Search Results (16)

Aiming at the problems of the imprecise safety distance standards for low-altitude UAVs within complex low-altitude environments and the difficulty of managing heterogeneous vehicles, a UAV safety interval calibration method based on random heading is proposed. Firstly, a UAV clustering model based on K-Means++ was established for the performance characteristics of UAVs, using evaluation indexes such as contour coefficient, sum of squares of errors, Davidson–Bourdain index, etc. Then, combining this with the characteristics of UAVs with random heading, a UAV safety interval calibration model based on random heading was constructed, and the conflict probability and airspace utilization rate were determined and metered for UAV safety interval calibration. The experimental results showed that the profile coefficient, sum of squares of errors, and Davidson–Berding index of the iteratively optimized UAV clustering were optimized by 53.9%, 55.6%, and 46.6%, respectively, compared with the initial clustering results, and that the safe intervals calibrated in the experimental environment of a single category of UAVs were also applicable to the fusion airspace environment after validation. The research results can provide a theoretical basis and methodological support for the safety interval calibration of UAVs in low-altitude fusion operations. Full article

As urban air mobility moves rapidly toward real-world deployment, accurate vehicle trajectory prediction and early collision risk detection are vital for safe low-altitude operations. This study presents a deep learning framework based on an LSTM–Attention network that captures both short-term flight dynamics and long-range dependencies in trajectory data. The model is trained on fifty-six routes generated from a UAM planned commercialization network, sampled at 0.1 s intervals. To unify spatial dimensions, the model uses Earth-Centered Earth-Fixed (ECEF) coordinates, enabling efficient Euclidean distance calculations. The trajectory prediction component achieves an RMSE of 0.2172, MAE of 0.1668, and MSE of 0.0524. The collision classification module built on the LSTM–Attention prediction backbone delivers an accuracy of 0.9881. Analysis of attention weight distributions reveals which temporal segments most influence model outputs, enhancing interpretability and guiding future refinements. Moreover, this model is embedded within the Short-Term Conflict Alert component of the Safety Nets module in the UAM traffic management system to provide continuous trajectory prediction and collision risk assessment, supporting proactive traffic control. The system exhibits robust generalizability on unseen scenarios and offers a scalable foundation for enhancing operational safety. Validation currently excludes environmental disturbances such as wind, physical obstacles, and real-world flight logs. Future work will incorporate atmospheric variability, sensor and communication uncertainties, and obstacle detection inputs to advance toward a fully integrated traffic management solution with comprehensive situational awareness. Full article

With the growing integration of the Internet of Things (IoT), low-altitude intelligent networks, and vehicular networks, smart city traffic systems are gradually evolving into an air–ground integrated intelligent monitoring framework. However, traditional centralized model training faces challenges such as high network load due to massive data transmission, energy management difficulties for mobile devices like UAVs, and privacy risks associated with non-anonymized road operation data. Therefore, this paper proposes an air–ground collaborative federated learning framework that integrates Bayesian prediction and an incentive mechanism to achieve privacy protection and communication optimization through localized model training and differentiated incentive strategies. Simulation experiments demonstrate that, compared to the Equal Contribution Algorithm (ECA) and the Importance Contribution Algorithm (ICA), the proposed method improves model convergence speed while reducing incentive costs, providing theoretical support for the reliable operation of large-scale intelligent traffic monitoring systems. Full article

Unmanned Aerial Vehicles (UAVs) are increasingly gaining popularity, and their consistent prevalence in various applications such as surveillance, search and rescue, and environmental monitoring requires the development of specialized policies for UAV traffic management. Integrating this novel aerial traffic into existing airspace frameworks presents unique challenges, particularly regarding safety and security. Consequently, there is an urgent need for robust contingency management systems, such as Anti-UAV technologies, to ensure safe air traffic. This survey paper critically examines the recent advancements in ground-to-air vision-based Anti-UAV detection and tracking methodologies, addressing the many challenges inherent in UAV detection and tracking. Our study examines recent UAV detection and tracking algorithms, outlining their operational principles, advantages, and disadvantages. Publicly available datasets specifically designed for Anti-UAV research are also thoroughly reviewed, providing insights into their characteristics and suitability. Furthermore, this survey explores the various Anti-UAV systems being developed and deployed globally, evaluating their effectiveness in facilitating the integration of small UAVs into low-altitude airspace. The study aims to provide researchers with a well-rounded understanding of the field by synthesizing current research trends, identifying key technological gaps, and highlighting promising directions for future research and development in Anti-UAV technologies. Full article

