UAS Traffic Management Research Articles

Ontological Airspace-Situation Awareness for Decision System Support

Air Traffic Management (ATM) has become complicated mainly due to the increase and variety of input information from Communication, Navigation, and Surveillance (CNS) systems as well as the proliferation of Unmanned Aerial Vehicles (UAVs) requiring Unmanned Aerial System Traffic Management (UTM). In response to the UTM challenge, a decision support system (DSS) has been developed to help ATM personnel and aircraft pilots cope with their heavy workloads and challenging airspace situations. The DSS provides airspace situational awareness (ASA) driven by knowledge representation and reasoning from an Avionics Analytics Ontology (AAO), which is an Artificial Intelligence (AI) database that augments humans’ mental processes by means of implementing AI cognition. Ontologies for avionics have also been of interest to the Federal Aviation Administration (FAA) Next Generation Air Transportation System (NextGen) and the Single European Sky ATM Research (SESAR) project, but they have yet to be received by practitioners and industry. This paper presents a decision-making computer tool to support ATM personnel and aviators in deciding on airspace situations. It details the AAO and the analytical AI foundations that support such an ontology. An application example and experimental test results from a UAV AAO (U-AAO) framework prototype are also presented. The AAO-based DSS can provide ASA from outdoor park-testing trials based on downscaled application scenarios that replicate takeoffs where drones play the role of different aircraft, i.e., where a drone represents an airplane that takes off and other drones represent AUVs flying around during the airplane’s takeoff. The resulting ASA is the output of an AI cognitive process, the inputs of which are the aircraft localization based on Automatic Dependent Surveillance–Broadcast (ADS-B) and the classification of airplanes and UAVs (both represented by drones), the proximity between aircraft, and the knowledge of potential hazards from airspace situations involving the aircraft. The ASA outcomes are shown to augment the human ability to make decisions.

Quantifying Well Clear Thresholds for UAV in Conjunction with Trajectory Conformity

The rapid advancement of unmanned aerial vehicles (UAVs) has introduced new challenges in overseeing and managing their flight operations due to their diverse flight dynamics and performance metrics. To address these complexities, this study introduces a concept of trajectory conformity aimed at enhancing the supervision and control of UAV flights. Trajectory conformity, from a regulatory perspective, is defined as the distribution of deviations between a UAV’s actual flight path and its intended trajectory, offering a measure of system-wide operational error. The concept of conformity is hoped to simplify and strengthen the monitoring process to ensure conflict-free drone flying. The present work is also concerned with the development of a comprehensive UAV collision risk model grounded in trajectory conformity analysis. The normality and homogeneity of UAV trajectory deviations are validated by evaluating the trajectory data obtained from real-world UAV flights. Well clear thresholds between two UAVs operating in three orthogonal directions within the same airspace have been established by the developed model. The results obtained demonstrate the effectiveness in omni-encounter scenarios, underscoring the potential to strengthen safety measures. The present work is expected to enhance UAV safety systems, such as detect and avoid (DAA) and unmanned aircraft system traffic management (UTM), by enabling real-time collision warnings within predefined safety thresholds, supporting proactive risk mitigation. Furthermore, the model’s versatility allows it to be applied to various UAV operational aspects in future works, including route planning, flight procedure design, airspace capacity assessments, and establishment of separation minima.

A Fixed Air Corridor Model for UAS Traffic Management in Urban Areas

A Fixed Air Corridor Model for UAS Traffic Management in Urban Areas

UAV Detect and Avoid from UTM-Dependent Surveillance

A hierarchical unmanned aircraft system (UAS) traffic management (UTM) system has deployed 45 ground transceiver stations (GTS) for UAS services in Taiwan. This UTM system covers most areas for UAV-dependent surveillance using ADS-B Like technology. UTM Controller can monitor all UAV flights under transparent surveillance in low airspace. Controller-initiated UAV “detect and avoid” (DAA) mechanism assists UAV separation to ensure flight safety on UTM for small multirotor UAVs. From similar concept to traffic alert and collision avoidance system (TCAS) for the manned aircraft system, the UTM software executes DAA functions to generate approach alerts to UTM Controller. Conflict is detected by heading arrow extrapolation from multiple approaching UAVs by their time to conflict (TTC) on icons. Traffic advisory (TA) and resolution advisory (RA) are pronounced on UTM console to controllers. The less priority UAV pilot will receive the controller-pilot communication (CPC) to perform avoidance resolution. In UTM, the surveillance data period is broadcasting at 5~8 seconds on LoRa (long-range wide-area network) chip. Referring to TA=48 seconds and RA=24 seconds, the signal delay in ADS-B Like system to UTM server is about 0.5 seconds and CPC response is measured about 3~5 seconds. From real flight tests, the RA is enough for the less priority pilot to maneuver UAV for avoidance. From real flight tests, the proposed DAA mechanism based on UTM-dependent surveillance is feasible to resolve multiple approaches. The developing UTM system using ADS-B Like technology is also examined of high availability with redundant reliability and performance stability for flight safety.

