SocialTrans: Transformer based social intentions interaction for pedestrian trajectory prediction

Kai Chen, Xiaodong Zhao, Yujie Huang, GyFang

Abstract: The prediction of pedestrian trajectories plays a crucial role in practical traffic scenarios. However, current methodologies have shortcomings, such as overlooking pedestrians' perception of motion information from neighbor groups, employing simplistic and fixed social state interaction models, and lacking in final position correction. To address these issues, SocialTrans is proposed. It utilizes global observations to model the motion states of pedestrians and their neighbors, constructing separate state tensors to encapsulate social interaction information between them. This design includes a Subject Intention Extraction Module and a Neighbor Perception Intentions Extraction Module, which operate in parallel throughout the observation period to facilitate deep interaction of social states rather than simple end-to-end external fusion. Furthermore, a trajectory prediction optimizer is developed to correct final position predictions and simulate pedestrian motion diversity through trajectory clustering. Experimental validation is conducted on the ETH/UCY and SDD public datasets to evaluate the effectiveness of the proposed approach. The results demonstrate the method's capability to learn historical trajectory information, achieve high-precision predictions, and achieve state-of-the-art performance, particularly outperforming existing SOTA models on the SDD dataset. The algorithm will be made available at https://github.com/XiaodZhao/SocialTrans