SocialMP: Learning Social Aware Motion Patterns via Additive Fusion for Pedestrian Trajectory Prediction

Abstract

Accurately capturing social interaction in complex scenarios is essential for pedestrian trajectory prediction task. The uncertainty in pedestrian interactions and the physical constraints imposed by the environment make this task challenging. To solve this problem, existing methods adopt dimensionality reduction algorithms to capture explainable human motions and behaviors. However, these approaches not only suffer from weak social awareness due to the inadequate feature extraction, but also overlook physical constraints, leading to predicted trajectories often cross unwalkable areas. To overcome these problems, we build an attention-based motion pattern representation, named SocialMP, which can effectively enhance the social awareness and environmental perception of motion patterns. Specifically, our method first characterizes the motion patterns through singular value decomposition and defines a visual field-based rule to model environmental social interaction. Then, an attention-based additive fusion mechanism is designed to enhance social awareness and environment perception of motion patterns. Therein, we integrate social interactions into motion patterns through cross-attention mechanism to generate latent motion patterns, and feed them into our devised additive fusion structure with backward connection for multiple iterations. Lastly, we design a map loss function by applying an additional penalty into average displacement error to prevent the pedestrians from passing through the unwalkable area. Extensive experiments on ETH-UCY and SDD datasets demonstrate that our SocialMP can not only improve prediction accuracy but also generate plausible trajectories.