Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/148322
Title: Collision probability between intruding drone and commercial aircraft in airport restricted area based on collision-course trajectory planning
Authors: Zhang, Na
Liu, Hu
Ng, Bing Feng
Low, Kin Huat
Keywords: Engineering
Issue Date: 2020
Source: Zhang, N., Liu, H., Ng, B. F. & Low, K. H. (2020). Collision probability between intruding drone and commercial aircraft in airport restricted area based on collision-course trajectory planning. Transportation Research Part C: Emerging Technologies, 120, 102736-. https://dx.doi.org/10.1016/j.trc.2020.102736
Journal: Transportation Research Part C: Emerging Technologies 
Abstract: With the significant improvements in drone technology and the popularization of drones among their hobbyists, the incidents of drones intruding airports have resulted in a large number of flight delays and temporary closure of runways. To minimize the interference of drones on normal operations in the airports, a collision probability evaluation scheme based on collision-course trajectories modeling is proposed in this work. Firstly, a trajectory planning model of drones intruding restricted airspace is derived based on a given trajectory of the commercial aircraft (CA) and the collision-course scenario of the drone. Subsequently, according to the trajectories of the drone and CA, a probabilistic model based on the stochastic kinematic model is developed to implement the collision risk evaluation. The proposed method is first comparatively demonstrated with the Monte-Carlo (MC) simulation and several special cases with known drone’s trajectories. Subsequently, the cases covering different drone’s initial positions, positions updates, and different collision zones are simulated and analyzed using the proposed collision-course based model. The simulation results show that the established model can be employed to evaluate the collision probability, even if the trajectory information of the intruding drone is limited.
URI: https://hdl.handle.net/10356/148322
ISSN: 0968-090X
DOI: 10.1016/j.trc.2020.102736
Rights: © 2020 Elsevier Ltd. All rights reserved. This paper was published in Transportation Research Part C: Emerging Technologies and is made available with permission of Elsevier Ltd.
Fulltext Permission: embargo_20221008
Fulltext Availability: With Fulltext
Appears in Collections:ATMRI Journal Articles

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  Until 2022-10-08
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