Vehicle Interactive Dynamic Graph Neural Network-Based Trajectory Prediction for Internet of Vehicles

In the context of the booming Internet of Vehicles, predicting vehicle trajectories is crucial for intelligent transportation systems. Existing methods, reliant on sensor data and behavior models, struggle with intricate relationships between vehicles and dynamic road networks. To overcome these challenges, we propose the vehicle interaction-based dynamic graph neural network (VI-DGNN) model. This model constructs a vehicle interaction graph to capture temporal and spatial dependencies among vehicles. A spatiotemporal attention network is employed to discern patterns in vehicle movements, addressing high-speed changes. Our model introduces a vehicle interaction mechanism for dynamic movement, leveraging proximity timestamp graph structures. By incorporating vehicle behavioral features and road network topology, our model minimizes distribution prediction variance, enhancing stability. Experimental results on real data sets demonstrate superior long-term prediction performance compared to state-of-the-art baselines.

I. Introduction

The rapid evolution of the Internet of Vehicles (IoV) has ushered in a new era of intelligent transportation systems [1], [2]. As vehicles become increasingly connected and autonomous, the ability to predict their future trajectories becomes paramount [3]. Accurate trajectory prediction can enhance traffic safety, optimize traffic flow, and facilitate the development of advanced driver-assistance systems [4]. However, the dynamic nature of traffic environments, coupled with the intricate interactions between vehicles, makes this task particularly challenging [5].