ADP-Net: a hierarchical attention-diffusion-prediction framework for human trajectory prediction.

ABSTRACT: Accurate prediction of human crowd behavior presents a significant challenge with critical implications for autonomous systems. The core difficulty lies in developing a comprehensive computational framework capable of effectively modeling the spatial-temporal dynamics through three essential components: feature extraction, attention propagation, and predictive modeling. Current spatial-temporal graph convolutional networks (STGCNs), which typically employ single-hop neighborhood message passing with optional self-attention mechanisms, exhibit three fundamental limitations: restricted receptive fields due to being confined to limited propagation steps, poor topological extensibility, and structural inconsistencies between network components that collectively lead to suboptimal performance.

SUBMITTER: Zhang Z 

PROVIDER: S-EPMC12696591 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature