Mid-air Collision Risk Research Articles

Enhanced Air Traffic Control Safety Using low Earth orbit Satellites: Non-TCAS Aircraft

This paper presents a study on a novel TCAS design that combines the exploitation of low-orbit satellites with the existing TCAS system to improve operational efficiency and overcome challenges. With the continuous growth of air traffic, ensuring safety remains a top priority. TCAS was developed to mitigate the risks of aircraft collisions and is mandatory on large transport aircraft. TCAS uses information and data to determine the altitude and relative positions of nearby aircraft. However, despite the advances in air traffic control (ATC) systems, non-TCAS-equipped aircraft continue to operate in the airspace, potentially increasing the risk of mid-air collisions. In addition, existing TCAS systems often generate frequent and unnecessary alarms, especially in densely populated terminal areas, leading to erroneous pilot actions. The proposed solution aims to detect non-TCAS-equipped aircraft by other aircraft, whether they are equipped with TCAS or not. Therefore, the goal is to optimize the efficiency of TCAS to reduce the risk of mid-air collision and improve overall aviation safety. The management applications are distributed on the cloud to save resource exploitation, including energy consumption by processing and air traffic control-related exchanges.

Evaluating near midair collision reporting systems using aircraft surveillance data: A case study at a university airport

Introduction: A near midair collision (NMAC) is defined by the Federal Aviation Administration as an event in which the crew of an aircraft perceives a situation that could lead to a midair collision or an event in which the separation between two aircraft is less than 500 feet (Federal Aviation Administration, 2018). NMAC reports collected by safety reporting systems have long been used to study and mitigate midair collision risk. However, reports submitted are subjective and require the reporter to voluntarily provide this information. With the implementation of Automatic Dependent Surveillance-Broadcast, aircraft temporal and positional data are easily collectible. Method: Using internal safety system, NASA Aviation Safety Reporting System, and FAA Near Midair Collision System reports together with ADS-B data, the effectiveness of different safety reporting systems to collect NMAC data around a towered university airport were compared over a six-month period. Unreported events were identified by utilizing ADS-B data to calculate aircraft separation events of less than 500 feet. Results: While 10 events were reported to the internal safety system, only one was reported to the NASA ASRS, and none to the FAA NMACS within the study’s scope. Sixteen events in which aircraft were within 500 feet of each other were found using ADS-B data, with none of these events having been reported to any safety reporting system. Conclusions: The findings of this study highlight the need for increased sharing in aviation safety data and show the potential of ADS-B data as a tool in studying near midair collisions and midair collision risk. Practical Applications: NMAC safety reporting systems may be less effective than expected. The addition of other sources of data such as ADS-B may be necessary to identify and investigate NMACs and other relevant near miss safety events.

Situational NMAC risk assessment using conditional random field for UAV tiered aerial passage operations

Complex urban unmanned aerial vehicle (UAV) operations necessitate near mid-air collision (NMAC) risk assessment to ensure safety. In this work, a novel situational NMAC risk assessment approach is developed by integrating the conditional random field (CRF) algorithm with safety metrics and conflict resolution performance to facilitate UAV tiered aerial passage operations. Firstly, a concept of tiered aerial passages is designed to provide a practical urban airspace planning scheme that addresses UAV heterogeneity. For UAV tiered aerial passage operations, a situational NMAC risk assessment framework is formulated, enabling the host UAV to evaluate and respond to real-time risks, ensuring avoidance of potential collisions. Specifically, a probabilistic risk assessment model is proposed based on the CRF algorithm, considering UAV safety metrics and resolution performance. Then the NMAC risks in overtaking, approaching head-on, and converging UAV situational encounters are quantified considering advisory, caution, and warning risk alert levels. To resolve risks assessed, acceptable, tolerable, and unacceptable scenarios are proposed to enhance the flexibility and personalization of the resolution decision-making. The proposed method is validated by testing encounter situations and comparative study. The results show that the proposed method outperforms UAV NMAC risk assessment, effectively tailoring different risk requirements for diverse UAV operations.

