HM-GAT: Vehicle Trajectory Prediction Based on Hierarchical Mamba and Graph Attention Networks

Abstract

In highly dynamic and interactive vehicular environments, accurate and efficient trajectory prediction is critical for driving safety and traffic efficiency. To overcome the limitations of existing methods, including degradation of long-range historical context, high computational cost for long sequences, attention dispersion in spatial interactions, and limited explicit modeling of future interactions, this paper proposes a Hierarchical Mamba Graph Attention Network (HM-GAT) for vehicle trajectory prediction. A Mamba-based selective state-space model (SSM) encoder with linear complexity is employed to capture nonlinear temporal dynamics and transient maneuvering behaviors. For spatial modeling, a heterogeneous topology-driven dual-layer graph attention mechanism, combined with adaptive sparse filtering and a foresighted future graph, enables precise identification of neighboring vehicles with potential collision risks. The proposed architecture integrates global intention exploration, topology bridging, and residual refinement in a coarse-to-fine manner, achieving effective spatio-temporal decoupling. Experimental results show that HM-GAT reduces the average RMSE by 33.1% relative to the best-performing baseline on NGSIM, achieves competitive prediction accuracy on Argoverse 2 without HD-map priors, and achieves lower inference latency than the compared baseline methods on both datasets.