外观
论文arXiv 2610.2113213 天前
WSN下基于分层包络对抗域适应的人员轨迹预测方法
原文链接：Trajectory Prediction Method for person under WSN with Hierarchical Envelope Adversarial Domain Adaptation（arXiv 2610.21132 · 发布日期：2026-10-15）
Trajectory Prediction Method for person under WSN with Hierarchical Envelope Adversarial Domain Adaptation
针对行人交互复杂与域偏移问题，提出含包络样本构造和局/全局对抗域适应的HEADA-LTCTPM，并在两个公共数据集验证。
核心要点
- 现有模型多只考虑单一社会耦合，易引入冗余交互且难适应多样场景。
- 方法构造包络样本并扩展为图结构，结合松散-紧密耦合建模。
- 对抗域适应模块融合局部与全局策略，动态适应不同场景耦合关系。
- 在两个代表性公共数据集上对比六个基线，ADE和FDE分别降低17.6%和19.1%。
验证范围与局限：验证限于两个代表性公共数据集上的行人轨迹预测实验。
英文摘要 (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) Consider only a single type of social coupling. This introduces redundant interactions. More critically, it fails to account for the varying nature of trajectory coupled across different scenarios and between different pedestrians. Consequently, models cannot deeply adapt to diverse scenes. 2) Inadequate domain shift handling. 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 and dynamic environments. To address these problems, a Hierarchical Envelope Adversarial Domain Adaptation with Loosely-Tightly coupled model (HEADA-LTCTPM) is proposed. First, an envelope sample construction mechanism was proposed. It constructs envelope samples and extends them into graph structures. Secondly, an adversarial domain adaptation module was proposed. This integrates both local and global domain adaptation strategies. It dynamically adapts to diverse coupled relationships across scenarios. These innovations collectively enhance prediction accuracy and robustness. In the experimental section, two representative public datasets were employed for validation and comprehensive comparisons with six relevant baseline algorithms were conducted. Results demonstrate that our model achieves significantly higher accuracy compared to existing methods, with the ADE and FDE metrics reduced by 17.6% and 19.1% respectively. The time overhead meets practical requirements, which verifies the effectiveness of our key innovations.
作者：Wenqian Jiang, Qifeng Sun, Xiaosong Gao, Xin Guo Zou, Xingrui Ni, Yuan Hu, Yongming Li
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