Current approaches in UAV Operational Risk Assessment and Practical Considerations

The expansion of Unmanned Aircraft Systems (UAS) is creating new markets, particularly in Urban Air Mobility (UAM), which presents unique challenges. Beyond the risk of mid-air collisions, UAM services in urban areas introduce ground risks to buildings, traffic routes, and pedestrians. This research explores trends in Ground Risk models for different operation planning stages: strategic, pre-tactical, and tactical. It offers a logical pipeline for UAS operators, emphasizing detailed analysis and data source considerations, along with the importance of model interoperability. Using a Naples case study, the research provides practical steps for national authorities to streamline the UAS authorization process.

Vision-Based Mid-Air Object Detection and Avoidance Approach for Small Unmanned Aerial Vehicles with Deep Learning and Risk Assessment

With the increasing demand for unmanned aerial vehicles (UAVs), the number of UAVs in the airspace and the risk of mid-air collisions caused by UAVs are increasing. Therefore, detect and avoid (DAA) technology for UAVs has become a crucial element for mid-air collision avoidance. This study presents a collision avoidance approach for UAVs equipped with a monocular camera to detect small fixed-wing intruders. The proposed system can detect any size of UAV over a long range. The development process consists of three phases: long-distance object detection, object region estimation, and collision risk assessment and collision avoidance. For long-distance object detection, an optical flow-based background subtraction method is utilized to detect an intruder far away from the host. A mask region-based convolutional neural network (Mask R-CNN) model is trained to estimate the region of the intruder in the image. Finally, the collision risk assessment adopts the area expansion rate and bearing angle of the intruder in the images to conduct mid-air collision avoidance based on visual flight rules (VFRs) and conflict areas. The proposed collision avoidance approach is verified by both simulations and experiments. The results show that the system can successfully detect different sizes of fixed-wing intruders, estimate their regions, and assess the risk of collision at least 10 s in advance before the expected collision would happen.

Airspace Saturation and Midair Collision Risk: A Case Study at a Class D Airport

Airspace Saturation and Midair Collision Risk: A Case Study at a Class D Airport

Poster] Spatial modelling of midair collision risk using ADS-B data


 The airspace environment is a complex system that is only expected to continue increasing in complexity with the introduction of new types of air vehicles and operations, such as uncrewed aircraft, and the projected growth of air traffic volumes. This increase in complexity brings a need for investigating and developing new models of complex airspace environments to better understand their constituent parts. To address this need, this paper presents a methodology to create a geospatial model of complex airspace environments which can be used to study any geospatially distributed entity part of these environments. The methodology leverages Discrete Global Grid Systems (DGGS) for this purpose, a Geographic Information Systems framework previously utilized in geography and urban planning. The usefulness of the model is demonstrated on a case study geospatial entity which is the risk of midair collisions for many geospatially distributed points in an airspace region of interest. Since such a model needs to be able to work for any type of air vehicle and airspace region in a fully three-dimensional model that is capable of performing time varying analysis in a computationally efficient manner, a rudimentary midair collision risk model was also developed which satisfies these requirements. Air traffic data from the OpenSky Network was collected and integrated in the geospatial model and the midair collision risk model was used to calculate the risk of collisions for many geospatially distributed points in the airspace for four scenarios of ascending airspace complexity. The results from these four scenarios showed that the proposed methodology can be used to study the risk of midair collisions for different points in the airspace for any type of air vehicle and airspace region of interest in a fully three-dimensional model that is capable of performing time varying analysis in a computationally efficient manner.
 

