Toward Mid-Air Collision-Free Trajectory for Autonomous and Pilot-Controlled Unmanned Aerial Vehicles by Kaya Kuru, John Michael Pinder, Benjamin Jon Watkinson, Darren Ansell, Keith Vinning, Lee Moore, Chris Gilbert, Aadithya Sujit, David Jones

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Application of Bayesian Networks and Information Theory to Estimate the Occurrence of Mid-Air Collisions Based on Accident Precursors by Rosa María Arnaldo Valdés, Schon Z.Y. Liang Cheng, Victor Fernando Gómez Comendador, Francisco Javier Sáez Nieto

“…This paper combines Bayesian networks (BN) and information theory to model the likelihood of severe loss of separation (LOS) near accidents, which are considered mid-air collision (MAC) precursors. BN is used to analyze LOS contributing factors and the multi-dependent relationship of causal factors, while Information Theory is used to identify the LOS precursors that provide the most information. …”
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Preliminary Feasibility Study of the Ad Hoc Separation Operational Concept by Lidia Serrano-Mira, Luis Pérez Sanz, Javier A. Pérez-Castán, Fedja Netjasov, Irene García Moreno, Eduardo S. Ayra

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Vision-Based Mid-Air Object Detection and Avoidance Approach for Small Unmanned Aerial Vehicles with Deep Learning and Risk Assessment by Ying-Chih Lai, Tzu-Yun Lin

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What Matters in the Effectiveness of Airborne Collision Avoidance Systems? Monte Carlo Simulation of Uncertainties for TCAS II and ACAS Xa by Sybert Stroeve

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Parametric Study of Structured UTM Separation Recommendations with Physics-Based Monte Carlo Distribution for Collision Risk Model by Chung-Hung John Wang, Chao Deng, Kin Huat Low

“…One layer of defense against mid-air-collision and the ensuing third-party injury or fatality is the pre-flight separation assurance. …”
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Quantifying Specific Operation Airborne Collision Risk through Monte Carlo Simulation by Aliaksei Pilko, Mario Ferraro, James Scanlan

“…Integration of Uncrewed Aircraft into unsegregated airspace requires robust and objective risk assessment in order to prevent exposure of existing airspace users to additional risk. A probabilistic Mid-Air Collision risk model is developed based on surveillance traffic data for the intended operational area. …”
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Vision-Based In-Flight Collision Avoidance Control Based on Background Subtraction Using Embedded System by Jeonghwan Park, Andrew Jaeyong Choi

“…One of the key features of an autonomous UAV is a robust mid-air collision avoidance strategy. This paper proposes a vision-based in-flight collision avoidance system based on background subtraction using an embedded computing system for unmanned aerial vehicles (UAVs). …”
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Multimodal Augmented Reality Applications for Training of Traffic Procedures in Aviation by Birgit Moesl, Harald Schaffernak, Wolfgang Vorraber, Reinhard Braunstingl, Ioana Victoria Koglbauer

“…Mid-air collision is one of the top safety risks in general aviation. …”
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A Quantitative Approach to Air Traffic Safety at Very Low Levels by Xavier Olive, Patrick Le Blaye

“…A safe integration of drone operations at very low levels, especially for beyond visual line-of-sight operations, must come with proper modeling of the mid-air collision risk at lower altitudes. In this paper, we present a state-of-the-art quantitative model for the air risk assessment of unmanned aircraft system (UAS) operations and illustrate how cooperative technologies such ADS-B and FLARM, together with networks of compatible ground receivers, are crucial to provide the traffic data required to support this model. …”
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Detection of a Moving UAV Based on Deep Learning-Based Distance Estimation by Ying-Chih Lai, Zong-Ying Huang

“…Distance information of an obstacle is important for obstacle avoidance in many applications, and could be used to determine the potential risk of object collision. In this study, the detection of a moving fixed-wing unmanned aerial vehicle (UAV) with deep learning-based distance estimation to conduct a feasibility study of sense and avoid (SAA) and mid-air collision avoidance of UAVs is proposed by using a monocular camera to detect and track an incoming UAV. …”
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To the risk management methodology of unmanned aircraft systems by V. D. Sharov, V. L. Kuznetsov, P. M. Polyakov

“…Although the UAS operation of category B is supposed to be performed in the segregated airspace, the methodology takes into consideration not only the risks of collision with objects on the ground but also mid-air collision risks with manned aircraft, since it is usually impractical to create the “ideal” segregated airspace. …”
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Identification and Quantification of Contributing Factors to the Criticality of Aircraft Loss of Separation by Lidia Serrano-Mira, Marta Pérez Maroto, Eduardo S. Ayra, Javier Alberto Pérez-Castán, Schon Z. Y. Liang-Cheng, Víctor Gordo Arias, Luis Pérez-Sanz

“…A Mid-Air Collision (MAC) is a fatal event with tragic consequences. …”
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Detection and Prediction of a Pair of Unmanned Aircraft Contact by Andrej NOVÁK, Alena NOVÁK SEDLÁČKOVÁ, Pavol PECHO

“…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. …”
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Deep generative modelling of aircraft trajectories in terminal maneuvering areas by Timothé Krauth, Adrien Lafage, Jérôme Morio, Xavier Olive, Manuel Waltert

“…Airspace design is subject to a multitude of constraints, which are mainly driven by the concern to keep the risk of mid-air collision below a target level of safety. …”
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Risk Assessment Method for UAV’s Sense and Avoid System Based on Multi-Parameter Quantification and Monte Carlo Simulation by Bona P. Fitrikananda, Yazdi Ibrahim Jenie, Rianto Adhy Sasongko, Hari Muhammad

“…The potential for accidents underscores the urgent need for effective measures to mitigate mid-air collision risks. This research aims to assess the effectiveness of the Sense and Avoid (SAA) system during operation by providing a rating system to quantify its parameters and operational risk, ultimately enabling authorities, developers, and operators to make informed decisions to reach a certain level of safety. …”
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UAV sensor failures dataset: Biomisa arducopter sensory critique (BASiC) by Muhammad Waqas Ahmad, Muhammad Usman Akram

“…Untimely control of sensor failures can result in mid-air collisions or crashes. To address these challenges, we created Biomisa Arducopter Sensory Critique (BASiC) dataset, a state-of-the-art resource for UAV sensor failure analysis. …”
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