Mid-air Collision Research Articles

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.

Air Traffic Controllers' Perspectives on Unmanned Aerial Vehicles Integration into Non-Segregated Airspace

The integration of Unmanned Aerial Vehicles (UAVs) into non-segregated airspace presents both opportunities and challenges for air traffic control (ATC). The aim of the study is to explore the perspectives of air traffic controllers on the current and anticipated challenges, workload, stress factors, performance errors, and mitigation strategies related to UAV integration. The sample consisted of 213 air traffic controllers in Türkiye. UAV operations have been available in Türkiye not only for military purposes but also for purposes such as forest fires, earthquakes, security, and others for a long time, and these UAV operations are provided with air traffic services (ATS) by air traffic controllers. The results show that air traffic controllers are concerned about mid-air collisions due to UAV technology limits and regulatory gaps, along with managing risks and unique flight characteristics. Addressing technology limitations, regulatory ambiguity, and other factors necessitates a comprehensive strategy. Solutions must prioritize collision avoidance systems, clear communication guidelines, and defined no-fly zones. It is recommended that future studies focus on the comprehensive impact of UAVs on air traffic operations and the development of regulations.

Optical Flow-Based Obstacle Detection for Mid-Air Collision Avoidance.

The sky may seem big enough for two flying vehicles to collide, but the facts show that mid-air collisions still occur occasionally and are a significant concern. Pilots learn manual tactics to avoid collisions, such as see-and-avoid, but these rules have limitations. Automated solutions have reduced collisions, but these technologies are not mandatory in all countries or airspaces, and they are expensive. These problems have prompted researchers to continue the search for low-cost solutions. One attractive solution is to use computer vision to detect obstacles in the air due to its reduced cost and weight. A well-trained deep learning solution is appealing because object detection is fast in most cases, but it relies entirely on the training data set. The algorithm chosen for this study is optical flow. The optical flow vectors can help us to separate the motion caused by camera motion from the motion caused by incoming objects without relying on training data. This paper describes the development of an optical flow-based airborne obstacle detection algorithm to avoid mid-air collisions. The approach uses the visual information from a monocular camera and detects the obstacles using morphological filters, optical flow, focus of expansion, and a data clustering algorithm. The proposal was evaluated using realistic vision data obtained with a self-developed simulator. The simulator provides different environments, trajectories, and altitudes of flying objects. The results showed that the optical flow-based algorithm detected all incoming obstacles along their trajectories in the experiments. The results showed an F-score greater than 75% and a good balance between precision and recall.

Experimental validation of a UAS at engine ingestion conditions: Part 2 Model validation

With uncrewed aircraft systems (UASs) becoming more popular in recent years there is an increasing risk of a midair collision with aircraft for which there are currently no regulations in place. Bird impacts have been thoroughly studied for years and are considered soft body impacts. The bird impact research cannot be directly applied to UAS ingestions since they are hard body impacts. Some recent research has looked into understanding the differences between soft and hard body impacts on aircraft engines. However, this research used primarily unvalidated models for the harsh conditions of an ingestion event, which creates uncertainty in the results. This work intends to fill in this gap in understanding by developing a validated computational model of a UAS for engine ingestion simulations. Part I of this work presented the experimental test plan and experimental results of UAS critical components and full UAS being launched at test articles with representative features of a fan blade. This work discusses the computational modeling completed in LS-DYNA to simulate the physical tests and to update and validate the UAS models.

Experimental validation of a UAS at engine ingestion conditions: Part I Experiments

In recent years, uncrewed aircraft systems (UASs) have greatly increased in popularity and accessibility to the public. This dramatic increase has led to a substantial risk of a midair collision between a UAS and a commercial aircraft, and these collisions need to be analyzed to understand the dangers associated with them. Soft body (i.e., birds or ice) impacts have been happening since the early days of flight and have been studied thoroughly. In contrast, there has been significantly less research on hard body impacts, with most of the currently published research not using experimentally validated models at the conditions expected to be seen in an engine ingestion. The purpose of this work is to discuss the design and execution of experiments to improve and validate a UAS model for high speed impacts for future engine ingestion studies. The overall effort consisted of first conducting physical tests that represent specific high speed ingestion scenarios. Then simulating these tests using a high fidelity finite element tool. Then finally updating the model as necessary. The design of the experiments, experimental test setup, instrumentation and experimental results are highlighted in Part I of this work. The computational model update and validation of the UAS at conditions seen in an engine ingestion are done in Part II of this work.

