针对体育多智能体轨迹预测，提出自适应建模框架提升跨角色跨领域泛化能力。
AdaSports-Traj: Role- and Domain-Aware Adaptation for Multi-Agent Trajectory Modeling in Sports
- 设计角色与领域感知适配器，动态调整智能体表征
- 在三个体育数据集上实现统一与跨域预测领先性能
- 适合需要跨运动场景轨迹建模的研究者和开发者
多智能体体育场景中的轨迹预测因智能体角色（如球员与球）的结构异质性及不同体育领域间分布差异而极具挑战。现有统一框架难以捕捉此类结构性分布偏移，导致角色与领域间泛化效果不佳。本文提出AdaSports-Traj，一种显式处理体育场景内与跨领域分布差异的自适应轨迹建模框架。核心在于引入角色与领域感知适配器，根据智能体身份与领域上下文条件性地调整潜在表征。同时，设计分层对比学习目标，分别监督角色敏感与领域感知表征，促进潜在空间解耦且避免优化冲突。在Basketball-U、Football-U与Soccer-U三个多样化体育数据集上的实验表明，该方法在统一与跨域轨迹预测设置中均取得优异表现。
原文摘要 · Abstract (English)
Trajectory prediction in multi-agent sports scenarios is inherently challenging due to the structural heterogeneity across agent roles (e.g., players vs. ball) and dynamic distribution gaps across different sports domains. Existing unified frameworks often fail to capture these structured distributional shifts, resulting in suboptimal generalization across roles and domains. We propose AdaSports-Traj, an adaptive trajectory modeling framework that explicitly addresses both intra-domain and inter-domain distribution discrepancies in sports. At its core, AdaSports-Traj incorporates a Role- and Domain-Aware Adapter to conditionally adjust latent representations based on agent identity and domain context. Additionally, we introduce a Hierarchical Contrastive Learning objective, which separately supervises role-sensitive and domain-aware representations to encourage disentangled latent structures without introducing optimization conflict. Experiments on three diverse sports datasets, Basketball-U, Football-U, and Soccer-U, demonstrate the effectiveness of our adaptive design, achieving strong performance in both unified and cross-domain trajectory prediction settings.
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