考虑群体关系的行人轨迹预测模型，提升预测准确性。
Who Walks With You Matters: Perceiving Social Interactions with Groups for Pedestrian Trajectory Prediction
- 基于长期距离核函数区分群体与非群体成员。
- 融合视觉和听觉信息感知周围交互，准确率显著提升。
- 模型可解释性强，适合自动驾驶与监控场景使用。
理解与预测人类运动在自动驾驶、监控等场景中日益重要且充满挑战。不同主体间的复杂交互是主要难点之一。现有研究多采用规则或数据驱动方法提取行人轨迹与交互模式，但仍未充分解决。受人类对社交关系感知启发，本文提出GrouP ConCeption（GPCC）模型，包含分组方法与感知模块：分组方法基于长期距离核函数将附近行人划分为群体成员或非群体成员；感知模块则融合目标行人的视觉与听觉信息。在多个数据集上的评估表明，该模型在轨迹预测精度上取得显著提升，验证了其对社会性与个体动态建模的有效性。定性分析显示，模型能像人一样直观利用分组与感知线索，具备良好可解释性。
原文摘要 · Abstract (English)
Understanding and anticipating human movement has become more critical and challenging in diverse applications such as autonomous driving and surveillance. The complex interactions brought by different relations between agents are a crucial reason that poses challenges to this task. Researchers have put much effort into designing a system using rule-based or data-based models to extract and validate the patterns between pedestrian trajectories and these interactions, which has not been adequately addressed yet. Inspired by how humans perceive social interactions with different level of relations to themself, this work proposes the GrouP ConCeption (short for GPCC) model composed of the Group method, which categorizes nearby agents into either group members or non-group members based on a long-term distance kernel function, and the Conception module, which perceives both visual and acoustic information surrounding the target agent. Evaluated across multiple datasets, the GPCC model demonstrates significant improvements in trajectory prediction accuracy, validating its effectiveness in modeling both social and individual dynamics. The qualitative analysis also indicates that the GPCC framework successfully leverages grouping and perception cues human-like intuitively to validate the proposed model's explainability in pedestrian trajectory forecasting.
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