Predicting Conflict Free Trajectories Using Supervised Machine Learning, Initial Investigations

R. Christien, K. Zeghal, E. Hoffman
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引用次数: 1

Abstract

This paper presents initial investigations on the prediction of conflict free aircraft trajectories using supervised machine learning. The motivation is to generate trajectory proposals to resolve conflicts based on current practices (imitation learning) as a way to get controller acceptability. The paper explores two distinct approaches. The first one takes a pilot point of view with a flight centred representation of the surrounding traffic, while the second one takes a controller point of view with a sector-based representation of the traffic. In addition, for the first approach, the traffic input is represented by an image going into a convolutional neural network, while in the second it is represented by a list of flights parameters going into a feed forward neural network.The case study addressed is to predict conflict free trajectories with a 5 minutes look-ahead. It relies on recorded traffic data from 2018 from a busy European en-route centre (Maastricht UAC) used to draw a 250k data set. This dataset was split in two 50% sub-sets: one with no change in vertical and/or horizontal dimension, the other with a change (change thresholds of 1000ft and 2NM determined statistically). The performance of both models is compared to a baseline to ensure a learning has been achieved. For the best model (sector based), the median deviations between the prediction and the true future locations are 0.4NM and 23ft "with no change", and 1.3NM and 500ft "with change". These results show that relevant information has been extracted and a mapping between inputs and outputs achieved. However, the prediction error remains quite significant compared to separation standards (5NM, 1000ft).Future work will investigate further both models, in particular analyze error (focus on bad performance cases patterns) and improving them, and later, adding other information (e.g. military areas, meteo).
使用监督机器学习预测无冲突轨迹,初步调查
本文介绍了使用监督式机器学习预测无冲突飞机轨迹的初步研究。动机是生成轨迹建议,以解决基于当前实践的冲突(模仿学习),作为获得控制器可接受性的一种方法。本文探讨了两种不同的方法。第一个采用飞行员的观点,以飞行为中心表示周围的交通,而第二个采用管制员的观点,以扇区为基础表示交通。此外,对于第一种方法,流量输入由进入卷积神经网络的图像表示,而在第二种方法中,它由进入前馈神经网络的航班参数列表表示。本案例研究的目的是提前5分钟预测无冲突轨迹。它依赖于2018年繁忙的欧洲航线中心(马斯特里赫特UAC)记录的交通数据,用于绘制25万数据集。该数据集被分成两个50%的子集:一个子集在垂直和/或水平维度上没有变化,另一个子集有变化(统计确定的1000英尺和2NM的变化阈值)。将两个模型的性能与基线进行比较,以确保实现了学习。对于最佳模型(基于扇区),预测和真实未来位置之间的中位数偏差为0.4NM和23英尺,“没有变化”,以及1.3NM和500英尺“有变化”。这些结果表明,相关信息已被提取,输入和输出之间的映射实现。然而,与分离标准(5NM, 1000ft)相比,预测误差仍然相当大。未来的工作将进一步研究这两个模型,特别是分析错误(关注性能差的案例模式)并改进它们,然后添加其他信息(例如军事领域、气象)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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