Real-Time UAV Trajectory Prediction for UTM Surveillance Using Machine Learning

The Unmanned Aerial System (UAS) Traffic Management (UTM) surveillance plays an important role in monitoring the safety compliance of UAV flights within specific operational zones. However, the quality of data reception from UAVs depends on transmission signal quality, leading to variable data latency during flights. As a means of addressing this issue and improving UAV operational safety, trajectory prediction emerges as a promising solution. This study aims to implement real-time trajectory prediction through the integration of machine learning techniques within the UTM surveillance system using Broadcast Remote ID. To facilitate this, a homemade receiver for broadcast Remote ID is developed using the ESP32 microcontroller. Subsequently, two distinctive flight tests are executed using a DJI Phantom 4 UAV. In the first flight, UAV trajectory data are used to serve as training input for the machine learning algorithms. The second flight focuses on the implementation of real-time trajectory prediction. The trajectory prediction model uses inputs such as latitude, longitude, height, speed, direction, latency time, and the Received Signal Strength Indicator (RSSI). Through an analysis of the first flight’s offline data, the Gated Recurrent Unit (GRU) algorithm is preferred to the Long Short-Term Memory (LSTM) algorithm. The outcomes of the second flight test prove the feasibility of the GRU-based trajectory prediction. The GRU model successfully produces real-time predictions during UAV flight, showcasing accuracy levels similar to those derived from offline prediction analyses with short processing time. This real-time trajectory prediction could improve the safety of UAV operation by providing the estimated position when the latency time is higher than the threshold.

The exploration and practice of low-altitude airspace flight service and traffic management in China

Due to the inherent nature of being highly digitalized, networked and intelligent, Unmanned Aerial System (UAS) operations pose a huge challenge to traditional aviation regulation and technical systems. How to keep safe, efficient and integrated operation for different Airspace users has become a pressing issue faced by civil aviation around the world. This paper focuses on the main operational scenarios and characteristics of unmanned aviation development in China. New operational characteristics and associated challenges due to diverse low-altitude users are analyzed, including operation concepts, UAS traffic management, technological test and verification, and standards. Drawing light on the practices in Europe and the United States, this paper summarizes China's practices and progress in low-altitude operations management, and analyzes future technological development needs and trends, as well as feasible implementation pathways and measures based on actual needs.

A Finite-State Fixed-Corridor Model for UAS Traffic Management

A Finite-State Fixed-Corridor Model for UAS Traffic Management

Vehicle-to-Vehicle Based Autonomous Flight Coordination Control System for Safer Operation of Unmanned Aerial Vehicles

The exponential growth of unmanned aerial vehicles (UAVs) or drones in recent years has raised concerns about their safe operation, especially in beyond-line-of-sight (BLOS) scenarios. Existing unmanned aircraft system traffic management (UTM) heavily relies on commercial communication networks, which may become ineffective if network infrastructures are damaged or disabled. For this challenge, we propose a novel approach that leverages vehicle-to-vehicle (V2V) communications to enhance UAV safety and efficiency in UAV operations. In this study, we present a UAV information collection and sharing system named Drone Mapper®, enabled by V2V communications, so that UAVs can share their locations with each another as well as with the ground operation station. Additionally, we introduce an autonomous flight coordination control system (AFCCS) that augments UAV safety operations by providing two essential functionalities: UAV collision avoidance and UAV formation flight, both of which work based on V2V communications. To evaluate the performance of the developed AFCCS, we conducted comprehensive field experiments focusing on UAV collision avoidance and formation flight. The experimental results demonstrate the effectiveness of the proposed system and show seamless operations among multiple UAVs.

