CSGAT-Net: a conditional pedestrian trajectory prediction network based on scene semantic maps and spatiotemporal graph attention

Pedestrian behavior exhibits high levels of dynamism, and pedestrian trajectories are influenced not only by the pedestrians themselves, but also by interactions with surrounding objects. Efficiently understanding pedestrian motion behavior and modeling its interactions play a crucial role in fields like autonomous driving. Addressing issues related to dynamic feature extraction and interaction modeling in pedestrian trajectory prediction tasks, this paper introduces the conditional pedestrian trajectory prediction network (CSGAT-Net) based on semantic segmentation maps and spatiotemporal graph attention. CSGAT-Net models the physical environment and pedestrian behavior information in the scene as a semantic map, and it leverages graph attention networks to extract pedestrian interaction features. Finally, it predicts pedestrian future trajectories using a variational autoencoder. Comparative experiments conducted on publicly available datasets, ETH and UCY, show that our model exhibits favorable objective evaluation metrics and subjective prediction performance. Particularly, in terms of ADE and FDE metrics, CSGAT-Net outperforms current state-of-the-art methods, indicating that our model can reasonably and accurately predict pedestrian trajectories in different scenarios.