CR-LSTM: Collision-prior Guided Social Refinement for Pedestrian Trajectory Prediction

Abstract

Pedestrian trajectory prediction is a challenge because of the complex social interactions in context and the elusive intention of each pedestrian. Collision avoidance is one of the most common social interactions in real world, while existing data-driven works have not handled it well yet. In order to address this issue, we propose a framework that considers the theory about the minimum distance between each pedestrian-pedestrian pair and the corresponding time as the collision related prior knowledge. With the prior, our social refinement module, called Collision-prior Guided Refinement, can be guided to understand the collision situations of a crowd through a message passing mechanism. To focus on more useful information from context, we also introduce pedestrian-wise attention and collision gate to jointly judge collision potential for all pedestrian-pedestrian pairs. Experimental results demonstrate that our framework can achieve competitive results on ETH and UCY datasets by comparing with existing works. In addition, it indicates the superiority of our framework in the aspect of collision avoidance.