Agent-SwinTF : An Agent-Based 3D Swin Transformer for Multi-Aircraft Trajectory Prediction in Game Scenarios

Fei Gong, Weiyi Ge, Ke Xie, Zhouwei Lou, Kanjian Zhang, Shuo Shan

Abstract: Accurate prediction of future trajectories of multiple aircraft is crucial for decision-making in dynamic game scenarios, but it is highly challenging due to the complex interactions among aircraft and the high dynamics of their movements. Existing models have limitations in capturing both individual characteristics and interactions in multi-trajectory prediction, as graph-based methods rely on predefined structures, social force models confuse individual information, and attention mechanisms continue to struggle with balancing local and global interactions. To address these problems, we propose Agent-SwinTF, a multi-trajectory prediction model based on the 3D Swin Transformer. Firstly, a novel Agent-based Patch method is introduced to maintain the independence of aircraft features within the window and to avoid cross-agent information interference. Secondly, an encoder-decoder framework is constructed using three Trajectory Specific Blocks to model local-global spatio-temporal interactions. Finally, a deep-shallow feature fusion module is built to enhance the representation of complex flight patterns by combining low-dimensional trajectory details from the encoder with high-dimensional semantic information from the decoder. On a self-constructed adversarial game simulation dataset, Agent-SwinTF achieves a Relative Displacement Error Ratio (RDER) value of 1.29, which significantly outperforms the baseline models. Additionally, the average displacement error (ADE) and final displacement error (FDE) are reduced by 52.81% and 25.52%, respectively, in the ablation experiments using the Patch-based approach. Experiments demonstrate that our method achieves superior performance in multi-aircraft trajectory prediction and provides strong support for dynamic game decision-making.

External IDs:dblp:conf/ijcnn/GongGXLZS25