  Pedestrian trajectory prediction, vital for selfdriving cars and
socially-aware robots, is complicated due to intricate interactions between
pedestrians, their environment, and other Vulnerable Road Users. This paper
presents GSGFormer, an innovative generative model adept at predicting
pedestrian trajectories by considering these complex interactions and offering
a plethora of potential modal behaviors. We incorporate a heterogeneous graph
neural network to capture interactions between pedestrians, semantic maps, and
potential destinations. The Transformer module extracts temporal features,
while our novel CVAE-Residual-GMM module promotes diverse behavioral modality
generation. Through evaluations on multiple public datasets, GSGFormer not only
outperforms leading methods with ample data but also remains competitive when
data is limited.
