Abstract: One of the major challenges in autonomous driving technology is the real-time prediction of reliable future trajectory information for surrounding agents to facilitate optimal decision-making for path planning. This paper proposes an agent interaction prediction model  named GT-Former. Built upon the Transformer structure, the proposed model integrates graph convolutional network (GCN) to output dynamic interaction features among agents. Furthermore, the interaction between the map and agents utilizes agents’ features as query conditions, combining cross-attention and multi-modal attention mechanisms to integrate both mono-modal and multi-modal interaction information, thereby comprehensively capturing the interaction information between agents and various map features. Simulation experiments on the Waymo dataset demonstrate that this integrated strategy enhances the accuracy of multi- agent trajectory prediction of the model.

Key words: autonomous driving, trajectory prediction, Transformer model, graph convolutional network(GCN), cross-attention, multimodal attention