High-Order Evolving Graphs for Enhanced Representation of Traffic Dynamics

TL;DR

An innovative framework for traffic dynamics analysis using High-Order Evolving Graphs, designed to improve spatio-temporal representations in autonomous driving contexts, and integrates Graph Neural Networks with high-order multi-aggregation strategies to significantly enhance the modeling of traffic scene dynamics.

Abstract

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

Figures (3)

• Figure 1: (a) Temporal bidirectional bipartite graph construction from spatio-temporal tube features. (b) Inductive learning framework updating root node features via generated nodes through statistical aggregation from neighbors.
• Figure 2: The proposed pipeline begins with (a) graph construction, where nodes receive features extracted from spatio-temporal tubes via a 3D CNN. The graph is then processed through (b) high-order multi-aggregation based inductive learning layers, followed by (c) feature-level gated attention aggregation and a fully connected linear layer for classification.
• Figure 3: Illustration of the multi-aggregation process in the high-order inductive learning mechanism as discussed in Section \ref{['sub:high-order-graph-learning']}. The top block shows the graph convolution where the root node is updated through transformations and aggregation. The bottom block details the statistical multi-aggregation applied to neighboring nodes, with features concatenated and projected to enhance node representations.