Advanced collision risk estimation in terminal manoeuvring areas using a disentangled variational autoencoder for uncertainty quantification

Air Traffic Management aims at ensuring safety during aircraft operations, particularly within Terminal Manoeuvring Areas where traffic density is high. The challenge lies in balancing safety and efficiency by closely managing the likelihood of mid-air collisions regarding the airport movements. Traditional models like the Reich and Anderson-Hsu models have been influential, but they fall short in representing the complex reality of Terminal Manoeuvring Areas. Data-driven approaches are emerging, with Monte Carlo simulations offering a more flexible methodology for collision risk estimation. This paper introduces a framework for assessing Mid-Air Collision likelihood resulting from Terminal Manoeuvring Area procedures by combining the field of Deep Generative Modelling using a Variational Autoencoder with the domain of rare event statistics through Subset Simulation. By incorporating disentanglement into the Variational Autoencoders model, we create a latent space that aligns dimensions with distinctive trajectory traits. Then, Subset Simulation is employed to gauge Mid-Air Collision probability, utilizing latent representations as input. Finally, sensitivity analysis reveals pivotal factors influencing collision risk, correlated with trajectory attributes via disentanglement. The methodology is applied to traffic around Zurich Airport: it evaluates the risk arising from go-around and take-off procedures using Automatic Dependent Surveillance-Broadcast data.

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.

A Risk Assessment Method for Mid-Air Collisions in Urban Air Mobility Operations

A Risk Assessment Method for Mid-Air Collisions in Urban Air Mobility Operations

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

Multiple machine learning modeling on near mid-air collisions: An approach towards probabilistic reasoning

This work presents a mathematical model to predict and explain Near Mid-Air Collisions (NMACs) based on the NASA Aviation Safety Reporting System (ASRS) database. The ASRS database contains more than 200,000 aviation incidents, which are used to learn how the combination of risk influencing factors (RIFs), such as crew size and component fatigue, affects the safety of airspace operations. Bayesian Networks (BNs) combine theory of probabilities with theory of graphs and are considered one of the most effective theoretical models in the fields of knowledge representation and reasoning with uncertainty. The resulting model allows to calculate the posterior probabilities of some targeted outputs, therefore providing a mathematically consistent framework to quantify and to compute with uncertainty the likelihood of incident occurrence over time when some factors are known. Furthermore, the bidirectional reasoning technology of BNs can calculate the posterior probabilities of its variables under the system incident condition, and find out the most likely combination that caused a NMAC. Finally, the resulting probabilistic models are compared with sixteen Machine Learning Algorithms, and advantageous properties were critically evaluated, such as a white-box reasoning and probability as a measure of certainty about the state of unobserved variables.

Bayesian network model of aviation safety: Impact of new communication technologies on mid-air collisions

This article presents a method of estimating the risk of a mid-air collision. The proposed method is an enhancement of the traditional aviation safety model - Integrated Safety Assessment Model (ISAM) - developed by the Federal Aviation Administration (FAA) and EUROCONTROL. ISAM is a mix of event-based models and fault trees that identifies causes of 35 different types of aviation accidents. While useful for conceptual understanding of accidents, the model does not handle human-technical or inter-system interactions. These drawbacks are especially evident when assessing safety impact of new communication, navigation and surveillance technologies since they rely on pilots and controllers. We propose a method of analyzing the impact of new technologies in aviation by presenting a case study of the Data Communication system – a new technology developed by the FAA used for communication between pilots and controllers. The method builds upon ISAM and leverages a Bayesian Network to estimate safety risk. The results indicate that the implementation of Data Comm can reduce the risk of collision by 25 %. In addition, if a collision has occurred, it is 10 million times more probable that the likely culprit is an error in human communication rather than a failure of communication equipment.

What Matters in the Effectiveness of Airborne Collision Avoidance Systems? Monte Carlo Simulation of Uncertainties for TCAS II and ACAS Xa

TCAS II is a rule-based airborne collision avoidance system (ACAS) that is used in current commercial air transport operations, and ACAS Xa is a new optimization-based system. Operational validation studies have mainly used deterministic simulations of ACAS performance using various sets of encounters. Recently a new approach was developed, which employs Monte Carlo (MC) simulation of agent-based models to evaluate the impact of sensor errors and pilot response variability. This paper contrasts the results of both approaches in a comparison of TCAS II and ACAS Xa for various types of synthetic encounters. It was found that conventional estimates of near mid-air collision (NMAC) probabilities are often lower than the estimates achieved using MC simulation, and that the biases in the P(NMAC) estimates are consistently larger for ACAS Xa than for TCAS II. Contributions to unresolved risk are largest for pilot performance, then for encounter types, and finally for sensor errors. The contribution of non-responding pilots is much larger than the differences between TCAS II and ACAS Xa. It is concluded that the agent-based MC simulation overcomes the limitations in traditional evaluation of altimetry errors and pilot response, providing an independent means to effectively analyze the robustness of ACASs.