Incorporating Optimization in Strategic Conflict Resolution for UAS Traffic Management

Incorporating Optimization in Strategic Conflict Resolution for UAS Traffic Management

A Survey on the Unmanned Aircraft System Traffic Management

The Unmanned Aircraft System Traffic Management (UTM) system is a set of services offering an automated management of the airspace and thus providing safe and secure Unmanned Aerial Vehicle (UAV) flights in both controlled and uncontrolled airspace. Controlled airspace refers to the portion of the airspace that is under the authority of Air Traffic Control (ATC) and where separation services are offered, while uncontrolled airspace refers to the portion of airspace where aircraft are not regulated by ATC. This article is a comprehensive survey of the existing UTMs development efforts with a focus on the different UTMs architectures, the provided services, the used communication technologies and the decision-making process within UTMs. We firstly review the different UTM architecture and propose a novel UTM taxonomy based on high-level qualitative criteria. Secondly, we detail the services provided by UTMs with an emphasis on the used technologies in the identification, the surveillance, the monitoring, and the deconfliction services. Effective decision-making is crucial, particularly in emergency scenarios such as Air-to-Ground (A2G) communication loss, battery or motor malfunction, or encountering aerial obstacles, among other potential hazards. Despite its significance, the UTM decision-making process is not enough considered in the literature and especially in UTM surveys. We analyze and compare in this article both the centralized and decentralized UTM decision-making. Centralized decision-making is not conducted in real-time and primarily relies on Air-to-Ground (A2G) communication. In the decentralized case, the decision-making process primarily relies on communication and collaboration among UAVs with varying degrees of autonomy. We show in this paper that centralized decision-making may encounter issues with packet loss and imperfect data, which can negatively impact the quality of decision-making. We also highlight that the decentralized decision-making may also face challenges related to security and scalability, which can hinder its effectiveness. Finally, evaluating the performance of UTMs on a real environment raises several challenges and the simulation is a cost-effective alternative. Hence, we provide a summary of the existing UTMs simulators and discuss their main features.

Analysis of UTM Tracking Performance for Conformance Monitoring via Hybrid SITL Monte Carlo Methods

Conformance monitoring supports UTM safety by observing if unmanned aircraft (UA) are adhering to declared operational intent. As a supporting system, robust cooperative tracking is critical. Nevertheless, tracking systems for UAS traffic management (UTM) are in an early stage and under-standardized, and existing literature hardly addresses the problem. To bridge this gap, this study aims to probabilistically evaluate the impact of the change in tracking performances on the effectiveness of conformance monitoring. We propose a Monte Carlo simulation-based method. To ensure a realistic simulation environment, we use a hybrid software-in-the-loop (SITL) scheme. The major uncertainties contributing to the stochastic evaluation are measured separately and are integrated into the final Monte Carlo simulation. Latency tests were conducted to assess the performance of different communication technologies for cooperative tracking. Flight technical error generation via SITL simulations and navigational system error generation based on flight experiments were employed to model UA trajectory uncertainty. Based on these tests, further Monte Carlo simulations were used to study the overall impacts of various tracking key performance indicators in UTM conformance monitoring. Results suggest that the extrapolation of UA position enables quicker non-conformance detection, but introduces greater variability in detection delay, and exacerbates the incidence of nuisance alerts and missed detections, particularly when latencies are high and velocity errors are severe. Recommendations for UA position update rates of ≥1 Hz remain consistent with previous studies, as investments in increasing the update rate do not lead to corresponding improvements in conformance monitoring performance according to simulation results.

Development of UTM Monitoring System Based on Network Remote ID with Inverted Teardrop Detection Algorithm

The new regulation of Remote Identification (Remote ID) established by the FAA is predicted which will stimulate the application of Remote ID in UAS traffic management (UTM). Our research is aimed at the development of a UTM monitoring system based on the network Remote ID and the implementation of a new collision detection algorithm based on an inverted teardrop shape area and dynamic detection size. The newly introduced detection shape area in the UTM system could improve flight safety, increase airspace traffic, and provide a clear depiction of UAVs’ movement direction. The monitoring system consists of Remote ID hardware, a cloud database, and a web-based UTM application that runs on a personal laptop computer. A flight test was conducted involving a human pilot flying a quadcopter UAV to analyze the performance of the system and algorithm. The result found that the developed UTM monitoring system produces a reasonable average delay of around 0.94[Formula: see text]s with a standard deviation of 0.2[Formula: see text]s. The new detection algorithm shows a promising result that produces a larger buffer distance between UAVs compared to the circle shape algorithms.

