  As a core technology of the autonomous driving system, pedestrian trajectory
prediction can significantly enhance the function of active vehicle safety and
reduce road traffic injuries. In traffic scenes, when encountering with
oncoming people, pedestrians may make sudden turns or stop immediately, which
often leads to complicated trajectories. To predict such unpredictable
trajectories, we can gain insights into the interaction between pedestrians. In
this paper, we present a novel generative method named Spatial Interaction
Transformer (SIT), which learns the spatio-temporal correlation of pedestrian
trajectories through attention mechanisms. Furthermore, we introduce the
conditional variational autoencoder (CVAE) framework to model the future latent
motion states of pedestrians. In particular, the experiments based on
large-scale trafc dataset nuScenes [2] show that SIT has an outstanding
performance than state-of-the-art (SOTA) methods. Experimental evaluation on
the challenging ETH and UCY datasets conrms the robustness of our proposed
model
