Dynamic relational graph modeling for multi-agent motion trajectory prediction

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.