  Pedestrian trajectory prediction is valuable for understanding human motion
behaviors and it is challenging because of the social influence from other
pedestrians, the scene constraints and the multimodal possibilities of
predicted trajectories. Most existing methods only focus on two of the above
three key elements. In order to jointly consider all these elements, we propose
a novel trajectory prediction method named Scene Gated Social Graph (SGSG). In
the proposed SGSG, dynamic graphs are used to describe the social relationship
among pedestrians. The social and scene influences are taken into account
through the scene gated social graph features which combine the encoded social
graph features and semantic scene features. In addition, a VAE module is
incorporated to learn the scene gated social feature and sample latent
variables for generating multiple trajectories that are socially and
environmentally acceptable. We compare our SGSG against twenty state-of-the-art
pedestrian trajectory prediction methods and the results show that the proposed
method achieves superior performance on two widely used trajectory prediction
benchmarks.
