Scholarlink

<p>Pedestrian trajectory prediction aims to forecast pedestrians’ future positions based on their historical movements and surrounding environmental information. This capability is crucial in applications such as autonomous driving. Many existing methods utilize deep learning models to analyze historical trajectories and interactions among pedestrians. While these models often achieve high predictive accuracy, their data-driven nature can result in trajectories that do not accurately reflect pedestrians’ real-world behavioral responses in specific contexts, lacking interpretability. Additionally, most studies do not consider the influence of specific scene information on pedestrians’ movement behavior decisions during Social Interaction Modeling. In this paper, we propose a framework named SE-MBMP (Scene-Enhanced Social Interpretable Movement Behavior for Multimodal Pedestrian Trajectory Prediction). By clustering extensive real-world pedestrian movement behavior data, SE-MBMP constructs an interpretable Movement Behavior Set that encompasses potential future behaviors. Furthermore, scene information is incorporated into Social Interaction Modeling, enhancing the accuracy of predicted trajectories. Extensive experiments on the ETH and UCY datasets demonstrate that our strategy achieves average ADE and FDE scores of 0.32 and 0.57, respectively, representing reductions of 28.1% and 31.3% compared to the METF method, underscoring its potential in trajectory prediction.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Scene-Enhanced Social Interpretable Movement Behavior for Multimodal Pedestrian Trajectory Prediction

  • 期刊论文

  • 发表日期：

    2025年5月19日

查看全文
  • Teng Zhang,
  • Bo Yang,
  • Jianlin Zhu,
  • Xincheng Hu

摘要

Pedestrian trajectory prediction aims to forecast pedestrians’ future positions based on their historical movements and surrounding environmental information. This capability is crucial in applications such as autonomous driving. Many existing methods utilize deep learning models to analyze historical trajectories and interactions among pedestrians. While these models often achieve high predictive accuracy, their data-driven nature can result in trajectories that do not accurately reflect pedestrians’ real-world behavioral responses in specific contexts, lacking interpretability. Additionally, most studies do not consider the influence of specific scene information on pedestrians’ movement behavior decisions during Social Interaction Modeling. In this paper, we propose a framework named SE-MBMP (Scene-Enhanced Social Interpretable Movement Behavior for Multimodal Pedestrian Trajectory Prediction). By clustering extensive real-world pedestrian movement behavior data, SE-MBMP constructs an interpretable Movement Behavior Set that encompasses potential future behaviors. Furthermore, scene information is incorporated into Social Interaction Modeling, enhancing the accuracy of predicted trajectories. Extensive experiments on the ETH and UCY datasets demonstrate that our strategy achieves average ADE and FDE scores of 0.32 and 0.57, respectively, representing reductions of 28.1% and 31.3% compared to the METF method, underscoring its potential in trajectory prediction.