计算机应用研究2026，Vol.43Issue(2)：472-478,7.DOI:10.19734/j.issn.1001-3695.2025.06.0184
一种基于矩阵记忆长短期记忆网络的行人轨迹预测算法
Pedestrian trajectory prediction algorithm based on matrix memory long short-term memory network
摘要
Abstract
This paper proposed a purely temporal prediction algorithm based on mLSTM to address insufficient temporal mode-ling,ambiguous multi-scale feature fusion,and unstable multi-task training in pedestrian trajectory prediction.The algorithm established an encoder-decoder architecture centered on mLSTM to capture temporal dependencies in trajectories.It designed a multi-scale trajectory feature fusion module with a bidirectional strategy to enable hierarchical representation of short-term and long-term features.This paper introduced an exponential moving average normalization-based multi-task mechanism to improve training stability and model generalization.Experimental results on the ETH and UCY datasets show that the proposed algo-rithm reduces average displacement error by 14.81％and 16.21％,and final displacement error by 19.66％and 4.62％,re-spectively,compared with those of Trajectory-Transformer and SGCN.The results demonstrate high prediction accuracy and robustness,providing a stable and effective backbone model for pedestrian trajectory prediction.关键词
行人轨迹预测/矩阵记忆的长短期记忆网络/多尺度特征融合/指数移动平均/多任务学习Key words
pedestrian trajectory prediction/matrix long short-term memory network/multi-scale feature fusion/exponential moving average/multi-task learning分类
信息技术与安全科学引用本文复制引用
厍向阳,王艺龙..一种基于矩阵记忆长短期记忆网络的行人轨迹预测算法[J].计算机应用研究,2026,43(2):472-478,7.基金项目
陕西省自然科学基础研究资助项目(2019JLM-11) （2019JLM-11）
陕西省科技计划资助项目(2021JQ-576) （2021JQ-576）
陕西省教育厅项目(19JK0526) （19JK0526）
