Multi-agent trajectory prediction, as a critical task in modeling complex interactions of objects in dynamic systems, has attracted significant research attention in recent years. Despite the promising advances, existing studies all follow the assumption that data distribution observed during model learning matches that encountered in real-world deployments. However, this assumption often does not hold in practice, as inherent distribution shifts might exist in the mobility patterns for deployment environments, thus leading to poor domain generalization and performance degradation. Consequently, it is appealing to leverage trajectories from multiple source domains to mitigate such discrepancies for multi-agent trajectory prediction task. However, the development of multi-source domain generalization in this task presents two notable issues: (1) negative transfer; (2) inadequate modeling for external factors. To address these issues, we propose a new causal formulation to explicitly model four types of features: domain-invariant and domain-specific features for both the focal agent and neighboring agents. Building upon the new formulation, we propose AdapTraj, a multi-source domain generalization framework specifically tailored for multi-agent trajectory prediction. AdapTraj serves as a plug-and-play module that is adaptable to a variety of models. Extensive experiments on four datasets with different domains demonstrate that AdapTraj consistently outperforms other baselines by a substantial margin.

翻译：多智能体轨迹预测作为建模动态系统中对象复杂交互的关键任务，近年来受到了广泛研究关注。尽管取得了令人瞩目的进展，现有研究均遵循一个假设：模型学习过程中观测到的数据分布与实际部署环境中的数据分布相匹配。然而，这一假设在实践中往往不成立，因为部署环境中的移动模式可能存在固有的分布偏移，从而导致域泛化能力不足和性能下降。因此，利用多个源域的轨迹来减轻多智能体轨迹预测任务中的此类差异具有重要价值。然而，该任务中多源域泛化的发展面临两个显著问题：（1）负迁移；（2）对外部因素建模不充分。为解决这些问题，我们提出了一种新的因果公式化方法，明确建模四类特征：目标智能体和邻域智能体的域不变特征与域特定特征。基于这一新公式，我们提出了AdapTraj，一个专为多智能体轨迹预测量身定制的多源域泛化框架。AdapTraj作为即插即用模块，可适配多种模型。在包含不同领域的四个数据集上进行的大量实验表明，AdapTraj始终以显著优势优于其他基线方法。