This paper provides a study of free-flight air traffic behaviour in increasingly constrained airspace environments. Traffic assumes three different free-flight operational constructs with airspace constraints considered as restricted (no-fly) regions. Simulations combine path planning and Monte Carlo techniques to qualitatively analyse emergent traffic behaviour and quantitatively assess spatial–temporal airspace conflict as the airspace constraints vary. Findings indicate that airspace constraints have a much stronger influence on aircraft behaviour than the free-flight operational construct, with any benefits of free flight rapidly diminishing as the airspace becomes more constrained. We conclude that structured traffic route (or network) designs and associated risk modelling approaches should be considered for safe and efficient traffic management of highly constrained and congested (or dense) airspace. This work therefore provides evidence to inform new airspace design and management initiatives, including low-altitude uncrewed traffic. Full article

Drones have experienced rapid technological advancements, leading to the proliferation of small, low-cost, remotely controlled, and autonomous aerial vehicles with diverse applications, from package delivery to personal transportation. However, integrating these drones into the existing air traffic management (ATM) system poses significant challenges. The current ATM infrastructure, designed primarily for traditionally manned aircraft, requires enhanced capacity, workforce, and cost-effectiveness to coordinate the large number of drones expected to operate at low altitudes in complex urban environments. Therefore, this study aims to develop an intelligent, highly automated drone management system for integration into smart city transportation networks. The key objectives include the following: (i) developing a conceptual framework for an intelligent total transportation management system tailored for future smart cities, focusing on incorporating drone operations; (ii) designing an advanced air traffic management and flight control system capable of managing individual drones and drone swarms in complex urban environments; (iii) improving drone management methods by leveraging drone-following models and emerging technologies such as the Internet of Things (IoT) and the Internet of Drones (IoD); and (iv) investigating the landing processes and protocols for unmanned aerial vehicles (UAVs) to enable safe and efficient operations. Full article

As urban population concentration accelerates, issues such as traffic congestion caused by automobiles and climate change due to carbon dioxide emissions are becoming increasingly severe. Recently, urban air mobility (UAM) has been attracting attention as a solution to these problems. UAM refers to a system that uses electric vertical takeoff and landing (eVTOL) aircraft to transport passengers and cargo at low altitudes between key points within urban areas, with lithium-ion batteries as the primary power source. The lithium-ion batteries used in UAM have characteristics that degrade performance in low temperatures, including decreased power output and diminished energy capacity. Although research has been conducted on preheating lithium-ion batteries to address this issue, sufficient consideration has not been given to the energy used for preheating. Therefore, this study compares the energy recovered by preheating lithium-ion batteries with the energy consumed during preheating and proposes a temperature management method for low temperatures that maximizes the energy gain of lithium-ion batteries. Full article

As the usability of and demand for unmanned aerial vehicles (UAVs) have increased, it has become necessary to establish a UAS traffic management (UTM) system for efficient UAV operations at low altitudes. To avoid collisions with ground obstacles, other UAVs, and manned aircraft, in building a safe path, the UTM needs to determine the time and space allocated to each flight. Ideas for discretizing and structuring airspace in various forms have been proposed to enhance the efficiency of system operation and improve traffic congestion through effectual airspace allocation. Additionally, various methods of allocating UAVs to structured unit spaces have been studied in the literature. In this paper, the methods and structural designs for allocating airspace that have appeared in related studies are classified into several types, and their strengths and weaknesses are analyzed. The structured airspace designs are categorized into three models: Air-Matrix, Air-Network, and Air-Tube, and analyzed according to their sub-structures and temporal allocation methods. In addition, a quantitative analysis is conducted by re-categorizing the structured airspace and operation methods and building their combinations. Full article

