Open Access

ARTICLE

Dynamic Interaction-Aware Trajectory Prediction with Bidirectional Graph Attention Network

Jun Li^#,*, Kai Xu^#,*, Baozhu Chen, Xiaohan Yang, Mengting Sun, Guojun Li, HaoJie Du

School of Computer Science and Information, Anhui Polytechnic University, Wuhu, 241000, China

* Corresponding Authors: Jun Li. Email: ; Kai Xu. Email:
#These authors contributed equally to this work

Computers, Materials & Continua 2025, 85(2), 3349-3368. https://doi.org/10.32604/cmc.2025.067316

Received 29 April 2025; Accepted 17 July 2025; Issue published 23 September 2025

Abstract

Pedestrian trajectory prediction is pivotal and challenging in applications such as autonomous driving, social robotics, and intelligent surveillance systems. Pedestrian trajectory is governed not only by individual intent but also by interactions with surrounding agents. These interactions are critical to trajectory prediction accuracy. While prior studies have employed Convolutional Neural Networks (CNNs) and Graph Convolutional Networks (GCNs) to model such interactions, these methods fail to distinguish varying influence levels among neighboring pedestrians. To address this, we propose a novel model based on a bidirectional graph attention network and spatio-temporal graphs to capture dynamic interactions. Specifically, we construct temporal and spatial graphs encoding the sequential evolution and spatial proximity among pedestrians. These features are then fused and processed by the Bidirectional Graph Attention Network (Bi-GAT), which models the bidirectional interactions between the target pedestrian and its neighbors. The model computes node attention weights (i.e., similarity scores) to differentially aggregate neighbor information, enabling fine-grained interaction representations. Extensive experiments conducted on two widely used pedestrian trajectory prediction benchmark datasets demonstrate that our approach outperforms existing state-of-the-art methods regarding Average Displacement Error (ADE) and Final Displacement Error (FDE), highlighting its strong prediction accuracy and generalization capability.

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Keywords

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