Ground risk, as one of the key parameters for assessing risk before an operation, plays an important role in the safety management of unmanned aircraft systems. However, how to correctly identify ground risk and to predict risk accurately remains challenging due to uncertainty in relevant parameters (people density, ground impact, etc.). Therefore, we propose a dynamic model based on a deep learning approach to assess the ground risk. First, the parameters that affect ground risk (people density, ground impact, sheltered, etc.) are defined and analyzed. Second, a kinetic-theory-based model is applied to assess the extent of ground impact. Third, a joint convolutional neural network–deep neural network model (C-Snet model) is built to predict the density of people on the ground and to calculate the shelter factor for different degrees of ground impact. Last, a dynamic model combining a deep learning and a kinetic model is established to predict ground risk. We performed simulations to validate the effectiveness and efficiency of the model. The results indicate that ground risk has spatial-temporal characteristics and that our model can predict risk accurately by capturing these characteristics.


    Zugriff

    Download


    Exportieren, teilen und zitieren



    Titel :

    Ground Risk Assessment for Unmanned Aircraft Systems Based on Dynamic Model


    Beteiligte:
    Qingyu Jiao (Autor:in) / Yansi Liu (Autor:in) / Zhigang Zheng (Autor:in) / Linshi Sun (Autor:in) / Yiqin Bai (Autor:in) / Zhengjuan Zhang (Autor:in) / Longni Sun (Autor:in) / Gaosheng Ren (Autor:in) / Guangyu Zhou (Autor:in) / Xinfeng Chen (Autor:in)


    Erscheinungsdatum :

    2022




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Unbekannt




    Dynamic Probabilistic Risk Assessment of Unmanned Aircraft Adaptive Flight Control Systems

    Hejase, Mohammad / Kurt, Arda / Aldemir, Tunc et al. | AIAA | 2018


    Preliminary Risk Assessment for Small Unmanned Aircraft Systems

    Barr, Lawrence C. / Newman, Richard / Ancel, Ersin et al. | AIAA | 2017


    Unmanned Aircraft Systems Ground Support Platform

    MANITTA SALVATORE | Europäisches Patentamt | 2016

    Freier Zugriff

    A review of unmanned aircraft system ground risk models

    Washington, Achim / Clothier, Reece A. / Silva, Jose | Elsevier | 2017


    A review of unmanned aircraft system ground risk models

    Washington, Achim | Online Contents | 2017