MGFI: Multi-graph Fusion and Future Interaction-Aware Network for Heterogeneous Trajectory Prediction

Abstract

Trajectory prediction serves as a pivotal component within the autonomous driving technology stack. However, predicting trajectories in heterogeneous traffic environments remains a formidable challenge, primarily due to the intricate interactions among agents and the significant distinctness in kinematic patterns across different object categories. To address these issues, we propose a novel trajectory prediction framework named Multi-graph Fusion and Future Interaction-Aware Network (MGFI). Specifically, we first construct a multi-graph spatio-temporal feature encoding module designed to comprehensively capture complex interaction information among traffic participants. This module employs four independent graph neural network branches on heterogeneous nodes. To synthesize the node features derived from these branches, we introduce a hierarchical feature fusion module that selectively aggregates features from the aforementioned multi-graph features. Finally, the fused node representations are fed into a sequence prediction module, which is further augmented by anticipated future interaction features to enhance prediction accuracy. Extensive experiments conducted on ApolloScape dataset demonstrate that the proposed MGFI significantly outperforms state-of-the-art trajectory prediction algorithms in terms of both prediction accuracy and robustness.