Numerical simulation of mid-air collisions between droplets and particles: An examination of particle forces and kinetic energy dissipation

Mid-air collisions between droplets and particles are common in practical fields. However, existing research mostly focuses on stationary collisions, leading to limited comprehension of particle forces and kinetic energy dissipation during mid-air collisions. To address this gap, we employed a three-dimensional numerical model that combines the volume of fluid and particle motion models to explore this phenomenon. The pressure force is the most influential factor in force analysis. By increasing the Weber number, the pressure force and hence the total force are amplified. Moreover, reducing the Reynolds number and the collision angle results in an increase in the viscous force. The principal form of kinetic energy dissipation is pressure energy. High Weber numbers decrease the dissipation of initial kinetic energy, and the impact of the Reynolds number is minimal. Furthermore, increasing collision angles result in decreased kinetic energy dissipation, while the dissipation of rotational kinetic energy is notably negligible.

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.

Vision-Based Flying Obstacle Detection for Avoiding Midair Collisions: A Systematic Review.

This paper presents a systematic review of articles on computer-vision-based flying obstacle detection with a focus on midair collision avoidance. Publications from the beginning until 2022 were searched in Scopus, IEEE, ACM, MDPI, and Web of Science databases. From the initial 647 publications obtained, 85 were finally selected and examined. The results show an increasing interest in this topic, especially in relation to object detection and tracking. Our study hypothesizes that the widespread access to commercial drones, the improvements in single-board computers, and their compatibility with computer vision libraries have contributed to the increase in the number of publications. The review also shows that the proposed algorithms are mainly tested using simulation software and flight simulators, and only 26 papers report testing with physical flying vehicles. This systematic review highlights other gaps to be addressed in future work. Several identified challenges are related to increasing the success rate of threat detection and testing solutions in complex scenarios.

Risk Assessment Method for UAV’s Sense and Avoid System Based on Multi-Parameter Quantification and Monte Carlo Simulation

The rise in Unmanned Aerial Vehicle (UAV) usage has opened exciting possibilities but has also introduced risks, particularly in aviation, with instances of UAVs flying dangerously close to commercial airplanes. The potential for accidents underscores the urgent need for effective measures to mitigate mid-air collision risks. This research aims to assess the effectiveness of the Sense and Avoid (SAA) system during operation by providing a rating system to quantify its parameters and operational risk, ultimately enabling authorities, developers, and operators to make informed decisions to reach a certain level of safety. Seven parameters are quantified in this research: the SAA’s detection range, field of view, sensor accuracy, measurement rate, system integration, and the intruder’s range and closing speed. While prior studies have addressed these parameter quantifications separately, this research’s main contribution is the comprehensive method that integrates them all within a simple five-level risk rating system. This quantification is complemented by a risk assessment simulator capable of testing a UAV’s risk rating within a large sample of arbitrary flight traffic in a Monte Carlo simulation setup, which ultimately derives its maximum risk rating. The simulation results demonstrated safety improvements using the SAA system, shown by the combined maximum risk rating value. Among the contributing factors, the detection range and sensor accuracy of the SAA system stand out as the primary drivers of this improvement. This conclusion is consistent even in more regulated air traffic imposed with five or three mandatory routes. Interestingly, increasing the number of intruders to 50 does not alter the results, as the intruders’ probability of being detected remains almost the same. On the other hand, improving SAA radar capability has a more significant effect on risk rating than enforcing regulations or limiting intruders.

Vision-Based In-Flight Collision Avoidance Control Based on Background Subtraction Using Embedded System.

The development of high-performance, low-cost unmanned aerial vehicles paired with rapid progress in vision-based perception systems herald a new era of autonomous flight systems with mission-ready capabilities. One of the key features of an autonomous UAV is a robust mid-air collision avoidance strategy. This paper proposes a vision-based in-flight collision avoidance system based on background subtraction using an embedded computing system for unmanned aerial vehicles (UAVs). The pipeline of proposed in-flight collision avoidance system is as follows: (i) subtract dynamic background subtraction to remove it and to detect moving objects, (ii) denoise using morphology and binarization methods, (iii) cluster the moving objects and remove noise blobs, using Euclidean clustering, (iv) distinguish independent objects and track the movement using the Kalman filter, and (v) avoid collision, using the proposed decision-making techniques. This work focuses on the design and the demonstration of a vision-based fast-moving object detection and tracking system with decision-making capabilities to perform evasive maneuvers to replace a high-vision system such as event camera. The novelty of our method lies in the motion-compensating moving object detection framework, which accomplishes the task with background subtraction via a two-dimensional transformation approximation. Clustering and tracking algorithms process detection data to track independent objects, and stereo-camera-based distance estimation is conducted to estimate the three-dimensional trajectory, which is then used during decision-making procedures. The examination of the system is conducted with a test quadrotor UAV, and appropriate algorithm parameters for various requirements are deduced.

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.