社会性锚点：面向群体有界轨迹预测

中文摘要 AI 辅助

轨迹预测是理解动态场景中人类行为模式的关键组成部分。研究者们投入了大量精力来建模社会交互，尤其是群体层面的交互，因为群体成员关系往往反映共享意图、协调运动和稳定的相互适应，从而为预测提供持久且语义上有意义的社会先验。然而，现有的群体建模方法可能依赖固定阈值，并主要从观测窗口内智能体的相对位置推断群体，忽视了群体规则应当具有智能体特异性、时间连贯性，并能适应不同个性、文化背景和不断演化的交互情境这一事实。受人类社会感知的启发——在边界敏感情境中交替使用人际距离，在动态交互中依赖相对速度一致性——我们提出了Socialality，一个受人类启发的轨迹预测框架，具有可解释的社会性锚点（Socialality anchors）和扩展的群体窗口，以实现稳定、情境感知的群体推断。具体而言，Socialality引入了一个双标量控制的群体核函数Socialality，联合利用历史观测和短期未来轨迹预览来学习智能体特定的群体规则，并采用群体感知机制以直观且可解释的方式建模群体内和群体外交互。此外，我们在标准基准上进行了广泛实验，以展示Socialality的性能提升，并提供了锚点分布的定性分析和统计研究，以验证所提出的社会性锚点的可解释性和稳定性。

英文摘要

Trajectory prediction is a key component for understanding human behavior patterns in dynamic scenes. Researchers have devoted substantial efforts to modeling social interactions, especially group-wise interactions, since group membership often reflects shared intention, coordinated motion, and stable mutual adaptation, thus providing a persistent and semantically meaningful social prior for forecasting. However, existing group modeling methods may rely on a fixed threshold and infer groups mainly from agents' relative positions within the observation window, overlooking the fact that grouping rules should be agent-specific, temporally coherent, and context-adaptive across diverse personalities, culturalities, and evolving interaction contexts. Inspired by human social perception that alternates between interpersonal distance in boundary-sensitive situations and relative speed consistency in dynamic interactions, we propose Socialality, a human-inspired trajectory prediction framework with interpretable Socialality anchors and an extended grouping window for stable, context-aware grouping inference. Concretely, Socialality introduces a duo-scalar-controlled grouping kernel Socialality that jointly leverages historical observations and short-term future trajectory previews to learn agent-specific grouping rules, and employs a group-wise perception mechanism to model in-group and out-of-group interactions in an intuitive and explainable manner. Furthermore, we conduct extensive experiments on standard benchmarks to demonstrate the performance gains of Socialality, and provide qualitative analyses and statistical studies of anchor distributions to verify the interpretability and stability of the proposed Socialality anchors.

发表机构

• Huazhong University of Science and Technology（华中科技大学）

机构由 AI 辅助整理，请以论文原文为准。