A survey of safety separation management and collision avoidance approaches of civil UAS operating in integration national airspace system

Guan, Xiangmin; Lyu, Renli; Shi, Hongxia; Chen, Jun

doi:10.1016/j.cja.2020.05.009

Cited by 40 publications

(19 citation statements)

References 45 publications

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“…Capitán et al [12] reported the development of a software architecture for UTM that can provide real-time monitoring of airspace to enable tactical de-confliction and management of emergencies. Guan et al [20] investigated collision avoidance and the management of separation within UTM including features such as prediction and assessment of risk, safe separation standards, and systems for detection and avoidance.…”

Section: Drone Industry Attitudes To Shared Airspacementioning

confidence: 99%

Pathways to Unsegregated Sharing of Airspace: Views of the Uncrewed Aerial Vehicle (UAV) Industry

Grote

Pilko

Scanlan

et al. 2021

Drones

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The uncrewed aerial vehicle (UAV or drone) industry is expanding, offering services such as video/photography, inspection, monitoring, surveying, and logistics. This is leading to competing demands for airspace with existing crewed aircraft activities, especially in uncontrolled airspace. As a result, there is an increasingly urgent need for a shared airspace solution that enables drones to be integrated with the wider aviation community in unsegregated operations. The purpose of this research was to engage with the drone industry to understand their issues regarding shared airspace as an important first step in the co-development of operating procedures that can provide equitable airspace access for all. An online, interactive workshop format was employed, with participants (n ~ 80) drawn from the UK drone industry and other attendant organisations. Verbal and written data were recorded, and then analysed using thematic analysis. The findings summarise the issues on a range of topics, grouped into three over-arching themes: (1) operational environment; (2) technical and regulatory environment; and (3) equity and wider society. Results suggested that important issues included the necessity for a dependable detect-and-avoid (DAA) system for in-flight de-confliction, based on onboard electronic conspicuity (EC) devices, and the need for support for shared airspace from the wider aviation community. This study contributes to the stakeholder engagement that will be essential if the co-development of a shared airspace solution is to be widely acceptable to all.

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Section: Drone Industry Attitudes To Shared Airspacementioning

confidence: 99%

Pathways to Unsegregated Sharing of Airspace: Views of the Uncrewed Aerial Vehicle (UAV) Industry

Grote

Pilko

Scanlan

et al. 2021

Drones

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“…The usage of commercial unmanned aerial vehicles (UAVs) has been increasing recently [1]. The identification and detection of UAVs is important for the security measures of airports, military zones, personal private zones, and human life and privacy [2]. UAVs are classified as either fixed-winged unmanned aerial vehicles (FW-UAV) or rotating-wing unmanned aerial vehicles (RW-UAV).…”

Section: Introductionmentioning

confidence: 99%

Real UAV-Bird Image Classification Using CNN with a Synthetic Dataset

Öztürk

Erçelebi

2021

Applied Sciences

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A large amount of training image data is required for solving image classification problems using deep learning (DL) networks. In this study, we aimed to train DL networks with synthetic images generated by using a game engine and determine the effects of the networks on performance when solving real-image classification problems. The study presents the results of using corner detection and nearest three-point selection (CDNTS) layers to classify bird and rotary-wing unmanned aerial vehicle (RW-UAV) images, provides a comprehensive comparison of two different experimental setups, and emphasizes the significant improvements in the performance in deep learning-based networks due to the inclusion of a CDNTS layer. Experiment 1 corresponds to training the commonly used deep learning-based networks with synthetic data and an image classification test on real data. Experiment 2 corresponds to training the CDNTS layer and commonly used deep learning-based networks with synthetic data and an image classification test on real data. In experiment 1, the best area under the curve (AUC) value for the image classification test accuracy was measured as 72%. In experiment 2, using the CDNTS layer, the AUC value for the image classification test accuracy was measured as 88.9%. A total of 432 different combinations of trainings were investigated in the experimental setups. The experiments were trained with various DL networks using four different optimizers by considering all combinations of batch size, learning rate, and dropout hyperparameters. The test accuracy AUC values for networks in experiment 1 ranged from 55% to 74%, whereas the test accuracy AUC values in experiment 2 networks with a CDNTS layer ranged from 76% to 89.9%. It was observed that the CDNTS layer has considerable effects on the image classification accuracy performance of deep learning-based networks. AUC, F-score, and test accuracy measures were used to validate the success of the networks.

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“…In addition, the airspace is already populated with another aircraft, which have very specific and demanding requirements related to their design, production, and operation. Research has been performed regarding the integration of UAS in a non-segregated airspace, considering separation procedures and mid-air collision (MAC) risk (Clothier et al 2015;Guan et al 2020). Spencer's (2018) review highlighted that a catastrophic accident caused by UAS would pose as a great threat to the drone industry, according to operator's point of view.…”

Section: Introductionmentioning

confidence: 99%

Operational Safety and Operator Management to Enable the Use of Unmanned Aircraft Systems (Uas) as Geomatics Tools

Vera

Quintanilla

Vidal

et al. 2021

Proceedings - 3rd Congress in Geomatics Engineering - CIGeo

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Potential civil applications of Unmanned Aircraft Systems (UAS), commonly known as drones, have risen steeply duringthe last decade, mainly due to their versatility and capability of spatial data gathering. Nonetheless, real use of UAS isquite restricted nowadays, primarily due to safety and regulatory constraints. This multidisciplinary project aims to performspecific safety assessments using the SORA methodology adopted by the European Aviation Safety Agency (EASA) anddevelop documentation and procedures for operators to follow, complying with all required safety and regulatoryrequirements. As a result, DEURPAS-UPV is the first Spanish drone operator belonging to a university to be authorizedby Spanish civil aviation agency (AESA-Agencia Estatal de Seguridad Aérea), to perform drone flights in urban areas, incontrolled airspace and during the night. In addition, DEURPAS-UPV has performed the first authorized experimentaltransport operations using drones in Spain. The results from safety assessment and designed procedures have beensuccessfully applied to the operation of Safety and Emergency service providers, such as Valencia Local Police Corps andthe Valencian Emergency and Safety Response Agency (AVSRE - Agencia Valenciana de Seguridad y Respuesta a lasEmergencias). Overall, this project has served as an enabler for more complex and safer UAS operations, from theoperator’s point of view, which will help break the barriers related to the use of these aircraft, with huge potential ingeomatics applications.

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A survey of safety separation management and collision avoidance approaches of civil UAS operating in integration national airspace system

Cited by 40 publications

References 45 publications

Pathways to Unsegregated Sharing of Airspace: Views of the Uncrewed Aerial Vehicle (UAV) Industry

Pathways to Unsegregated Sharing of Airspace: Views of the Uncrewed Aerial Vehicle (UAV) Industry

Real UAV-Bird Image Classification Using CNN with a Synthetic Dataset

Operational Safety and Operator Management to Enable the Use of Unmanned Aircraft Systems (Uas) as Geomatics Tools

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