Detection and Prediction of a Pair of Unmanned Aircraft Contact

Keywords: conflict, conflict space, algorithm, UAV, prediction


In the current world of increasing density of unmanned aerial vehicle operations in the airspace, there is an enhanced emphasis on their safety due to the potential for mid-air collision, either with another aircraft or with each other. At the same time, unmanned aerial vehicles are also being used in the context of introducing smart technologies into maintenance processes, where there is also a need to prevent a potentially possible conflict when two drones come close together. The paper introduces a mathematical model for tactical prediction of a conflict between a pair of drones. The tactical prediction of drone conflict is intended to alert the drone operator to an immediate potentially dangerous situation. The mathematical simulation in this paper extrapolates the 3D trajectory in the direction of the relative velocity vector of the convergence over the advance time. If the extrapolated trajectory has at least one point in common with the conflict space of the other drone, the conflict is signalled to the drone operator. This model can then be used in practice to simulate flight operations in shared airspace or to develop the currently required rules in selected situations.


Novák A, et al. Use of unmanned aerial vehicles in aircraft maintenance. Transportation Research Procedia. 2020;51: 160-170. doi: 10.1016/j.trpro.2020.11.018.

Bugaj M, Novák A, Stelmach A, Lusiak T. Unmanned aerial vehicles and their use for aircraft inspection. Proceedings of the 22nd International Conference on New Trends in Civil Aviation. 2020. p. 45-50. doi: 10.23919/NTCA50409.2020.9290929.

Hruz M, et al. The use of UAV with infrared camera and RFID for airframe condition monitoring. Appl. Sci. 2021;11(9): 3737. doi: 10.3390/app11093737.

Havel K, Husarčík J. A theory of the tactical conflict prediction of a pair of aircraft. The Journal of Navigation. 1989;42(3): 417-429. doi: 10.1017/S0373463300014715.

D'Amato E, Notaro I, Mattei M. Distributed collision avoidance for unmanned aerial vehicles integration in the civil airspace. 2018 International Conference on Unmanned Aircraft Systems (ICUAS). 2018. p. 94-102. doi: 10.1109/ICUAS.2018.8453432.

Hu J, Erzberger H, Goebel K, Liu Y. Conflict probability estimation using a riskbased dynamic anisotropic operational safety bound for UAV traffic management. AIAA Scitech 2020 Forum. 2020. doi: 10.2514/6.2020-0738.

Jover J, Bermúdez A, Casado R. A tactical conflict resolution proposal for U-Space Zu airspace volumes. Sensors. 2021;21(16). doi:10.3390/s21165649.

Lee J, Lee H, Shim DH. Vision-based state estimation and collision alert generation for detect-and-avoid. Proceedings of the 2020 AIAA/IEEE 39th Digital Avionics Systems Conference (DASC). 2020. p. 1-7. doi: 10.1109/DASC50938.2020.9256797.

Wan Y, Tang J, Lao S. Research on the collision avoidance algorithm for fixed-wing UAVs based on maneuver coordination and planned trajectories prediction. Applied Sciences. 2019;9(4). doi: 10.3390/app9040798.

Wang C, Xu C, Dai Y. A crash prediction method based on bivariate extreme value theory and video-based vehicle trajectory data. Accident Analysis and Prevention. 2019;123: 365-373. doi: 10.1016/j.aap.2018.12.013.

Wu Z, Li J, Zuo J, Li S. Path planning of UAVs based on collision probability and Kalman filter. IEEE Access. 2018;6: 34237-34245. doi: 10.1109/ACCESS.2018.2817648.

Zhang Z, et al. A uniform model for conflict prediction and airspace safety assessment for free flight. In: Liang Q, et al. (eds) Communications, Signal Processing, and Systems. CSPS 2019. Lecture Notes in Electrical Engineering, vol 571. Singapore: Springer; 2020. doi: 10.1007/978-981-13-9409-6_131.

Yong J, et al. Traffic conflict prediction model for bottleneck section of expressway construction area based on video recognition. Proceedings of the 2019 12th International Conference on Intelligent Computation Technology and Automation (ICICTA). 2019. p. 259-264. doi: 10.1109/ICICICTA49267.2019.00062.

Lazar T, et al. Modern Safety Technologies in Transportation (MOSATT): Proceedings of International Scientific Conference, 14-15 Nov. 2017, Herľany, Slovakia. Vol. 7. Košice: Faculty of Aeronautics of Technical University of Košice; 2017. p. 90-94.

Kraus J. Determining acceptable level of safety of approach to landing. Transport Means - Proceedings of the International Conference. 2016. p. 230-235.

Lališ A, Vittek P, Kraus J. Process modelling as the means of establishing semi-automated safety management. Transport Means - Proceedings of the International Conference. 2016. p. 254-258.

Novák Sedláčková A, Novák A, Pecho P. Implementation of smart technologies into the civil aviation aircraft maintenance process. Proceedings of the 2021 International Scientific Conference "The Science and Development of Transport" (ZIRP). 2021. p. 123-134.

Baek KY, Bang H. ADS-B based trajectory prediction and conflict detection for air traffic management. Int’l J. of Aeronautical & Space Sci. 2012;13(3): 377-385. doi: 10.5139/IJASS.2012.13.3.377.

Jover J, Bermúdez A, Casado R. A tactical conflict resolution proposal for U-Space Zu airspace volumes. Sensors. 2021;21(16): 5649. doi: 10.3390/s21165649.

Perez-Leon H, Acevedo JJ, Maza I, Ollero A. Integration of a 4D-trajectory follower to improve multi-UAV conflict management within the U-space context. Journal of Intelligent and Robotic Systems: Theory and Applications. 2021;102(3). doi: 10.1007/s10846-021-01415-0.

How to Cite
NOVÁK A, NOVÁK SEDLÁČKOVÁ A, PECHO P. Detection and Prediction of a Pair of Unmanned Aircraft Contact. Promet [Internet]. 2022Jun.1 [cited 2022Jul.3];34(3):467-74. Available from: