论文标题
使用UTM的机器学习辅助无人机操作：要求，挑战和解决方案
Machine Learning-Assisted UAV Operations with UTM: Requirements, Challenges, and Solutions
论文作者
论文摘要
无人驾驶飞机（UAV）正在商业空间中出现，并将支持许多应用程序和服务，例如智能农业，动态网络部署以及网络覆盖扩展，监视和安全性。无人飞机系统（UAS）流量管理（UTM）为通过通信网络集成了无人机控制器和中央数据库的安全无人机操作提供了一个框架。本文讨论了机器学习的挑战和机会（ML）有效地提供关键的UTM服务。我们介绍了UTM的四个支柱 - - 操作计划，情境意识，地位和顾问以及安全性---讨论主要服务，ML的具体机会以及正在进行的研究。我们得出的结论是，多方面的操作环境和操作参数将受益于收集的数据和数据驱动算法，以及在线学习以面对新的无人机操作情况。
Unmanned aerial vehicles (UAVs) are emerging in commercial spaces and will support many applications and services, such as smart agriculture, dynamic network deployment, and network coverage extension, surveillance and security. The unmanned aircraft system (UAS) traffic management (UTM) provides a framework for safe UAV operation integrating UAV controllers and central data bases via a communications network. This paper discusses the challenges and opportunities for machine learning (ML) for effectively providing critical UTM services. We introduce the four pillars of UTM---operation planning, situational awareness, status and advisors and security---and discuss the main services, specific opportunities for ML and the ongoing research. We conclude that the multi-faceted operating environment and operational parameters will benefit from collected data and data-driven algorithms, as well as online learning to face new UAV operation situations.
