Social graph convolutional LSTM for pedestrian trajectory prediction

Social graph convolutional LSTM for pedestrian trajectory prediction

Abstract Understanding the movement of pedestrians and predicting their future trajectory can be very important in intelligent transportation systems because accurate pedestrian trajectory prediction will improve the level of autonomous driving technology and reduce traffic accidents. The authors ad...

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Bibliographic Details
Main Authors: Yutao Zhou, Huayi Wu, Hongquan Cheng, Kunlun Qi, Kai Hu, Chaogui Kang, Jie Zheng
Format: Article
Language:English
Published: Wiley 2021-03-01
Series:IET Intelligent Transport Systems
Subjects:
Computer vision and image processing techniques
Traffic engineering computing
Other topics in statistics
Combinatorial mathematics
Online Access:https://doi.org/10.1049/itr2.12033

Internet

https://doi.org/10.1049/itr2.12033

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