HHGNN: Heterogeneous Hypergraph Neural Network for Traffic Agents Trajectory Prediction in Grouping Scenarios

In many intelligent transportation systems, pre- dicting the future motion of heterogeneous traffic participants is a fundamental but challenging task due to various factors encompassing the agents’ dynamic states, interactions with neighboring agents and surrounding traffic infrastructures, and their stochastic and multi-modal natural behavior tendencies. However, existing approaches have limitations as they either fo- cus solely on static, pairwise interactions, ignoring interactions of varied granularity, or fail to tackle agents’ heterogeneity. In this paper, instead of focusing solely on pairwise interactions, we propose a Heterogenous Hypergraph Graph Neural Network (HHGNN) based motion prediction model that leverages the nature of hypergraph to encode the groupwise interactions among traffic participants. Moreover, we propose the type- aware two-level hypergraph message passing module (TTHMS) with learnable hyperedge-type embeddings to model the intra- group and inter-group level interactions among heterogeneous traffic agents (e.g., vehicles, pedestrians, and cyclists). Besides, We integrate a scene context fusion layer in TTHMS to incorpo- rate the scene context. Comparison and ablation experiments on the Waymo Open Motion Dataset (WOMD) demonstrate HHGNN’s effectiveness within the motion prediction task.