Spatial-Temporal Dual Graph Neural Network for Pedestrian Trajectory Prediction

Pedestrian trajectory prediction has become a crucial task in the field of autonomous driving. To improve the accuracy of pedestrian trajectory prediction, researchers primarily concentrate on tackling two key challenges. One is to extract the intricate interactions between pedestrians, and the other is to simulate the diverse decision-making intentions displayed by pedestrians. However, most existing methods utilize the distance attribute to build the relationship of pedestrians only, but ignore other features such as the steering. Besides, some generation theory based methods would lead to substantial deviations in the generated trajectory distribution since they always refine the variational likelihood lower bound of observed data. In this paper, we adopt Graph theory and propose a Spatial-Temporal Dual Graph neural network for pedestrian trajectory prediction. In the proposed method, we construct a pedestrian graph structure by utilizing pedestrian distance and steering features to extract more comprehensive interaction information. Additionally, we introduces the flow-based Glow-PN module to predict multi-modal trajectories of pedestrians. Experimental results on two public benchmark datasets show that our model achieves superior prediction performance and operates effectively in diverse scenarios.