Edge-Enhanced Heterogeneous Graph Transformer With Priority-Based Feature Aggregation for Multi-Agent Trajectory Prediction

Trajectory prediction, which aims to predict the future positions of all agents in a crowd scene, given their past trajectories, plays a vital role in improving the safety of autonomous driving vehicles. For heterogeneous agents, it is imperative to account for the gap in feature distribution differences between agents in different categories. Besides, exploring the reference relationship between the future motions of agents is crucial yet overlooked in previous trajectory prediction methods. To tackle these challenges, we propose an edge-enhanced heterogeneous graph Transformer with priority-based feature aggregation for multi-modal trajectory prediction. Specifically, a new edge-enhanced heterogeneous interaction module that carries relative position information via edges is proposed to explore the complex interaction among agents. Additionally, we propose the concept of priority during the decoding phase and the corresponding measuring method, based on which a priority-based feature aggregation module is presented to enable referencing between agents, allowing for a more reasonable trajectory generation process. Additionally, we design an effective feature fusion method based on state refinement LSTM so that temporal and social features can be well integrated while accounting for their roles in trajectory prediction. Extensive experimental results on public datasets demonstrate that our approach outperforms the state-of-the-art baseline methods, confirming the effectiveness of our proposed method. The source code of our EPHGT model will be publicly released at https://github.com/xbchen82/EPHGT.