Unmanned Aircraft System Traffic Management (UTM) is a traffic management system enabling drones to safely and efficiently fly in low-altitude airspace below 120~150m (400~500ft). UTM provides services such as communication, flight route management, location monitoring, and collision avoidance so that drones completing various missions can fly beyond visual line of sight (BVLOS) safely and increase the usability of airspace. In other words, UTM is a new air traffic management for drones with high levels of automation, advanced decision making and control. Many countries around the world are developing UTM systems that systematically manage the traffic of drones flying at low altitude. In Korea, UTM research has been ongoing as an R&D project since 2017. The purpose of this paper is to introduce the Korean UTM system and to apply it to actual flight demonstration through the developed operational procedures. The approach of this article is to establish Korean UTM architecture through existing references and examples from other countries, devise an operational procedure suitable for the system, and describe the results of using it for flight demonstration. In other words, this paper covers Korea’s UTM architecture, operational procedures, and flight demonstration through a macro approach to UTM. In addition, this paper presents policy and technical challenges that UTM must go through and that need to be solved in the future, which are classified into four categories. Full article

EuroDRONE is an Unmanned Traffic Management (UTM) demonstration project, funded by the EU’s SESAR organization, and its aim is to test and validate key UTM technologies for Europe’s ‘U-Space’ UTM program. The EuroDRONE UTM architecture comprises cloud software (DroNav) and hardware (transponder) to be installed on drones. The proposed EuroDRONE system is a Highly Automated Air Traffic Management System for small UAVs operating at low altitudes. It is a sophisticated, self-learning system based on software and hardware elements, operating in a distributed computing environment, offering multiple levels of redundancy, fail-safe algorithms for conflict prevention/resolution and assets management. EuroDRONE focuses its work on functionalities which involve the use of new communication links, the use of vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) technology to communicate information between drones and operators for safe and effective UTM functionality. Practical demonstrations that took place in Patras/Messolonghi in 2019 are presented and show the benefits and shortcomings of near-term UTM implementation in Europe. Full article

Airspace geofencing is a key capability for low-altitude Unmanned Aircraft System (UAS) Traffic Management (UTM). Geofenced airspace volumes can be allocated to safely contain compatible UAS flight operations within a fly-zone (keep-in geofence) and ensure the avoidance of no-fly zones (keep-out geofences). This paper presents the application of three-dimensional flight volumization algorithms to support airspace geofence management for UTM. Layered polygon geofence volumes enclose user-input waypoint-based 3-D flight trajectories, and a family of flight trajectory solutions designed to avoid keep-out geofence volumes is proposed using computational geometry. Geofencing and path planning solutions are analyzed in an accurately mapped urban environment. Urban map data processing algorithms are presented. Monte Carlo simulations statistically validate our algorithms, and runtime statistics are tabulated. Benchmark evaluation results in a Manhattan, New York City low-altitude environment compare our geofenced dynamic path planning solutions against a fixed airway corridor design. A case study with UAS route deconfliction is presented, illustrating how the proposed geofencing pipeline supports multi-vehicle deconfliction. This paper contributes to the nascent theory and the practice of dynamic airspace geofencing in support of UTM. Full article

In this work, we introduce a microscopic traffic flow model called Scalar Capacity Model (SCM) which can be used to study the formation of traffic on an airway link for autonomous Unmanned Aerial Vehicles (UAVs) as well as for the ground vehicles on the road. Given the 3D trajectory of UAV flights (as opposed to the 2D trajectory of ground vehicles), the main novelty in our model is to eliminate the commonly used notion of lanes and replace it with a notion of density and capacity of flow, but in such a way that individual vehicle motions can still be modeled. We name this a Density/Capacity View (DCV) of the link capacity and how vehicles utilize it versus the traditional One/Multi-Lane View (OMV). An interesting feature of this model is exhibiting both passing and blocking regimes (analogous to multi-lane or single-lane) depending on the set scalar parameter for capacity. We show the model has linear local (platoon) and asymptotic linear stability. Additionally, we perform numerical simulations and show evidence for non-linear stability. Our traffic flow model is represented by a nonlinear differential equation which we transform into a linear form. This makes our model analytically solvable in the blocking regime and piece-wise analytically solvable in the passing regime. Finally, a key advantage of using our model over an OMV model for representing UAV’s flights is the removal of the artificial restriction on passing via only adjacent lanes. This will result in an improved and more realistic traffic flow for UAVs. Full article

