Rule-Based Conflict Management for Unmanned Traffic Management Scenarios

Alharbi, Abdulrahman; Poujade, Arturo; Malandrakis, Konstantinos; Petrunin, Ivan; Panagiotakopoulos, Dimitrios; Tsourdos, Antonios

doi:10.1109/dasc50938.2020.9256690

Cited by 11 publications

(6 citation statements)

References 11 publications

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“…Literature [25] discusses some de-confliction strategies to resolve any UAV conflict that appears in the scenarios. In this simulation setting, de-confliction strategies such as ground delay and slow speed were used.…”

Section: Methodlogy a Historical Data Generation And Pre-processingmentioning

confidence: 99%

Traffic Flow Prediction for UTM Application: A Deep Learning Approach

Alharbi

Petrunin

Panagiotakopoulos

2022

2022 IEEE/AIAA 41st Digital Avionics Systems Conference (DASC)

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Over the past few years, the research community has focused greatly on predicting air traffic flows, yielding remarkable outcomes. We found that existing literature in the field mainly covers prediction of air traffic flows for conventional aircraft. However, there is limited research about prediction of air traffic flows for Uncrewed Aircraft Traffic Management (UTM). This research study proposes a deep learning-based approach to predict air traffic congestion in the context of UTM over a period of three minutes. The use of the model aims to address congestion considering air traffic uncertainties instead of addressing the conventional issues of trajectory prediction or conflict detection and resolution. Our model also considers the influence of recreational users who fly UAVs at random times, during the execution of the above essential missions. Further, the effects of airspace structure configurations like static No-Fly Zones (NFZ), airfields with variable availability for drone flights, recreational areas, emergency UTM operation and environmental factors such as weather conditions have also been studied. The proposed model shows better performance compared to other approaches such as the Shallow neural networks and regression models.

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Section: Methodlogy a Historical Data Generation And Pre-processingmentioning

confidence: 99%

Traffic Flow Prediction for UTM Application: A Deep Learning Approach

Alharbi

Petrunin

Panagiotakopoulos

2022

2022 IEEE/AIAA 41st Digital Avionics Systems Conference (DASC)

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“…To resolve potential UAV conflicts, our recent work [75] outlined some possible deconfliction strategies, which consider the UAV priority mechanism we previously mentioned.…”

Section: Deconfliction Strategymentioning

confidence: 99%

Modeling and Characterization of Traffic Flow Patterns and Identification of Airspace Density for UTM Application

2022

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Current airspace has limited resources, and the widespread use of unmanned aerial vehicles (UAVs) increases airspace density, which is already crowded with manned aircraft. This demands the improvement of airspace safety and capacity while considering all parametric uncertainties that may hinder aircraft and UAV mobility such as dynamic airspace structures and weather conditions. This paper proposes a data analytics framework to characterize traffic flow patterns of unmanned traffic management (UTM) airspace by analyzing simulated historical data. Mission patterns are characterized and identified by considering multiple UAV missions and scenarios with different priority levels to highlight UAVs' trajectories and deviations from the actual path due to these constraints. The pertinent data analysis supports risk analysis and improves trajectory planning in different airspace regions considering all dynamic parameters such as extreme weather, emergency services, and dynamic airspace structures. The data processing framework, which is density-based spatial clustering of applications with noise (DBSCAN), identified significant deviations in mission patterns with almost 82% confidence level. The UTM traffic flow characterization is conducted by three key characterization parameters mainly Distance from Centroid (DFC), Distance to Complete Mission (DTCM) and Time to Complete Mission (TTCM). This work also analyzed the airspace congestion using the Kernel density estimation (KDE). This analysis identified some regions of interference as potential congested areas representing safety concerns. The proposed framework is envisioned to assist UTM authority by characterizing air traffic behavior, managing its flow, improving airspace design, and providing the basis for developing predictive capabilities that support traffic flow management.

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“…This paper reviews and studies the works of foreign authors who study the possibility of introducing unmanned aerial vehicles in various fields of activity. A significant number of works are devoted to studies in the field of UAV infrastructure development, environmental and economic impact [1][2][3][4][5][6][7][8][9][10]. In the works of Ravil I. Mukhamediev, Adilkhan Symagulov, Yan Kuchin detail the variations of UAV application, listing and describing many options for their use, as well as describing the momentum from the introduction of drones in different areas [2].…”

Section: Literature Reviewmentioning

confidence: 99%

Problems and prospects for the development of unmanned aerial vehicles in Russia

Smirnov

Smolokurov²,

Timofeeva³

et al. 2022

E3S Web Conf.

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Unmanned aerial vehicles are vehicles that can fly without a human pilot on board. Recently, unmanned aerial vehicles (UAV) have become widespread in the civil and military sectors, and the development of UAV is one of the promising trends in modern aviation. Drones have long ceased to be expensive and highly specialised and are in widespread use. They can be equipped with multiple cameras, gyrostabilisers, LIDAR and GPS to collect and transmit data in real time. Due to the demand for drones in various applications such as agriculture, search and rescue, wireless communications, surveillance and cargo transportation, several types of UAV have been invented with different sizes, weights, flight ranges, engine types, etc., optimized for specialized types of work. The deeper integration of drones into the transport system has the potential to relieve general strain in many companies and factories, reducing production and transport times, and facilitating faster response to emergencies. However, drones face a number of challenges related to flight logistics, collision avoidance, lack of necessary infrastructure, autonomous operation time and imperfect artificial intelligence.

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Rule-Based Conflict Management for Unmanned Traffic Management Scenarios

Cited by 11 publications

References 11 publications

Traffic Flow Prediction for UTM Application: A Deep Learning Approach

Traffic Flow Prediction for UTM Application: A Deep Learning Approach

Modeling and Characterization of Traffic Flow Patterns and Identification of Airspace Density for UTM Application

Problems and prospects for the development of unmanned aerial vehicles in Russia

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