Dynamic Relational Graph Modeling for Multi-Agent Motion Trajectory Prediction
https://doi.org/10.35596/1729-7648-2025-23-4-109-117
Abstract
Accurate trajectory prediction of multiple agents is a critical task in the fields of autonomous driving, human-computer interaction, and behavior analysis. However, the dynamic and interactive nature of agent behavior poses significant challenges, since it requires the formation of complex spatio-temporal dependencies and dynamically evolving interactions between agents. A novel approach is proposed for modeling dynamic relational graphs, the core component of which is the attention focus block, taking into account the relative positions of graph-based agents. By considering objects in a scene (e.g., vehicles and road elements) as graph nodes and their interactions as edges, the proposed approach effectively captures both local and global dependencies in a scene and makes a prediction about the future trajectory. The presented approach is evaluated using the Argoverse1 trajectory prediction dataset. Experimental results show that this model outperforms existing methods.
About the Authors
Yi Tang
Belarusian State University of Informatics and Radioelectronics
Belarus
Belarus
Yi Tang, Postgraduate at the Department of Information Technologies in Automated Systems
220013, Minsk, Brovki St., 6
Tel.: +375 25 764-41-91
D. Yu. Pertsau
Belarusian State University of Informatics and Radioelectronics
Belarus
Belarus
Cand. Sci. (Tech.), Associate Professor, Associate Professor at the Department of Electronic Computer Science
Minsk
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Review
For citations:
Tang Y., Pertsau D.Yu. Dynamic Relational Graph Modeling for Multi-Agent Motion Trajectory Prediction. Doklady BGUIR. 2025;23(4):109-117. https://doi.org/10.35596/1729-7648-2025-23-4-109-117
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