High-Order Evolving Graphs for Enhanced Representation of Traffic Dynamics

TL;DR

This paper introduces a novel high-order evolving graph framework that models complex traffic interactions in real-time, improving the accuracy and generalization of traffic dynamics analysis for autonomous driving.

Contribution

It proposes a new high-order evolving graph approach with GNNs and inductive learning for better traffic scene modeling and generalization to unseen scenarios.

Findings

Enhanced traffic behavior modeling accuracy

Effective generalization to new traffic scenarios

Significant improvement over baseline methods

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