WR-SRPG: Joint Walking Rhythm and Social Relation-Potential for Pedestrian Trajectory Prediction

Humans are not robots with predefined path planning rules. Pedestrian trajectory prediction is effected by the physical world's laws, social space and their own personality. In this paper, we represent a novel insight walking rhythm and social relation-potential model (WR-SRPG) to solve these problems. We propose a new way to measure the information of pedestrian walking characteristics and personality characteristics. We design a Social Relation-Potential Grid (SRPG) module to model the complex social interaction between pedestrians. And we integrate multi-head attention mechanism, position encoding and remote estimation points to expand the ability of the model to focus on different locations and model long-term spatiotemporal dependence. The experimental results show that our method surpass state-of-the-art method on ETH&UCY datasets in ADE and FDE, and successfully predicts the complex social behaviors among pedestrians.