Abstract: Pedestrian trajectory prediction is a fundamental and critical task in autonomous driving and intelligent surveillance systems.The constraints of the scene are one of the important factors affecting pedestrian movement trajectories.Despite existing research efforts to incorporate scene factors into trajectory prediction,these methods often fall short in integrating scene information,particularly in terms of comprehensive scene fusion.To overcome these limitations,this study proposes a new pedestrian trajectory prediction model,namely SPP-STGCN.The SPP-STGCN model adopts a two-stage architecture to enhance prediction accuracy.In the first stage,the model integrates trajectory and scene data.Through the Scene Adjacency Fusion Block(SAFB),the model fuses these two types of features to construct a spatio-temporal graph adjacency matrix that incorporates scene features,thereby providing rich contextual information for prediction.Concurrently,the model operates in parallel along the temporal and spatial dimensions,constructing pedestrian trajectory spatio-temporal graphs that describe temporal and spatial correlations based on trajectory information.In the second stage,scene-graph convolutional networks extract features from the temporal and spatial spatio-temporal graphs.The extracted features are then fused and processed through a temporal pyramid extrapolation convolution to obtain the two-dimensional Gaussian distribution of the pedestrian’s future trajectory.Finally,SPP-STGCN uses this distribution as a probabilistic model for predicting pedestrian trajectories,generating future trajectories through sampling.Comparative experimental results on the ETH and UCY public datasets show that the SPP-STGCN model has achieved the current state-of-the-art performance in comparison experiments with nine mainstream models.Ablation experiments and qualitative analysis further confirm the effectiveness and rationality of the proposed model.The SPP-STGCN model significantly enhances pedestrian trajectory prediction performance by effectively integrating scene features.

Key words: Pedestrian trajectory prediction, Scene-pedestrian-pedestrian interaction, Spatio-temporal graph convolution, Adjacency matrix generation, Attention mechanism