SDAGCN: Sparse Directed Attention Graph Convolutional Network for Spatial Interaction in Pedestrian Trajectory Prediction

Abstract

Pedestrian trajectory prediction is crucial across various domains, but remains challenging due to complex spatial interactions. Existing Graph Convolutional Network (GCN) methods show promise but often fail to capture these dynamics effectively. To address this limitation, a Sparse Directed Attention Graph Convolutional Network (SDAGCN) is proposed to handle both social interactions among pedestrians and self-interactions within individuals. Traditional GCN-based methods often model social interactions as undirected or dense graphs. However, due to the field of view and awareness of collision avoidance of pedestrians, they tend to focus unilaterally on specific neighbors. To reflect this, SDAGCN constructs a sparse and directed spatial graph that considers these attributes innovatively. Furthermore, the attention weights of pedestrians towards their neighbors are closely tied to spatial conflicts. The conflicts are deeply influenced by relative velocity and distance. Therefore, these attributes are leveraged to calculate the attention weights. These two components form the Sparse Directed Attention (SDA) mechanism, which effectively discerns the influence of neighbors on a target pedestrian in various situations. Additionally, the self-interaction of each pedestrian is significantly influenced by their speed. To capture variations in self-interaction across different states, SDAGCN employs a single-layer perceptron with the square of pedestrian speed as input. Experiments conducted on the ETH and UCY datasets demonstrate that our method outperforms other GCN-based spatial interaction methods, showcasing its potential in accurately predicting pedestrian trajectories by effectively handling complex social and self-interactions.