基于多模式时空交互的行人轨迹预测模型

桑海峰; 陈旺兴; 王海峰; 王金玉

doi:10.12263/DZXB.20210752

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基于多模式时空交互的行人轨迹预测模型

学术论文 | 更新时间：2025-12-08

- 基于多模式时空交互的行人轨迹预测模型
- Pedestrian Trajectory Prediction Model Based on Multi-Model Space-Time Interaction
- 电子学报 2022年50卷第11期页码：2806-2812
- 作者机构：
  
  沈阳工业大学信息科学与工程学院, 辽宁沈阳 110870
- 作者简介：
  
  [ "桑海峰男, 1978年1月出生于辽宁省沈阳市.现为沈阳工业大学教授、博士生导师.主要研究方向为机器视觉检测技术和智能视频分析技术.E‑mail: sanghaif@163.com" ]
  [ "陈旺兴（通讯作者）男, 1998年3月出生于江西省抚州市.目前在沈阳工业大学攻读硕士学位，主要研究方向为行人轨迹预测.E‑mail: 1909703861@qq.com" ]
  [ "王海峰男, 1995年3月出生于吉林省吉林市.目前在沈阳工业大学攻读硕士学位，主要研究方向为行人轨迹预测.E‑mail: 798466420@qq.com" ]
  [ "王金玉女, 1996年5月出生于辽宁省盖州市.目前在沈阳工业大学攻读博士学位，主要研究方向为行人轨迹预测.E‑mail: 1911131982@qq.com" ]
- 基金信息：
  
  国家自然科学基金(61773105);辽宁省教育厅科研项目(LJGD2020006)
- DOI：10.12263/DZXB.20210752
  中图分类号： TP391
- 收稿：2021-06-14，
  
  修回：2021-08-23，
  
  纸质出版：2022-11-25
- 稿件说明：
移动端阅览
桑海峰,陈旺兴,王海峰等.基于多模式时空交互的行人轨迹预测模型[J].电子学报,2022,50(11):2806-2812.

SANG Hai-feng,CHEN Wang-xing,WANG Hai-feng,et al.Pedestrian Trajectory Prediction Model Based on Multi-Model Space-Time Interaction[J].ACTA ELECTRONICA SINICA,2022,50(11):2806-2812.
桑海峰,陈旺兴,王海峰等.基于多模式时空交互的行人轨迹预测模型[J].电子学报,2022,50(11):2806-2812. DOI： 10.12263/DZXB.20210752.

SANG Hai-feng,CHEN Wang-xing,WANG Hai-feng,et al.Pedestrian Trajectory Prediction Model Based on Multi-Model Space-Time Interaction[J].ACTA ELECTRONICA SINICA,2022,50(11):2806-2812. DOI： 10.12263/DZXB.20210752.

摘要

在正确地规划合理路径方面，行人轨迹预测具有重要的意义.大多数现有轨迹预测方法在考虑周围行人的影响时，都是简单地将周围行人全部考虑在内，这必然带来的冗余信息.本文提出了一种基于多模式时空交互的行人轨迹预测模型，该模型通过多模式行人空间交互模块对不同行人在不同情况下给予不同的权重，使得模型可以有效减小冗余信息带来的影响.并且本文的模型针对于输入轨迹信息的不同重要程度，设计了加权信息融合模块在原轨迹信息的基础上融合了赋予不同权重的历史轨迹信息，使得模型的轨迹信息更加有效.此外，该模型采用了时间卷积网络模块来捕获行人的时间交互.实验结果表明，在公开数据集ETH和UCY上，相比于Social-STGCNN平均位移误差(Average Displacement Error

ADE)和终点位移误差(Final Displacement Error

FDE)分别降低了15%和14%.

Abstract

Pedestrian trajectory prediction plays an important role in correctly planning reasonable paths. Most of the existing trajectory prediction methods simply take all the pedestrians into account when considering the influence of the surrounding pedestrians

which inevitably brings redundant information. A pedestrian trajectory prediction model based on multi-mode space-time interaction is proposed. This model gives different weights to different pedestrians in different situations through multi-mode pedestrian space interaction module

which makes the model effectively reduce the impact of redundant information. Aiming at the different importance of the input trajectory information

the weighted information fusion module is designed to integrate the historical trajectory information with different weights on the basis of the original trajectory information

so as to make the trajectory information of the model more effective. In addition

the model uses time convolution network module to capture pedestrian time interaction. The experimental results show that compared with social-stgcnn

average displacement error(ADE) and final displacement error(FDE) is reduced by 15% and 14% respectively on the open data sets ETH and UCY.

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references

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