Conflict detection and resolution is one of the main topics in air traffic management. Traditional approaches to this problem use all the available information to predict future aircraft trajectories. In this work, we propose the use of a neural network to determine whether a particular configuration of aircraft in the final approach phase will break the minimum separation requirements established by aviation rules. To achieve this, the network must be effectively trained with a large enough database, in which configurations are labeled as leading to conflict or not. We detail the way in which this training database has been obtained and the subsequent neural network design and training process. Results show that a simple network can provide a high accuracy, and therefore, we consider that it may be the basis of a useful decision support tool for both air traffic controllers and airborne autonomous navigation systems.

中文翻译：

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最终进近演习中基于神经网络的飞机冲突预测

冲突检测和解决是空中交通管理的主要主题之一。解决该问题的传统方法是使用所有可用信息来预测未来的飞机轨迹。在这项工作中，我们建议使用神经网络来确定飞机在最终进近阶段的特定配置是否会违反航空规则规定的最小间隔要求。为此，必须使用足够大的数据库对网络进行有效的培训，在该数据库中，将配置标记为是否导致冲突。我们详细介绍了获取此训练数据库的方式以及随后的神经网络设计和训练过程。结果表明，简单的网络可以提供较高的准确性，因此，