Data-driven mid-air collision risk modelling using extreme-value theory

Mid-air collision risk estimation is crucial for maintaining aviation safety and improving the efficiency of air traffic procedures. This paper introduces a novel, data-driven methodology for estimating the probability of mid-air collisions between aircraft by combining Monte Carlo simulation and the Peaks Over Threshold approach from Extreme Value Theory. This innovative approach has substantial advantages over traditional methods. Firstly, it reduces the number of assumptions about the traffic flow compared to traditional analytical methods. In fact, data-driven techniques require fewer assumptions, as they inherently capture the structures of the traffic flow within the underlying data. Secondly, it converges faster than methods based on crude Monte Carlo simulation. Notably, by employing Extreme Value Theory, this approach enables efficient evaluation of low-probabilities, which are commonly found in collision risk modelling. The effectiveness of the proposed methodology is demonstrated through estimating the probability of a mid-air collision in a real-world practical example. The case study investigates the risk of collisions between departures and go-arounds in the terminal airspace at Zurich Airport, highlighting the potential for improved safety and efficiency in air traffic management.

Quantifying Specific Operation Airborne Collision Risk through Monte Carlo Simulation

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. Simulated probable traffic scenarios are superimposed on a desired Uncrewed Aircraft operation and then sampled using Monte Carlo methods. The results are used to estimate the operation-specific collision probability with known uncertainty in the output. The methodology is demonstrated for an example medical logistics operation in the United Kingdom, and a Target Level of Safety is used as a benchmark to decide whether the operation should be permitted.

Deep generative modelling of aircraft trajectories in terminal maneuvering areas

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. For that purpose, Monte Carlo simulation methods can be applied to estimate aircraft conflict probability but require the accurate generation of artificial trajectories. Generative models allow to generate an infinite number of trajectories for air traffic procedures where only few observations are available. The generated trajectories must not only resemble observed trajectories in terms of statistical distributions but they should stay flyable and consider uncertainty due to weather, air traffic control, aircraft performances, or human factors. This paper focuses on the generation problem, and its main contribution lies in the adaptation of the Variational Autoencoder structure to the problem of 4-dimensional aircraft trajectories modelling using Temporal Convolutional Networks and a prior distribution composed of a Variational Mixture of Posteriors (VampPrior). The proposed model has been trained on trajectories in the Terminal Manoeuvre Area of Zurich airport, which have a particularly high degree of variability as air traffic controllers often take actions that deviate aircraft from the nominal approach procedure. The model has demonstrated great abilities to take into account such amount of uncertainty. Regarding metrics that evaluate the estimation of the statistical distribution of the observed trajectories, and the flyability of the generated ones, the proposed method outperforms traditional statistical methods by being able to generate more complex and realistic trajectories.

Near Midair Collision Analog for Drones Based on Unmitigated Collision Risk

The capability to avoid other air traffic is a fundamental component of the layered conflict management system to ensure safe and efficient operations. The evaluation of systems designed to mitigate the risk of midair collisions of manned aircraft is based on large-scale modeling and simulation efforts and a quantitative volume defined as a near midair collision. Six-degree-of-freedom rigid point mass simulations are routinely employed by standards developing organizations when designing these systems. Because midair collisions are difficult to observe in these simulations and are inherently rare events, basing evaluations on near midair collisions enables a more robust statistical analysis. However, a near midair collision and its underlying assumptions for assessing close encounters with manned aircraft do not adequately consider the different characteristics of smaller drone encounters. The primary contribution of this paper is a quantitative criterion to use when simulating two or more smaller drones in sufficiently close proximity that a midair collision might reasonably occur and without any mitigations to reduce the likelihood of a midair collision. The criteria assume a historically motivated upper bound for the collision likelihood. We also demonstrate that the near midair collision analogs can be used to support modeling and simulation activities.

항공안전 데이터를 이용한 항공기 공중충돌위험식별 모형 검증 및 고도화

In case of South Korea, the airspace which airlines can operate is extremely limited due to the military operational area located within the Incheon flight information region. As a result, safety problems such as mid-air collision between aircraft or Traffic alert and Collision Avoidance System Resolution Advisory (TCAS RA) may occur with higher probability than in wider airspace. In order to prevent such safety problems, an mid-air collision risk detection model based on Detect-And-Avoid (DAA) well clear metrics is investigated. The model calculates the risk of mid-air collision between aircraft using aircraft trajectory data. In this paper, the practical use of DAA well clear metrics based model has been validated. Aviation safety data such as aviation safety mandatory report and Automatic Dependent Surveillance Broadcast is used to measure the performance of the model. The attributes of individual aircraft track data is analyzed to correct the threshold of each parameter of the model.