In order to study air traffic control of UAS’s (Unmanned Aerial Systems) in very low altitudes, the UTM (UAS Traffic Management) simulator has to be as flexible and expandable as other research simulators because relevant technologies and regulations are not matured enough at this stage. Available approaches using open sources and platforms are investigated to be used in the UTM simulator. The fundamental rationale for selection is availability of necessary resources to build a UTM simulator. Integration efforts to build a UTM simulator are elaborated, using Ardupilot, MavProxi, Cesium, and VWorld, which are selected from the thorough field study. Design requirements of a UTM simulator are determined by analyzing UTM services defined by NASA (National Aeronautics and Space Administration) and Eurocontrol. The UTM simulator, named eUTM, is composed of three components: UOS (UTM Operating System), UTM, and multiple GCSs (Ground Control Stations). GCSs are responsible for generation of flight paths of various UASs. UTM component copies functions of a real UTM such as monitoring and controlling air spaces. UOS provides simulation of environment such as weather, and controls the whole UTM simulator system. UOS also generates operation scenarios of UTM, and resides on the same UTM computer as an independent process. Two GCS simulators are connected to the UTM simulator in the present configuration, but the UTM simulator can be expanded to include up to 10 GCS simulators in the present design. In order to demonstrate the flexibility and expandability of eUTM simulator, several operation scenarios are realized and typical deconfliction scenarios among them are tested with a deconfliction algorithm. During the study, some limits are identified with applied open sources and platforms, which have to be resolved in order to obtain a flexible and expandable UTM simulator supporting relevant studies. Most of them are related to interfacing individual sources and platforms which use different program languages and communication drivers. Full article

The current airspace route system consists mainly of pre-defined routes with a low number of intersections to facilitate air traffic controllers to oversee the traffic. Our aim is a method to create an artificial and reliable route network based on planned or as-flown trajectories. The application possibilities of the resulting network are manifold, reaching from the assessment of new air traffic management (ATM) strategies or historical data to a basis for simulation systems. Trajectories are defined as sequences of common points at intersections with other trajectories. All common points of a traffic sample are clustered, and, after further optimization, the cluster centers are used as nodes in the new main-flow network. To build almost-realistic flight trajectories based on this network, additional parameters such as speed and altitude are added to the nodes and the possibility to take detours into account to avoid congested areas is introduced. As optimization criteria, the trajectory length and the structural complexity of the main-flow system are used. Based on these criteria, we develop a new cost function for the optimization process. In addition, we show how different traffic situations are covered by the network. To illustrate the capabilities of our approach, traffic is exemplarily divided into separate classes and class-dependent parameters are assigned. Applied to two real traffic scenarios, the approach was able to emulate the underlying route systems with a difference in median trajectory length of 0.2%, resp. 0.5% compared to the original routes. Full article

Recent evolutions of the Unmanned Aircraft Systems (UAS) Traffic Management (UTM) concept are driving the introduction of new airspace structures and classifications, which must be suitable for low-altitude airspace and provide the required level of safety and flexibility, particularly in dense urban and suburban areas. Therefore, airspace classifications and structures need to evolve based on appropriate performance metrics, while new models and tools are needed to address UTM operational requirements, with an increasing focus on the coexistence of manned and unmanned Urban Air Mobility (UAM) vehicles and associated Communication, Navigation and Surveillance (CNS) infrastructure. This paper presents a novel airspace model for UTM adopting Performance-Based Operation (PBO) criteria, and specifically addressing urban airspace requirements. In particular, a novel airspace discretisation methodology is introduced, which allows dynamic management of airspace resources based on navigation and surveillance performance. Additionally, an airspace sectorisation methodology is developed balancing the trade-off between communication overhead and computational complexity of trajectory planning and re-planning. Two simulation case studies are conducted: over the skyline and below the skyline in Melbourne central business district, utilising Global Navigation Satellite Systems (GNSS) and Automatic Dependent Surveillance-Broadcast (ADS-B). The results confirm that the proposed airspace sectorisation methodology promotes operational safety and efficiency and enhances the UTM operators’ situational awareness under dense traffic conditions introducing a new effective 3D airspace visualisation scheme, which is suitable both for mission planning and pre-tactical UTM operations. Additionally, the proposed performance-based methodology can accommodate the diversity of infrastructure and vehicle performance requirements currently envisaged in the UTM context. This facilitates the adoption of this methodology for low-level airspace integration of UAS (which may differ significantly in terms of their avionics CNS capabilities) and set foundations for future work on tactical online UTM operations. Full article