发表机构
University of Macau; State Key Laboratory of Internet of Things for Smart City, University of Macau; Department of Civil and Environmental Engineering, University of Macau（澳门大学; 澳门大学智慧城市物联网国家重点实验室; 澳门大学土木与环境工程系）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究自动驾驶中轨迹预测问题，提出SWIFT框架，结合小世界网络与交通流理论，通过特定网络和模块引入结构偏差与增强推理，实验证明其在多方面优于基线，展现出结构感知设计的有效性。
AI 中文摘要
自动驾驶中的准确轨迹预测取决于对交通主体间动态和上下文相关交互进行建模。然而，现有多数方法纯粹是数据驱动且缺乏结构先验，限制了分布变化下的泛化能力。本文通过交通网络的结构和动力学重新审视交互建模，提出SWIFT框架，它将小世界网络与交通流理论相结合。通过小世界交互网络和流态编码器引入结构归纳偏差，用多关系图模块增强交互推理。在三个真实世界数据集上的实验表明，SWIFT在不同交通场景下预测准确率更高，且具有更好的泛化性、鲁棒性和在有限训练数据下的强性能。
英文摘要
Accurate trajectory prediction in autonomous driving hinges on modeling dynamic and context-dependent interactions among traffic agents. However, most existing approaches are purely data-driven and lack structural priors, which limits their generalization under distribution shifts. In this work, interaction modeling is revisited through the structure and dynamics of traffic networks, and SWIFT (Small-World Interaction Framework for Trajectory prediction) is proposed as a unified framework that integrates small-world networks with traffic flow theory. SWIFT introduces structural inductive biases via a Small-World Interaction Network that captures both local and global dependencies, and a Flow Regime Encoder that adapts the interaction structure to scene-level traffic states. Interaction reasoning is further enhanced through a multi-relational graph module that explicitly encodes direct and higher-order agent relationships. Extensive experiments on three real-world datasets, nuScenes, MoCAD, and NGSIM, show that SWIFT consistently outperforms strong baselines in prediction accuracy across diverse traffic regimes. Beyond accuracy, SWIFT exhibits improved generalization to unseen locations and regimes, robustness under noisy observations, and strong performance with limited training data, supporting the effectiveness of its structure-aware design.
CommentsAccepted by IEEE Transactions on Pattern Analysis and Machine Intelligence
