Research on pedestrian trajectory prediction method based on social attention mechanism

Linhui LI; Bin ZHOU; Jing LIAN; Yafu ZHOU

doi:10.11959/j.issn.1000-436x.2020100

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Research on pedestrian trajectory prediction method based on social attention mechanism

Correspondences | 更新时间：2024-06-05

- Research on pedestrian trajectory prediction method based on social attention mechanism
- Journal on Communications Vol. 41, Issue 6, Pages: 175-183(2020)
- 作者机构：
  
  1. 大连理工大学汽车工程学院，辽宁大连 116024
  2. 大连理工大学工业装备结构分析国家重点实验室，辽宁大连 116024
- 作者简介：
- 基金信息：
  
  The National Natural Science Foundation of China(61976039);The National Natural Science Foundation of China(51775082);The Fundamental Research Funds for the Central Universities(DUT19LAB36)
- DOI：10.11959/j.issn.1000-436x.2020100
  CLC： TP391
- Online First：2020-06，
  
  Published：25 June 2020
- 稿件说明：
移动端阅览
Linhui LI, Bin ZHOU, Jing LIAN, et al. Research on pedestrian trajectory prediction method based on social attention mechanism[J]. Journal on Communications, 2020, 41(6): 175-183.
DOI：

Linhui LI, Bin ZHOU, Jing LIAN, et al. Research on pedestrian trajectory prediction method based on social attention mechanism[J]. Journal on Communications, 2020, 41(6): 175-183. DOI： 10.11959/j.issn.1000-436x.2020100.

摘要

为提高行人交互中轨迹预测速度、精度与模型可解释性，提出了一种基于社会注意力机制的GAN模型。首先，定义了一种新型社会关系，对行人间的影响进行社会关系建模，设计了基于注意力机制的网络模型，提高了网络预测速度和可解释性。然后，探索不同池化汇集机制对预测结果的影响，确定性能优异的池化模型。最后，搭建了轨迹预测网络，并在UCY和ETH数据集中进行训练。实验结果表明，所提模型预测精度优于现有方法，且实时性较现有方法提升18.3% 。

Abstract

In order to improve the speed

accuracy and model interpretability of trajectory prediction in pedestrian interaction

a GAN model based on social attention mechanism was proposed.Firstly

a new type of social relationship on pedestrians was defined to model social relationships and a network model based on the attention mechanism was designed to improve the speed and interpretability of network prediction.Secondly

the influence of different pooling mechanisms on the prediction results was explored to determine the pooling model with excellent performance.Finally

a trajectory prediction network was built on this basis and trained on the UCY and ETH data sets.The experimental results show that the model not only has better prediction accuracy than the existing methods

but also improves the real-time performance by 18.3% compared with the existing methods.

关键词

Keywords

references

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