Hierarchical Envelope Adversarial Domain Adaptation with Loose-Tight Coupled Trajectory Prediction Model

LI Yong-ming; HU Jie; ZHANG Xiao-heng; WANG Pin; LI Wen-zheng

doi:10.12263/DZXB.20250729

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Hierarchical Envelope Adversarial Domain Adaptation with Loose-Tight Coupled Trajectory Prediction Model

PAPERS | 更新时间：2026-02-05

- Hierarchical Envelope Adversarial Domain Adaptation with Loose-Tight Coupled Trajectory Prediction Model
- ACTA ELECTRONICA SINICA Vol. 53, Issue 10, Pages: 3640-3658(2025)
- 作者机构：
  
  1.重庆大学微电子与通信工程学院，重庆 400044
  2.重庆开放大学，重庆 400044
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China(U21A20448;61771080;72001032)
- DOI：10.12263/DZXB.20250729
  CLC： TP391.4;
- Received：21 August 2025，
  
  Accepted：24 October 2025，
  
  Published：25 October 2025
- 稿件说明：
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李勇明, 胡杰, 张小恒, 等. 分级包络对抗域适应和松-紧耦合行人轨迹预测模型[J]. 电子学报, 2025, 53(10): 3640-3658.

LI Yong-ming, HU Jie, ZHANG Xiao-heng, et al. Hierarchical Envelope Adversarial Domain Adaptation with Loose-Tight Coupled Trajectory Prediction Model[J]. Acta Electronica Sinica, 2025, 53(10): 3640-3658.
李勇明, 胡杰, 张小恒, 等. 分级包络对抗域适应和松-紧耦合行人轨迹预测模型[J]. 电子学报, 2025, 53(10): 3640-3658. DOI：10.12263/DZXB.20250729

LI Yong-ming, HU Jie, ZHANG Xiao-heng, et al. Hierarchical Envelope Adversarial Domain Adaptation with Loose-Tight Coupled Trajectory Prediction Model[J]. Acta Electronica Sinica, 2025, 53(10): 3640-3658. DOI：10.12263/DZXB.20250729

摘要

行人轨迹预测在自动驾驶、智能安防及智慧城市等领域有着广泛用途，但是行人交互的复杂性和不确定性使得行人轨迹预测任务至今仍是一个充满挑战的课题.现有行人轨迹预测模型存在以下共性局限性：（1）仅考虑单一的社交耦合关系，不但引入了冗余交互关系，而且未能充分考虑不同场景下轨迹社交耦合关系不同以及行人间耦合关系不一的特性，从而限制了模型对不同场景特征的深度挖掘与有效利用；（2）未充分考虑域偏移问题，极少数方法虽然考虑了域偏移问题，但是采用基于统计准则的域分布对齐方式，对预定义统计度量具有高度依赖性，对复杂多变场景表现出明显的局限性.为了解决上述问题，本文提出了一种基于分级包络对抗域适应和松-紧耦合的行人轨迹预测模型.首先，设计包络样本变换机制构造包络样本并将其拓展到图结构；其次，结合局部域适应和全局域适应，设计对抗域适应模块；此外，针对不同场景下不同耦合关系，构建松-紧耦合包络样本构造模块.实验部分采用了两个代表性的公共数据集进行有效性验证，并与6个相关代表性算法模型进行综合对比.实验结果表明，本文模型比相关算法显著具有更高准确性，ADE（Average Displacement Error）指标与FDE（Final Displacement Error）指标分别下降了17.6%和19.1%，时间开销满足实际需要，这说明本文主要创新点有效.

Abstract

Pedestrian trajectory prediction holds significant applications across autonomous driving

intelligent surveillance

and smart cities. However

the complexity and unpredictability of pedestrian interactions make this task a persistent challenge. Current models face two common limitations: (1) Considering only a single type of social coupling. This introduces redundant interactions. More critically

it fails to account for the varying nature of trajectory coupling across different scenarios and between different pedestrians. Consequently

models cannot deeply explore or effectively utilize diverse scene features; (2) Inadequate handling of domain shift. Although very few methods address domain shift

they rely on statistical criterion-based domain distribution alignment. Such approaches exhibit strong dependency on predefined statistical metrics. This leads to significant limitations in complex

dynamic environments. To address these issues

this paper proposes a hierarchical envelope adversarial domain adaptation with loose-tight coupled model. Firstly

an envelope sample transformation mechanism was designed. It constructs envelope samples and extends them into graph structures; Secondly

an adversarial domain adaptation module was developed. This integrates both local and global domain adaptation strategies; meanwhile

a loose-tight coupling envelope sample construction module was created. It dynamically adapts to diverse coupling relationships across scenarios. These innovations collectively enhance prediction accuracy and robustness. The experimental section employed two representative public datasets for validation and conducted comprehensive comparisons with six relevant baseline algorithms. Results demonstrate that our model achieves significantly higher accuracy compared to existing methods

with the average displacement error (ADE) and final displacement error (FDE) metrics reduced by 17.6% and 19.1%

respectively. The time overhead meets practical requirements

which verifies the effectiveness of our key innovations.

关键词

Keywords

references

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