Tri-HGNN: Learning triple policies fused hierarchical graph neural networks for pedestrian trajectory prediction

Abstract

In complex and dynamic urban traffic scenarios, the accurate trajectory prediction of surrounding pedestrians with interactive behaviors plays a vital role in the self-driving system. Intrinsic factors and extrinsic factors will inevitably influence the pedestrians trajectory. Intrinsic factors such as pedestrians diversified intentions bring rich and diverse multi-modal future possibilities. Besides, extrinsic factors affecting the future trajectory are accompanied by context semantics such as interactions among pedestrians. However, most of the existing methods discuss two problems (interaction and intention) separately. Considering both two factors impact the trajectory of pedestrians, a Triple Policies Fused Hierarchical Graph Neural Networks (Tri-HGNN) is proposed to model spatial and temporal interactions and intentions among the whole scene of pedestrians at each time step and predict the multiple future trajectories. Tri-HGNN contains three different policies: (i) Extrinsic-level policy is used to extract spatial nodes embedding from the interaction graph of pedestrian trajectories by using the Graph Attention Network. (ii) Intrinsic-level policy adopts the Graph Convolutional Network to infer the human intention for more accurate prediction. Moreover, human intention is influenced by the intrinsic interaction generated among pedestrians, so we fuse the interaction features to grasp the influence of the extrinsic interaction. (iii) Basic-level policy then integrates the heuristic information obtained from other two policies and concatenates it with historical trajectories to make multiple predictions through Temporal Convolution Network. Experimental results show that our model improves performance compared with state-of-the-art methods on the ETH/UCY and SDD benchmarks.