SoPerModel: Leveraging Social Perception for Multi-Agent Trajectory Prediction

Trajectory prediction is an essential task within various automation systems. Recent studies have highlighted that the social interactions among multiple agents are crucial for accurate predictions, relying on empirically derived human-imposed constraints to model these interactions. However, from a sociological perspective, agents’ interactions exhibit significant inherent randomness. Dependence on a priori knowledge may lead to biased estimations of data distributions across different scenarios, failing to account for this randomness. Consequently, such methodologies often do not comprehensively capture the full spectrum of social influences, thus limiting the models’ predictive efficacy. To address these issues, we propose a novel multi-agent trajectory prediction framework, SoPerModel, which incorporates a freeform social evolution module (FSEM) and a local perception attention mechanism (LPA). The FSEM enables SoPerModel to naturally capture representative social interactions among agents without the reliance on additional human-derived priors. Through LPA, the model integrates both local and global social interaction information and leverages them to enhance trajectory prediction performance. Our framework is empirically evaluated on real-world trajectory prediction datasets, and the results demonstrate that our approach achieves a highly competitive performance compared with state-of-the-art models.