用视觉和运动信息构建行人交互图，提升拥挤环境轨迹预测精度。
Geometric Graph Neural Network Modeling of Human Interactions in Crowded Environments
- 基于视域、运动方向和距离函数定义交互邻域
- 平均位移误差与最终位移误差均显著降低
- 融合心理学知识，适合智能交通与人群仿真场景
在拥挤环境中建模人类轨迹具有挑战性，源于行人的复杂行为与互动。本文提出一种几何图神经网络架构，融入心理学研究的领域知识，用于建模行人互动并预测未来轨迹。与以往使用完全图的方法不同，我们基于行人的视野范围、运动方向及距离核函数定义交互邻域，构建人群的图表示。在多个数据集上的评估表明，该方法在平均位移误差和最终位移误差指标上均实现提升。研究结果强调了将领域知识与数据驱动方法结合，在人群互动建模中的重要性。
原文摘要 · Abstract (English)
Modeling human trajectories in crowded environments is challenging due to the complex nature of pedestrian behavior and interactions. This paper proposes a geometric graph neural network (GNN) architecture that integrates domain knowledge from psychological studies to model pedestrian interactions and predict future trajectories. Unlike prior studies using complete graphs, we define interaction neighborhoods using pedestrians' field of view, motion direction, and distance-based kernel functions to construct graph representations of crowds. Evaluations across multiple datasets demonstrate improved prediction accuracy through reduced average and final displacement error metrics. Our findings underscore the importance of integrating domain knowledge with data-driven approaches for effective modeling of human interactions in crowds.
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