Airspace Designs and Operations for UAS Traffic Management at Low Altitude

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.

Tactical conflict resolution in urban airspace for unmanned aerial vehicles operations using attention-based deep reinforcement learning

Unmanned aerial vehicles (UAVs) have gained much attention from academic and industrial areas due to the significant number of potential applications in urban airspace. A traffic management system for these UAVs is needed to manage this future traffic. Tactical conflict resolution for unmanned aerial systems (UASs) is an essential piece of the puzzle for the future UAS Traffic Management (UTM), especially in very low-level (VLL) urban airspace. Unlike conflict resolution in higher altitude airspace, the dense high-rise buildings are an essential source of potential conflict to be considered in VLL urban airspace. In this paper, we propose an attention-based deep reinforcement learning approach to solve the tactical conflict resolution problem. Specifically, we formulate this task as a sequential decision-making problem using Markov Decision Process (MDP). The double deep Q network (DDQN) framework is used as a learning framework for the host drone to learn to output conflict-free maneuvers at each time step. We use the attention mechanism to model the individual neighbor's effect on the host drone, endowing the learned conflict resolution policy to be adapted to an arbitrary number of neighboring drones. Lastly, we build a simulation environment with various scenarios covering different types of encounters to evaluate the proposed approach. The simulation results demonstrate that our proposed algorithm provides a reliable solution to minimize secondary conflict counts compared to learning and non-learning-based approaches under different traffic density scenarios.

Flight Test Analysis of UTM Conflict Detection Based on a Network Remote ID Using a Random Forest Algorithm

In an area where unmanned aerial system (UAS) traffic is high, a conflict detection system is one of the important components for the safety of UAS operations. A novel UAS traffic management (UTM) monitoring application was developed, including a conflict detection system using the inverted teardrop area detection based on real-time flight data transmitted from the network remote identification (Remote ID) modules. This research aimed to analyze the performance of the UTM-monitoring application based on flight test data using statistical and machine learning approaches. The flight tests were conducted using several types of small fixed-wing unmanned aerial vehicles (UAVs) controlled by a human pilot using a Taiwan cellular communication network in suburban and rural areas. Two types of scenarios that involved a stationary, on-the-ground intruder and a flying intruder were used to simulate a conflict event. Besides the statistical method calculating the mean and standard deviation, the random forest algorithm, including regressor and classifier modules, was used to analyze the flight parameters and timing parameters of the flight tests. The result indicates that the processing time of the UTM application was the most significant parameter to the conflict warning parameter, besides the relative distance and height between UAVs. In addition, the latency time was higher for the flight in the rural area than the suburban area and also higher for data transmitted from the flying position than the ground position. The findings of our study can be used as a reference for aviation authorities and other stakeholders in the development of future UTM systems.

Dynamic Capacity Management for Air Traffic Operations in High Density Constrained Urban Airspace

Unmanned Aircraft Systems (UAS) Traffic Management (UTM) is an active research subject as its proposed applications are increasing. UTM aims to enable a variety of UAS operations, including package delivery, infrastructure inspection, and emergency missions. That creates the need for extensive research on how to incorporate such traffic, as conventional methods and operations used in Air Traffic Management (ATM) are not suitable for constrained urban airspace. This paper proposes and compares several traffic capacity balancing methods developed for a UTM system designed to be used in highly dense, very low-level urban airspace. Three types of location-based dynamic traffic capacity management techniques are tested: street-based, grid-based, and cluster-based. The proposed systems are tested by simulating traffic within mixed (constrained and open) urban airspace based on the city of Vienna at five different traffic densities. Results show that using local, area-based clustering for capacity balancing within a UTM system improves safety, efficiency, and capacity metrics, especially when simulated or historical traffic data are used.

Parametric Study of Structured UTM Separation Recommendations with Physics-Based Monte Carlo Distribution for Collision Risk Model

With the increasing demand for unmanned aircraft system (UAS) traffic management (UTM) airspace comes the need to ensure the safe operation and management of said airspace. One layer of defense against mid-air-collision and the ensuing third-party injury or fatality is the pre-flight separation assurance. This could be achieved by establishing the separation requirements for the UTM traffic based on the flight dynamics and communication navigation surveillance (CNS) performance that could be achieved in the airspace in question. A modified Reich collision risk model, typically used in civil aviation for separation minima evaluation, was used for the evaluation of the initial separation that would meet the target level of safety within a prescribed look-ahead time. This paper presents the parametric evaluation of using this physics-based and Monte Carlo-driven Reich collision risk model to evaluate the separation recommendation needed to achieve 10−7 mid-air-collision risk in UTM. The evaluation was conducted for an encounter pair consisting of identical ∼1.2 kg quadrotors with various encounter geometries, cruise velocities, navigation uncertainties, and communication latency.

