A Deep Learning Framework of Autonomous Pilot Agent for Air Traffic Controller Training
A Deep Learning Framework of Autonomous Pilot Agent for Air Traffic Controller Training
复制标题
DOI：
10.1109/thms.2021.3102827
复制
发表时间：
2021-10-01
影响因子：
3.6
通讯作者：
Zhang, Jianwei
中科院分区：
文献类型：
--
作者：
Lin, Yi;Wu, YuanKai;Zhang, Jianwei
在这项工作中，提出了一个基于深度学习的框架来实现一个自主飞行员代理（阿帕），它作为一个人类的伪飞行员，以协助空中交通管制员（ATCO）的培训。一个新的范例，包括语音识别，语言理解，飞行员重复生成（PRG），和文本到语音（TTS），设计制定的框架管道，其中还包括一个仿真系统接口。我们主要关注PRG和TTS模型，以解决这项工作中的ATC特殊性。提出了一种神经网络结构，通过序列到序列的文本映射来生成文本重复指令。改进了Transformer模块，实现了一个高效的TTS模型，并采用非自回归机制实现了并行合成。一个专门的音素词汇表的设计，以科普在ATC领域的多语言问题，并解决了词汇外的问题。在阿帕框架下，提出了一种虚拟训练模式，可以不受时间和地点的限制完成训练任务。真实世界数据集上的实验结果表明，所提出的阿帕框架在仿真训练过程中以实时的方式以相当高的置信度代替人类飞行员。最重要的是，阿帕框架和虚拟培训系统能够科普实际出勤的困境（如COVID-19），并提高ATCO培训的设备利用率。
In this work, a deep learning-based framework is proposed to implement an autonomous pilot agent (APA), which serves as a human pseudo-pilot to assist air traffic controller (ATCO) training. A novel paradigm, including speech recognition, language understanding, pilot repetition generation (PRG), and text-to-speech (TTS), is designed to formulate the framework pipeline, which also incorporates a simulation system interface. We mainly focus on the PRG and TTS models to address the ATC specificities in this work. The neural architecture is proposed to generate the text repetition instruction by using a sequence-to-sequence text mapping. The Transformer block is improved to implement a high-efficient TTS model, in which the nonautoregressive mechanism is applied to achieve the parallel synthesis. A dedicated phoneme vocabulary is designed to cope with the multilingual issue in the ATC domain and address the out-of-vocabulary problem. With the APA framework, a virtual training mode is proposed to complete the training task without the limitation of time and location. Experimental results on a real-world dataset show that the proposed APA framework replaces the human pilot with considerable high confidence in a real-time manner during the simulation training. Most importantly, the APA framework and the virtual training system are able to cope with the dilemma of physical attendance (like COVID-19) and improve the equipment utilization capacity for the ATCO training.