DiTSG: Context-Aware Air Traffic Scene Generation with Multi-Agent Diffusion Transformers
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 ...
| Authors | Seokbin Yoon, Kyungmin Kim, Taejung Kwon et al. |
|---|---|
| Primary category | cs.MA |
| Published | 12 October 2026 |
| Last updated at source | 12 October 2026 |
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