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A Recurrent Attention and Interaction Model for Pedestrian Trajectory Prediction

Xuesong Li Yating Liu Kunfeng Wang Fei-Yue Wang

自动化学报（英文版）2020，Vol.7Issue(5)：1361-1370,10.

自动化学报（英文版）2020，Vol.7Issue(5)：1361-1370,10.DOI:10.1109/JAS.2020.1003300

A Recurrent Attention and Interaction Model for Pedestrian Trajectory Prediction

Xuesong Li ¹Yating Liu ¹Kunfeng Wang ²Fei-Yue Wang³

作者信息

1. State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, University of Chinese Academy of Sciences, Beijing 100049, China
2. College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China
3. State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
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摘要

关键词

Deep learning/long short-term memory (LSTM)/recurrent attention and interaction (RAI) model/trajectory prediction

Key words

Deep learning/long short-term memory (LSTM)/recurrent attention and interaction (RAI) model/trajectory prediction

引用本文复制引用

Xuesong Li,Yating Liu,Kunfeng Wang,Fei-Yue Wang..A Recurrent Attention and Interaction Model for Pedestrian Trajectory Prediction[J].自动化学报（英文版）,2020,7(5):1361-1370,10.

基金项目

This work was supported by the National Natural Science Foundation of China (U1811463) and the Fundamental Research Funds for the Central Universities (12060093192). （U1811463）

自动化学报（英文版）

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ISSN：2329-9266

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