Empirical study of airport geofencing for unmanned aircraft operation based on flight track distribution

With the increasing activities of unmanned aircraft (UA) near airports, severe challenges have emerged regarding airport safety management. This paper presents a brand-new data-driven based approach to accurately design the geofencing for UAs around an airport, focusing on midair collision risk between commercial flights and small UAs which is considered as the most important hazard. In premise, prevalent trajectory extraction from real flight tracks is realized based on an advanced and general model, taking the advantage of describing turning legs which is more popular in terminal area. As a key solution, a Gauss-Laplace-composite distribution is provided for rigorous estimation of flight track distribution in line with the extracted prevalent trajectory. Based on those, airport geofencing is proposed consisting of an inner critical area and an outer buffer area. This approach can be adaptive for different traffic patterns of an airport. The buffer area is an extension range to help the execution of some defense measures. Results of an empirical study for setting up geofencing for small UAs around Chongqing Jiangbei International Airport (ZUCK) show the superiority of the proposed approach, which indicates that it can provide practical application, especially varying from different target level of safety.

Accelerated landing in a stingless bee and its unexpected benefits for traffic congestion.

To land, flying animals must simultaneously reduce speed and control their path to the target. While the control of approach speed has been studied in many different animals, little is known about the effect of target size on landing, particularly for small targets that require precise trajectory control. To begin to explore this, we recorded the stingless bees Scaptotrigona depilis landing on their natural hive entrance-a narrow wax tube built by the bees themselves. Rather than decelerating before touchdown as most animals do, S. depilis accelerates in preparation for its high precision landings on the narrow tube of wax. A simulation of traffic at the hive suggests that this counterintuitive landing strategy could confer a collective advantage to the colony by minimizing the risk of mid-air collisions and thus of traffic congestion. If the simulated size of the hive entrance increases and if traffic intensity decreases relative to the measured real-world values, 'accelerated landing' ceases to provide a clear benefit, suggesting that it is only a useful strategy when target cross-section is small and landing traffic is high. We discuss this strategy in the context of S. depilis' ecology and propose that it is an adaptive behaviour that benefits foraging and nest defence.

Simulation modelling of traffic collision avoidance system with wind disturbance

Traffic collision avoidance systems (TCASs) are accepted worldwide as last-resort means of reducing the risk of midair collisions between aircraft [1]. Before deploying the different versions of the constantly evolving TCAS and because of the potential for catastrophic errors, rigorous analyses organized by several civil aviation authorities, e.g., the Federal Aviation Administration (FAA) and EuroControl, are required [2]. A combination of elaborate flight tests and considerate simulation studies are performed to ensure system effectiveness and safety.

Structuring the safety case for unmanned aircraft system operations in non-segregated airspace

Routine access to non-segregated airspace is a key enabler for the civilian Unmanned Aircraft System (UAS) industry. Approvals for UAS operations in this airspace are contingent on the provision of a safety case, which details how the risk of a Mid-Air Collision (MAC) accident will be managed to an acceptable level. There is no accepted framework for structuring operational safety cases for UAS and this gives rise to a number of challenges to the application of the regulation by “safety target” approach. Further, a wide range of controls has been proposed for mitigating the risk, however the effectiveness of the controls is not known. A reconciliation and extension of existing causal models describing the MAC accident sequence is provided in this paper. A barrier bow tie model is developed as a means for structuring the safety case for generic UAS operations in non-segregated airspace. The model is applied to the classification of over 50 commonly used risk controls and the relationship between the control and the manner in which the reduction in MAC risk is achieved is determined. A case-study application is also presented validating the utility of the tool in the development and communication of safety cases for UAS operations in civilian airspace.

