We present an innovative framework for traffic dynamics analysis using High-Order Evolving Graphs, designed to improve spatio-temporal representations in autonomous driving contexts. Our approach constructs temporal bidirectional bipartite graphs that effectively model the complex interactions within traffic scenes in real-time. By integrating Graph Neural Networks (GNNs) with high-order multi-aggregation strategies, we significantly enhance the modeling of traffic scene dynamics, providing a more accurate and detailed analysis of these interactions. Additionally, we incorporate inductive learning techniques inspired by the GraphSAGE framework, enabling our model to adapt to new and unseen traffic scenarios without the need for retraining, thus ensuring robust generalization. Through extensive experiments on the ROAD and ROAD Waymo datasets, we establish a comprehensive baseline for further developments, demonstrating the potential of our method in accurately capturing traffic behavior. Our results emphasize the value of high-order statistical moments and feature-gated attention mechanisms in improving traffic behavior analysis, laying the groundwork for advancing autonomous driving technologies. Our source code is available at: https://github.com/Addy-1998/High_Order_Graphs

翻译：我们提出了一种利用高阶演化图进行交通动力学分析的创新框架，旨在提升自动驾驶场景中的时空表示能力。该方法通过构建时序双向二分图，能够实时有效地建模交通场景中复杂的交互关系。通过将图神经网络（GNN）与高阶多聚合策略相结合，我们显著增强了交通场景动力学的建模能力，为这些交互作用提供了更精确细致的分析。此外，我们借鉴GraphSAGE框架的思想引入了归纳学习技术，使模型能够适应新的、未见过的交通场景而无需重新训练，从而确保了强大的泛化能力。通过在ROAD和ROAD Waymo数据集上进行大量实验，我们为后续研究建立了全面的基准，证明了该方法在准确捕捉交通行为方面的潜力。我们的结果凸显了高阶统计矩和特征门控注意力机制在改进交通行为分析中的价值，为推进自动驾驶技术奠定了基础。源代码已公开于：https://github.com/Addy-1998/High_Order_Graphs