IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v213y2021ics0951832021001630.html
   My bibliography  Save this article

Collision probability estimation for small unmanned aircraft systems

Author

Listed:
  • Zou, Yiyuan
  • Zhang, Honghai
  • Zhong, Gang
  • Liu, Hao
  • Feng, Dikun

Abstract

With the continuous expansion of the small unmanned aircraft market, the collision risk problem for small unmanned aircraft systems (sUAS) has been increasingly highlighted. In this paper, the rapid calculating methods of collision probability estimation for sUAS are proposed supposing that the predicted position error follows a certain Gaussian distribution. There are three types of collision zones established for collision modelling including cuboid, ellipsoid and cylinder. For each type of collision zones, a corresponding algorithm is derived for collision probability estimation based on the univariate conditioning or Laguerre polynomials. Randomized tests are conducted to validate the effectiveness of the proposed methods. The test results indicate that the average computation times of these three methods are approximately two orders of magnitude faster than the corresponding exact solutions, while the average computation errors are all less than 1%. Numerical simulations are carried out to analyze the differences in the collision probabilities when using different types of collision zones. The simulation results testify that the selection of the collision zones affects the collision probability estimation apparently, especially when the crossing angle of the two sUAS is between 0° and 40°.

Suggested Citation

  • Zou, Yiyuan & Zhang, Honghai & Zhong, Gang & Liu, Hao & Feng, Dikun, 2021. "Collision probability estimation for small unmanned aircraft systems," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
    • Handle: RePEc:eee:reensy:v:213:y:2021:i:c:s0951832021001630
    DOI: 10.1016/j.ress.2021.107619
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832021001630
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ress.2021.107619?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Wagner A. Kamakura, 1989. "The Estimation of Multinomial Probit Models: A New Calibration Algorithm," Transportation Science, INFORMS, vol. 23(4), pages 253-265, November.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Honghai Zhang & Yuhan Fei & Jingyu Li & Bowen Li & Hao Liu, 2022. "Method of Vertiport Capacity Assessment Based on Queuing Theory of Unmanned Aerial Vehicles," Sustainability, MDPI, vol. 15(1), pages 1-23, December.
    2. Ziegler Haselein, Bruno & da Silva, Jonny Carlos & Hooey, Becky L., 2024. "Multiple machine learning modeling on near mid-air collisions: An approach towards probabilistic reasoning," Reliability Engineering and System Safety, Elsevier, vol. 244(C).
    3. Wu, Pengcheng & Chen, Jun, 2024. "Data-driven zonotopic approximation for n-dimensional probabilistic geofencing," Reliability Engineering and System Safety, Elsevier, vol. 244(C).
    4. Dai, Wei & Quek, Zhi Hao & Low, Kin Huat, 2024. "Probabilistic modeling and reasoning of conflict detection effectiveness by tracking systems towards safe urban air mobility operations," Reliability Engineering and System Safety, Elsevier, vol. 244(C).
    5. Pang, Bizhao & Hu, Xinting & Dai, Wei & Low, Kin Huat, 2022. "UAV path optimization with an integrated cost assessment model considering third-party risks in metropolitan environments," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    6. Zhong, Gang & Du, Sen & Zhang, Honghai & Zhou, Jiangying & Liu, Hao, 2024. "Demarcation method of safety separations for sUAV based on collision risk estimation," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
    7. Honghai Zhang & Jingyu Li & Yuhan Fei & Cheng Deng & Jia Yi, 2023. "Capacity Assessment and Analysis of Vertiports Based on Simulation," Sustainability, MDPI, vol. 15(18), pages 1-22, September.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Haaijer, Marinus E., 1996. "Predictions in conjoint choice experiments : the x-factor probit model," Research Report 96B22, University of Groningen, Research Institute SOM (Systems, Organisations and Management).
    2. Martinetti, Davide & Geniaux, Ghislain, 2017. "Approximate likelihood estimation of spatial probit models," Regional Science and Urban Economics, Elsevier, vol. 64(C), pages 30-45.
    3. Bhat, Chandra R., 2018. "New matrix-based methods for the analytic evaluation of the multivariate cumulative normal distribution function," Transportation Research Part B: Methodological, Elsevier, vol. 109(C), pages 238-256.
    4. DeSarbo, Wayne S. & Kim, Youngchan & Wedel, Michel & Fong, Duncan K. H., 1998. "A Bayesian approach to the spatial representation of market structure from consumer choice data," European Journal of Operational Research, Elsevier, vol. 111(2), pages 285-305, December.
    5. Batram, Manuel & Bauer, Dietmar, 2019. "On consistency of the MACML approach to discrete choice modelling," Journal of choice modelling, Elsevier, vol. 30(C), pages 1-16.
    6. Kenetsu Uchida & Agachai Sumalee & David Watling & Richard Connors, 2007. "A Study on Network Design Problems for Multi-modal Networks by Probit-based Stochastic User Equilibrium," Networks and Spatial Economics, Springer, vol. 7(3), pages 213-240, September.
    7. Rinus Haaijer & Michel Wedel & Marco Vriens & Tom Wansbeek, 1998. "Utility Covariances and Context Effects in Conjoint MNP Models," Marketing Science, INFORMS, vol. 17(3), pages 236-252.
    8. Sumalee, Agachai & Tan, Zhijia & Lam, William H.K., 2009. "Dynamic stochastic transit assignment with explicit seat allocation model," Transportation Research Part B: Methodological, Elsevier, vol. 43(8-9), pages 895-912, September.
    9. Maher, M. J. & Hughes, P. C., 1997. "A probit-based stochastic user equilibrium assignment model," Transportation Research Part B: Methodological, Elsevier, vol. 31(4), pages 341-355, August.
    10. repec:dgr:rugsom:96b22 is not listed on IDEAS

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:reensy:v:213:y:2021:i:c:s0951832021001630. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/reliability-engineering-and-system-safety .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.