In this work, a novel framework is proposed to improve air traffic safety from the perspective of human factors by detecting possible risks from air-ground speech communication. In the proposed framework, the automatic speech recognition and the spoken instruction understanding techniques serve as an interface to sense real-time traffic dynamics of airspace, which are achieved by deep learning-based data-driven models. Based on the obtained dynamics, several applications are designed to detect safety-critical risks of air-ground communication, including the normative instruction check, readback confirmation, trajectory conformance, and potential aircraft conflict detection. A prototype is developed to implement the proposed framework. To validate the proposed framework and prototype, we deploy them in the Chengdu area control center, Changsha aerodrome and approach, China. The data statistics collected from the real-world environment demonstrate that the proposed framework has the ability to improve air traffic safety and reduce the workload of air traffic controllers by understanding the traffic dynamics automatically. In addition, the performance of the core techniques is also confirmed by extensive data investigation.


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    Title :

    Data-driven traffic dynamic understanding and safety monitoring applications


    Contributors:
    Zhou, Shizhong (author) / Hu, Yi (author) / Guo, Dongyue (author) / Lin, Yi (author) / Yang, Bo (author)


    Publication date :

    2022-10-12


    Size :

    1508025 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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