SE-STDGNN: A Self-Evolving Spatial-Temporal Directed Graph Neural Network for Multi-Vehicle Trajectory Prediction

Abstract

Vehicle trajectory prediction (VTP) is essential for microscopic traffic risk assessment, autonomous vehicle navigation, and traffic behavior analysis. Related research leveraging learning-based methodologies has yielded notable success on various benchmark trajectory datasets. However, these models often experience performance degradation when faced with dynamic changes in traffic conditions such as vehicle density, road types, and weather conditions, as they have not been exposed to these variations during the training process. To effectively address the need for real-time adaptation in dynamic traffic scenarios, we propose a novel framework titled self-evolving spatial-temporal directed graph neural network (SE-STDGNN). This model utilizes evolving graph convolution networks (EvolveGCNs) to aggregate spatial-temporal features of vehicles and their neighbors, which are then utilized by a trajectory prediction module to forecast future trajectories. Further, a self-evolving mechanism is introduced to adjust model parameters dynamically in the real-time operation. The efficacy of SE-STDGNN is validated using the public vehicle trajectory dataset AD4CHE.