Pedestrian Trajectory Prediction Model Based on Multi-Model Space-Time Interaction

SANG Hai-feng; CHEN Wang-xing; WANG Hai-feng; WANG Jin-yu

doi:10.12263/DZXB.20210752

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Pedestrian Trajectory Prediction Model Based on Multi-Model Space-Time Interaction

PAPERS | 更新时间：2025-12-08

- Pedestrian Trajectory Prediction Model Based on Multi-Model Space-Time Interaction
- ACTA ELECTRONICA SINICA Vol. 50, Issue 11, Pages: 2806-2812(2022)
- 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：
- 基金信息：
- DOI：10.12263/DZXB.20210752
  CLC： TP391
- Received：14 June 2021，
  
  Revised：2021-08-23，
  
  Published：25 November 2022
- 稿件说明：
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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

MOUSSAÏD M , PEROZO N , GARNIER S , et al . The walking behaviour of pedestrian social groups and its impact on crowd dynamics [J]. Plos One , 2010 , 5 ( 4 ): e10047 .

ZHAO D , CHEN Y , LE L . Deep reinforcement learning with visual attention for vehicle classification [J]. IEEE Transactions on Cognitive & Developmental Systems , 2017 , 9 ( 4 ): 356 ‐ 367 .

ALAHI A , GOEL K , RAMANATHAN V , et al . Social LSTM: Human trajectory prediction in crowded spaces [C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition . Las Vegas : IEEE CS , 2016 : 961 ‐ 971 .

HUANG Y F , BI H K , LI Z X , et al . Stgat: Modeling spatial-temporal interactions for human trajectory prediction [C]// Proceedings of the IEEE International Conference on Computer Vision . Seoul : CV/IEEE , 2019 : 6271 ‐ 6280 .

SADEGHIAN A , KOSARAJU V , SADEGHIAN A , et al . Sophie: An attentive GAN for predicting paths compliant to social and physical constraints [C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition . Long Beach : IEEE CS , 2019 : 1349 ‐ 1358 .

GUPTA A , JOHNSON J , FEI-FEI LI , et al . Social GAN: Socially acceptable trajectories with generative adversarial networks [C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition . Salt Lake City : IEEE CS , 2018 : 2255 ‐ 2264 .

IVANOVIC B , PAVONE M . The trajectron: Probabilistic multi-agent trajectory modeling with dynamic spatiotemporal graphs [C]// Proceedings of the IEEE International Conference on Computer Vision . Seoul : CV/IEEE , 2019 : 2375 ‐ 2384 .

ZHANG P , OUYANG W , ZHANG P , et al . SR-LSTM: State refinement for LSTM towards pedestrian trajectory prediction [C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition . Long Beach : IEEE CS , 2019 : 12077 ‐ 12086 .

XUE H , HUYNH D Q , REYNOLDS M . Bi-prediction: pedestrian trajectory prediction based on bidirectional LSTM classification [C]// Proceedings of the International Conference on Digital Image Computing: Techniques and Applications . Sydney : IEEE , 2017 : 1 ‐ 8 .

XUE H , HUYNH D Q , REYNOLDS M . SS-LSTM: A hierarchical LSTM model for pedestrian trajectory prediction [C]// Proceedings of the IEEE Winter Conference on Applications of Computer Vision . Lake Tahoe : IEEE , 2018 : 1186 ‐ 1194 .

XUE H , HUYNH D Q , REYNOLDS M . A location-velocity-temporal attention LSTM model for pedestrian trajectory prediction [J]. IEEE Access , 2020 , 8 : 4 4576‐ 44589 .

LEE N , CHOI W , VERNAZA P , et al . Desire: Distant future prediction in dynamic scenes with interacting agents [C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition . Honolulu : IEEE , 2017 : 2165 ‐ 2174 .

FERNANDO T , DENMAN S , SRIDHARAN S , et al . Soft+ hardwired attention: An LSTM framework for human trajectory prediction and abnormal event detection [J]. Neural Networks , 2018 , 108 : 466 ‐ 478 .

SUN L , YAN Z , MELLADO S M , et al . 3DOF pedestrian trajectory prediction learned from long-term autonomous mobile robot deployment data [C]// Proceedings of the IEEE International Conference on Robotics and Automation . Brisbane : IEEE , 2018 : 5942 ‐ 5948 .

MOHAMED A , QIAN K , ELHOSEINY M , et al . Social-stgcnn: A social spatio-temporal graph convolutional neural network for human trajectory prediction [C]// 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Seattle : IEEE , 2020 : 14412 ‐ 14420 .

ZHAO X , CHEN Y , GUO J , et al . A spatial-temporal attention model for human trajectory prediction [J]. IEEE/CAA Journal of Automatica Sinica , 2020 , 7 ( 4 ): 965 ‐ 974 .

WANG C X , CAI S F , TAN G . Graphtcn: Spatio-temporal interaction modeling for human trajectory prediction [C]// Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision . Waikoloa : IEEE , 2021 : 3450 ‐ 3459 .

PELLEGRINI S , ESS A , SCHINDLER K , VAN GOOL L . You'll never walk alone: Modeling social behavior for multi-target tracking [C]// 2009 IEEE 12th International Conference on Computer Vision . Kyoto : IEEE , 2009 : 261 ‐ 268 .

LERNER A , CHRYSANTHOU Y , LISCHINSKI D . Crowds by example [J]. Computer Graphics Forum , 2007 , 26 ( 3 ): 655 ‐ 664 .

KOSARAJU V , SADEGHIAN A , MARTÍN-MARTÍN R , et al . Social-bigat: Multimodal trajectory forecasting using bicycle-GAN and graph attention networks [C]// Proceedings of Annual Conference on Neural Information Processing Systems . Vancouver : NeurIPS , 2019 : 1 ‐ 10 .

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