050 |
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 |
DOI | 10.1109/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 | |
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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