Adaptive Group Masked Graph Convolution Network for Pedestrian Trajectory Prediction

CHEN Wangxing; SANG Haifeng; LIU Qing

doi:10.12263/DZXB.20251200

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Adaptive Group Masked Graph Convolution Network for Pedestrian Trajectory Prediction

PAPERS | 更新时间：2026-05-28

- Adaptive Group Masked Graph Convolution Network for Pedestrian Trajectory Prediction
- ACTA ELECTRONICA SINICA Vol. 54, Issue 2, Pages: 774-784(2026)
- 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：
  
  Key Research and Development Projects of Liaoning Provincial Department of Science and Technology(2024JH2/102400028);Liaoning Provincial Department of Education Research Project(YTMS20231210)
- DOI：10.12263/DZXB.20251200
  CLC： TP391.4;
- Received：16 January 2026，
  
  Accepted：11 February 2026，
  
  Published：25 February 2026
- 稿件说明：
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陈旺兴, 桑海峰, 刘晴. 基于自适应群体掩码图卷积网络的行人轨迹预测[J]. 电子学报, 2026, 54(02): 774-784.

CHEN Wangxing, SANG Haifeng, LIU Qing. Adaptive Group Masked Graph Convolution Network for Pedestrian Trajectory Prediction[J]. Acta Electronica Sinica, 2026, 54(02): 774-784.
陈旺兴, 桑海峰, 刘晴. 基于自适应群体掩码图卷积网络的行人轨迹预测[J]. 电子学报, 2026, 54(02): 774-784. DOI：10.12263/DZXB.20251200

CHEN Wangxing, SANG Haifeng, LIU Qing. Adaptive Group Masked Graph Convolution Network for Pedestrian Trajectory Prediction[J]. Acta Electronica Sinica, 2026, 54(02): 774-784. DOI：10.12263/DZXB.20251200

摘要

行人轨迹预测对于提升自动驾驶和服务机器人的决策能力以及降低未来潜在碰撞风险方面具有重要意义。然而，由于行人群体内外社会交互关系的差异性和复杂性，现有研究往往未能对群内和群外交互关系进行显式区分与独立建模，不同类型交互特征在模型学习过程中相互混淆，难以精准刻画行人在复杂场景下的真实运动模式，进而制约了模型预测性能的进一步提升。因此，本文提出了一种基于自适应群体掩码图卷积网络（Adaptive Group Masked Graph Convolution Network，AGMGCN）的行人轨迹预测模型，通过对群内和群外交互关系进行独立建模，从而提升模型轨迹预测的准确性。该模型首先构建社会图并采用自注意力机制进行处理，以获得注意力矩阵用于初步表示行人之间的交互关系。后续设计了时频域卷积模块，通过在时域和频域同时对注意力矩阵进一步处理，生成用于表征行人时空交互关系的时频交互矩阵，以实现对行人复杂动态交互更准确的刻画。为有效区分并独立建模群内和群外交互，模型设计了自适应群体掩码模块，根据行人之间的特征相似性自适应确定阈值，并通过阈值处理生成群内掩码矩阵和群外掩码矩阵，为后续群内和群外交互关系的独立建模提供支持。在此基础上，将时频交互矩阵与群外和群内掩码矩阵相结合，并分别应用图卷积捕捉群内交互特征和群外交互特征，从而实现行人群内和群外交互关系的独立建模。最后，模型设计了特征融合模块动态加权融合群内交互特征和群外交互特征，并通过时间卷积网络完成行人未来轨迹的预测。在ETH、UCY和SDD数据集上的实验结果表明，在仅使用23.9 K模型参数的条件下，本文提出的方法相较于DSTIGCN在平均位移误差和终点位移误差上分别降低了12%和20%，验证了所提方法在预测精度和计算成本方面的优势。

Abstract

Pedestrian trajectory prediction is crucial for improving the decision-making capabilities of autonomous vehicles and service robots

as well as mitigating potential future collision risks. However

due to the differences and complexity of social interactions within and outside pedestrian groups

existing studies often fail to explicitly distinguish and independently model interactions within and outside groups. This leads to confusion of different types of interaction features during model learning

making it difficult to accurately depict the real motion patterns of pedestrians in complex scenarios

thus restricting further improvement in model prediction performance. Therefore

this paper proposes a pedestrian trajectory prediction model based on an adaptive group masked graph convolution network (AGMGCN). By independently modeling in-group and out-group interactions

the accuracy of model trajectory prediction is improved. The model first constructs a social graph and processes it using a self-attention mechanism to obtain an attention matrix that initially represents the interactions between pedestrians. Subsequently

a time-frequency domain convolution module is designed to further process the attention matrix in both the time and frequency domains

generating a time-frequency interaction matrix to characterize the spatial-temporal interactions of pedestrians

thus achieving a more accurate portrayal of complex dynamic interactions. To effectively distinguish and independently model in-group and out-group interactions

an adaptive group masking module is designed. This module adaptively determines a threshold based on the feature similarity between pedestrians and generates in-group and out-group masking matrices through threshold processing

providing support for the subsequent independent modeling of in-group and out-group interactions. Building upon this foundation

the time-frequency interaction matrix is combined with out-group and in-group masking matrices

and graph convolution is applied to respectively capture in-group and out-group interaction features

thereby achieving independent modeling of pedestrian in-group and out-group interaction relationships. Finally

a feature fusion module is designed to dynamically weight and fuse in-group and out-group interaction features and then temporal convolution networks are used to predict the future trajectory of pedestrians. Experimental results on the ETH

UCY and SDD datasets demonstrate that

with only 23.9 K model parameters

the proposed method reduces the average displacement error by 12% and the final displacement error by 20% compared to DSTIGCN

validating the advantages of the proposed method in terms of prediction accuracy and computational cost.

关键词

Keywords

references

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