TCAS Solution to Reduce Alarm Rate in Cockpit and Increase Air Safety

Over the years, air traffic has continued to increase. The development of modern systems has made it possible to cope with this increase, whilst maintaining the necessary level of safety. The Traffic Alert and Collision Avoidance System (TCAS) has been developped to reduce the risk of mid-air collision and is currently mandated on all large transport aircraft This system is based on interrogations to determine the altitude and relative positions of nearby aircraft. Despite technical advances in ATC systems, there are cases when the separation provision fails due to a human or technical error. Any separation provision failures may result in an increased risk of a midair collision. These systems would generate a high rate of unnecessary alarms especially in dense terminal areas and pilots become stressed and wrong actions can be made. A new design of TCAS is investigated in this paper to compensate for any limitations of ATC performance and decrease the rate of unnecessary alerts. It is based on transmitting altitudes and GPS coordinates between aircraft transponders to determine the accurate positions and directions of movement of nearby aircraft.

Analysis of induced Traffic Alert and Collision Avoidance System collisions in unsegregated airspace using a Colored Petri Net model

For rigorously safe aviation, it is of great significance to conduct an analysis of the current and new operations, providing a global perspective of the scenario dynamics and a better understanding of the potential collision occurrence for risk assessment. The objective of this study is to consider a number of the difficulties that are involved when establishing a validation of the Traffic Alert and Collision Avoidance System (TCAS), which constitutes the last resort for reducing the risk of near mid-air collisions between aircraft in a multithread scenario, and to analyze the TCAS logic using a causal model. In this paper, the causal model that is specified in the Colored Petri Net (CPN) formalism is employed as a key approach to analyze quantitatively the state space of a congested traffic scenario in which the events could transform a conflict into a potential collision. It offers a rigorous tool not only for TCAS validation but also for the analysis of a wide range of properties of the TCAS behavior. This CPN model assumes unrestrained initial positions and TCAS II-equipped aircraft; it is demonstrated to be extremely effective for generating all possible future TCAS failure end-states from the current locations, and the Interactive Collision Avoidance Simulator EuroControl simulator is used to illustrate the collision process of a three-aircraft scenario.

Error model estimation for airborne beacon‐based surveillance

A next-generation aircraft collision avoidance system under development is designed to reduce unnecessary alerts while reducing the risk of mid-air collision. Current and future collision avoidance systems rely upon range, bearing and altitude information from airborne, beacon-based surveillance. Realistic models of surveillance error characteristics are needed in order to develop and certify the safety and performance of these systems. Prior models of bearing measurements assumed uncorrelated Gaussian noise. This study explains how hidden Markov models can be used to capture the time-correlated error characteristics exhibited by recorded surveillance data. Simulations show the impact of this higher-fidelity model on predicted collision avoidance performance.

A causal model to explore the ACAS induced collisions

Current Air Traffic Management research programs (i.e. Single European Sky ATM (air traffic management) Research (SESAR), next generation air transportation system (NextGen)), try to overcome airside capacity shortages while improving cost-efficient operations and safety. An increment in the airspace traffic density can lead to congested traffic scenarios for which it becomes necessary to develop new safety procedures that address multithread threats. This paper considers some of the difficulties in establishing validation of the airborne collision avoidance system (ACAS), which constitutes the last-resort for reducing the risk of near mid-air collision between aircraft in a multithread scenario. A causal model that is specified in Colored Petri Net (CPN) formalism is presented as a key approach to analyze the state space of a congested traffic scenario in which the events that could transform a conflict into a collision are identified, providing a challenging tool not only for validation but also for the implementation of a new ACAS logic. The InCAS EuroControl simulator has been used to illustrate the importance of cause–effect analysis for the relationships between various encounters that arise in a multithread scenario, in which TCAS II v. 7.1 fails to avoid a collision when two Resolution Advisories are issued without consideration of the downstream effects.