Structured Urban Airspace Capacity Analysis: Four Drone Delivery Cases

A route network-based urban airspace is one of the initial operational concepts of managing the high-density very low-level (VLL) urban airspace for unmanned aircraft system (UAS) traffic management (UTM). For the conceptual urban airspace, it is necessary to perform a quantitative analysis of urban airspace to stakeholders for designing rules and regulations. This study aims to discuss the urban airspace capacity for four different operation types by applying different sequencing algorithms and comparing its results to provide insight and suggestions for different operation cases to assist airspace designers, regulators, and policymakers. Four drone delivery operation types that can be applied in the high-density VLL urban airspace are analysed using the suggested four metrics: total flight time; total flight distance; mission completion time; the number of conflicts. The metrics can be calculated from a flight planning algorithm that we proposed in our previous studies. The algorithm for multiple agents flight planning problems consists of an inner loop algorithm, which calculates each agent’s flight plan, and an outer loop algorithm, which determines the arrival and departure sequences. For each operation type, we apply two different outer loops with the same inner loop to suggest an appropriate sequencing algorithm. Numerical simulation results show tendencies for each type of operation with regard to the outer loop algorithms and the number of agents, and we analyse the results in terms of airspace capacity, which could be utilised for designing structures depending on urban airspace situations and environments. We expect that this study could give some intuition and support to policymakers, urban airspace designers, and regulators.

Enhancing Trajectory-Based Operations for UAVs through Hexagonal Grid Indexing: A Step towards 4D Integration of UTM and ATM

Aviation is expected to face a surge in the number of manned aircraft and drones in the coming years, making it necessary to integrate Unmanned Aircraft System Traffic Management (UTM) into Air Traffic Management (ATM) to ensure safe and efficient operations. This research proposes a novel hexagonal grid-based 4D trajectory representation framework for unmanned aerial vehicle (UAV) traffic management that overcomes the limitations of existing square/cubic trajectory representation methods. The proposed model employs a hierarchical indexing structure using hexagonal cells, enabling efficient ground based strategic conflict detection and conflict free 4D trajectory planning. Additionally, the use of Hexagonal Discrete Global Grid Systems provides a more accurate representation of UAV trajectories, improved sampling efficiency and higher angular resolution. The proposed approach can be used for predeparture conflict free 4D trajectory planning, reducing computational complexity and memory requirements while improving the accuracy of strategic trajectory conflict detection. The proposed framework can also be extended for air traffic flow management trajectory planning, Air Traffic Control (ATC) workload measurement, sector capacity estimation, dynamics airspace sectorization using hexagonal sectors and traffic density calculation, contributing to the development of an efficient UTM system, and facilitating the integration of UAVs into the national airspace system with ATM

Security Threats and Cellular Network Procedures for Unmanned Aircraft Systems: Challenges and Opportunities

Researchers and standardization bodies have raised concerns about using legacy cellular networks for supporting unmanned aerial vehicle (UAV) operations. Different from traditional user equipment (UE), an unmanned aircraft system (UAS)-capable UE - UAV-UE or controller-UE - needs additional network security measures to ensure safe airspace operation. This article introduces the security requirements and threats with respect to three major themes: authentication and authorization, location information veracity and tracking, and command and control signaling. We present the 3GPP reference architecture for network connected UASs, the new application functions of the 5G core network, and the 5G security mechanisms and procedures for meeting the established requirements. Three 5G core application functions supporting UASs facilitate the interworking between the 3GPP network and the UAS traffic management, delivering location reports, validating UAS subscriptions, and matching UAS IDs with their respective UE IDs, among others. We identify opportunities for UAS network security research and recommend critical security features and processes to be considered for standardization. We conclude that while the 5G standard introduces important security mechanisms, more security research and benchmarking are needed for cellular networks to support secure and scalable real-time control of UAVs and the emerging applications enabled by them.