Vehicle Trajectory Prediction Based on Dynamic Graph Neural Network

Predicting vehicle trajectories is a crucial component of intelligent transportation systems, bearing significant research significance. Leveraging the latest advancements in deep learning and data processing technologies enables us to model intricate interactions among multiple vehicles in complex traffic scenarios. Traditional trajectory prediction methods typically rely on sensor data and vehicle behavior models, which may struggle to capture the intricate relationships between vehicles in dynamic, high-traffic situations, as well as the topological complexities of road networks. To tackle these challenges, we introduce a novel approach: the Vehicle-Driven Dynamic Graph Neural Network (V-DGNN) model. This model starts by constructing an interaction graph among vehicles, enabling the simultaneous capture of both temporal and spatial dependencies between them. Moreover, it incorporates a spatiotemporal attention network for extracting vehicle motion patterns. We also propose a unique mechanism to address the challenges posed by high-speed spatiotemporal changes. This mechanism involves the sensitive sampling of nearby timestamps to effectively learn the dynamic distribution of vehicles. Additionally, the model incorporates vehicle behavior features and road network topology information as supplementary inputs while minimizing prediction variances. This equips our model with the ability to make robust predictions even in the face of distribution changes. Experimental results on two real-world datasets convincingly demonstrate that our approach outperforms current state-of-the-art models, delivering superior long-term predictive performance.