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Signed Relation Graph Based Dynamical Interacting System Modeling for Multi-Agent Trajectory Prediction
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AI
DOI：10.1109/TMM.2025.3645587.png)
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摘要
En 中文
Many complex systems prevalent in nature and society, from particle physics systems to social networks and team sports, can be viewed as dynamical interacting systems. Understanding the underlying interactions of agents in the system is the key task for predicting future behaviors of agents, which can be applied in various applications, e.g., autonomous vehicles and smart video surveillance. Since the interaction patterns between agents in the system can be dynamic and heterogeneous rather than fixed and homogeneous, it is very challenging to model interacting systems. In this paper, we design a novel graph structure called Signed Relation Graph (SRG) to model dynamical interacting systems. Since collective behaviors are very common in real-world scenes, our method is a group based model that takes heterogeneous relationships between agents into consideration, and achieves jointly modeling inter-group interactions and intra-group interactions. To assign signs on SRG, an unsupervised method called Relationship Reasoning Network is proposed. The relationship categories are reasoned explicitly, which makes handling multi-agent systems with multiple and dynamic interactions available. Further, Group Interaction Attention Graph Neural Network is proposed to aggregate information on SRG, which achieves not only reasoning the intensity of different interaction patterns but also modeling the trade-off between inter-group interactions and intra-group interactions. Our interacting systems modeling method can be used to predict multi-agent future trajectories in a variety of scenes with hard scenarios, including dense and drastic scenarios. Experimental results on three widely used human trajectory prediction datasets, including ETH and UCY in traffic scenes and NBA SportVU in sports scenes, demonstrate the effectiveness of our proposed model.
Keyword:
Dynamical interacting system modeling
human trajectory prediction
signed relation graph
relationship reasoning network