DiTSG:面向上下文感知的空中交通场景生成的多智能体扩散Transformer
DiTSG: Context-Aware Air Traffic Scene Generation with Multi-Agent Diffusion Transformers
Seokbin Yoon · Kyungmin Kim · Taejung Kwon · Keumjin Lee · Peng Wei
中文摘要
在给定空域内，空中交通管制员能安全高效管理多少架飞机、哪些交通构型，是空中交通管理的基础问题。管制员通过速度建议和雷达引导等干预持续修改航空器轨迹，现有空中交通模拟器难以有效捕捉这些干预模式。为此，提出用于交通场景生成的扩散Transformer（DiTSG），从记录的多航空器轨迹中学习生成空中交通场景。DiTSG采用堆叠的多智能体倒置Transformer层作为扩散Transformer的去噪骨干，联合生成交通场景内多架航空器的轨迹，并以各智能体特定的要求到达时间作为生成条件。DiTSG生成的轨迹保留了实际空中交通运行中观察到的真实航空器间交互。实验表明，DiTSG生成多样化的交通场景，再现记录交通的统计特性，同时保持真实的航空器间交互。
关键要点
- 01现有空中交通模拟器难以捕捉管制员速度建议与雷达引导等干预模式
- 02DiTSG用堆叠多智能体倒置Transformer层作扩散Transformer去噪骨干
- 03联合生成多航空器轨迹，并以各智能体要求到达时间为条件
- 04生成场景再现记录交通统计特性并保持真实航空器间交互
解读
尚无解读。
原始英文摘要
arXiv:2610.16959v2 Announce Type: replace-cross Abstract: Understanding how many aircraft, and which traffic configurations, can be safely and efficiently managed by air traffic controllers within a given airspace is fundamental to air traffic management. Air traffic controllers continuously modify aircraft trajectories through interventions such as speed advisories and radar vectoring, and the patterns of these interventions are difficult to capture effectively using existing air traffic simulators. To address this limitation, we propose Diffusion Transformers for Traffic Scene Generation (DiTSG), which learns to generate air traffic scenes from recorded multi-aircraft trajectories. DiTSG employs stacked multi-agent inverted Transformer layers as the denoising backbone of a diffusion transformer, jointly generates the trajectories of multiple aircraft within a traffic scene, and conditions the generation on agent-specific required times of arrival. The resulting trajectories generated by DiTSG preserve realistic inter-aircraft interactions observed in actual air traffic operations. Experiments show that the DiTSG generates diverse traffic scenes that reproduce the statistical properties of recorded traffic while preserving realistic inter-aircraft interactions.