基于数字孪生的航班保障预警系统

传统机场场面滑行冲突检测主要依靠管制员目视判断冲突风险，随着航空运输量的增长，依靠人工判断方式的短板日益突出，急需一种降低人工依赖的冲突判定方法。目前大多数冲突模型存在模型与场面真实情况交互不及时等问题；因此本文提出一种基于数字孪生的机场场面滑行冲突预测模型，该模型通过物理系统与孪生系统数据交互从而及时对场面冲突进行预测。进一步，考虑孪生系统时延要求，基于4G、5G和AeroMACS(aeronautical mobile airport communications system)等机场场面主流的通信方式，进行了时延建模与分析。仿真结果表明,该模型能预测各冲突类型中航空器到达最小安全距离的时间，并在5G和AeroMACS组网条件下满足典型业务时延（0.5~20 ms），可为机场场面系统的网联化与智能化发展提供技术参考。

AGVS: A new change detection dataset for airport ground video surveillance

The traditional airport scene taxiing conflict detection mainly relies on the controllers to visually judge the conflict risk. With the increase of air traffic volume, the shortcomings of relying on manual judgment are increasingly prominent, and a conflict judgment method is urgent to reduce manual dependence. At present, most conflict models have some problems, such as the lack of timely interaction between the model and the real situation of the scene. Consequently, based on digital twin, a taxiing conflict prediction model in airport scene is proposed in this paper. The scene conflict can be predicted timely in this model through the data interaction between the physical system and the twin system. Further, considered the delay requirement of the twin system, the delay modeling and analysis are carried out based on the mainstream communication methods 4G, 5G and AeroMACS(aeronautical mobile airport communications system) in airport scene. The simulation results show that the delay of 5G and AeroMACS is between 0.5 and 20 ms, which meets the requirement of communication delay.