NDLI: Human trajectory prediction for automatic guided vehicle with recurrent neural network

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Human trajectory prediction for automatic guided vehicle with recurrent neural network

Content Provider	IET Digital Library
Author	Song, Chao Chen, Zhixian Qi, Xiaozhi Zhao, Baoliang Hu, Ying Liu, Shoubin Zhang, Jianwei
Abstract	The accurate prediction of the pedestrian trajectory is necessary to endow automatic guided vehicle with the capabilities to adjust velocity and path dynamically for the navigation in real pedestrian scenes. For this purpose, this study presents a social conscious prediction model considering two main factors that affect the pedestrians’ walking in the crowd – relative distance and moving direction. To form an effective model, the authors’ conscious pooling layer is added to the Long Shot Term Memory network (LTSM) model to build the relationship between pedestrians, learning the current position m and movement trend. The experiments are conducted to compare the proposed model with the previous state-of-the-art model on several public datasets. The experimental results show that the proposed model predicts pedestrian trajectories more accurately.
Starting Page	1574
Ending Page	1578
Page Count	5
Volume Number	2018
e-ISSN	20513305
Issue Number	Issue 16, Nov (2018)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/joe/2018/16
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/joe.2018.8264
Journal	The Journal of Engineering
Publisher	The Institution of Engineering and Technology
Publisher Date	2018-08-16
Access Restriction	Open
Rights License	Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/)
Subject Keyword	Automatic Guided Vehicle Control Engineering Computing Human Trajectory Prediction Knowledge Engineering Technique Learning in AI LSTM Model Mobile Robots Neural Computing Technique Pedestrian Pedestrian Scenes Pedestrian Trajectory Recurrent Neural Nets Recurrent Neural Network Social Conscious Prediction Model Spatial Variables Control Traffic Engineering Computing Trajectory Control Transportation System Control
Content Type	Text
Resource Type	Article

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