Browse Topic: Air traffic control
Air Traffic Management (ATM) must be familiar with the exact Aircraft Take-off Weights (ATOWs) of airplanes to make the most use of runways, maintain safety margins high, and keep utilization and resources in balance. This paper aims to present a dependable ATOW forecasting methodology that can assist the air transport industry in enhancing operational decision-making. This research used datasets acquired from the EUROCONTROL Performance Review Commission (PRC) 2024 Aircraft Take-Off Weight Estimation dataset featuring 527,000 flights over Europe containing aircraft details, air trips and flight conditions. Technique comprises structured data input, inspection of missing data, timestamp aggregation to identify demand cycles over time, and domain-specific feature engineering using distance_per_minute, block_minutes, taxiout_ratio, and a strong wake turbulence metric The two supervised learning models used were Linear Regression (LR) for understanding and XGBoost for performance prediction In comparison to LR's 4,409 kg MAE (mean absolute error), 7,061 kg RMSE (root mean square error), and 0.9825 R2 value, XGBoost significantly excelled with validation results showing an R2 value of 0.9992 and an RMSE of 1,514 kg In the absence of labelled test targets, cross-validation nevertheless showed a constant degree of generalizability The residual diagnostics showed that the model was reliable for practical execution with low-variance deviations that were unbiased An accurate ATOW estimate improves the demand-capacity balance and On-Time Performance (OTP) in ATM, which in turn affects the runway schedule, wake turbulence diversion, slot allocation, and fuel planning The results highlight the need to include ATOW predictions in both tactical and strategic planning to reduce delays, increase airspace usage, and promote sustainable aviation operation and possesses significant improvements will consist of weather and runway conditions, stochastic ambiguity computation, and drift monitoring to keep up with ever-changing operating variables while maintaining accurate forecasts.
This paper presents a multi-aircraft Markov decision process congestion game to resolve multi-aircraft near midair collisions (NMACs) for small unmanned aerial vehicles (sUAVs). Two key features of this framework are: 1) it leverages the concept of strategic equilibria from game theory to define optimality in multi-aircraft near midair encounters and 2) it extends the existing NMAC metrics to stochastic formulations via the occupancy measure of a Markov decision process. This game-theoretic approach decomposes the classically centralized air traffic control objective to multiple objectives that correspond to each aircraft within the NMAC, and as result, provides an aircraft-centric notion of optimality and safety that is well-suited for distributed conflict resolutions in multi-aircraft NMACs. In addition to modeling multi-aircraft as a game, stochastic metrics that extend the deterministic notions of NMACs are explored. The safety and optimality of the Nash equilibrium multi-aircraft trajectory under a joint NMAC threat is analyzed under different NMAC thresholds and evaluation metrics. Results are simulated numerically for a representative sUAV NMAC geometry.
With the exponential rise in drone activity, safely managing low-flying airspace has become challenging — especially in highly populated areas. Just last month an unauthorized drone collided with a ‘Super Scooper’ aircraft above the Los Angeles wildfires, grounding the aircraft for several days and hampering the firefighting efforts.
Hensoldt Taufkirchen, Germany lothar.belz@Hensoldt.net
Urban Air Mobility (UAM) envisions heterogenous airborne entities like crewed and uncrewed passenger and cargo vehicles within, and between urban and rural environment. To achieve this, a paradigm shift to a cooperative operating environment similar to Extensible Traffic Management (xTM) is needed. This requires the blending of traditional Air Traffic Services (ATS) with the new generation UAM vehicles having their unique flight dynamics and handling characteristics. A hybrid environment needs to be established with enhanced shared situational awareness for all stakeholders, enabling equitable airspace access, minimizing risk, optimized airspace use, and providing flexible and adaptable airspace rules. This paper introduces a novel concept of distributed airspace management which would be apt for all kinds of operational scenarios perceived for UAM. The proposal is centered around the efficiency and safety in air space management being achieved by self-discipline. It utilizes Blockchain’s core concepts like Distributed Ledger, Consensus, and Immutable Smart Contracts. The concept blends harmoniously to the Concept of Operations (CONOPS) recently published by Federal Aviation Administration (FAA), though the degrees of involvement by various actors of the eco system are primed for the very adaptation soon when fully autonomous aircraft are expected to dominate the urban skies. Strategic deconfliction and cooperative management are effectively realized with distribution of airspace knowledge, participative decision making and mutual trust. The concept is scalable to the foretold autonomy in this area. Trend predictors extrapolate a massive increase in dynamics, interactions and decision making as the flying vehicles count occupying a city's airspace, is set for exponential growth with personally owned flying vehicles. Proposed solution would operate efficiently with current computing technologies and can be scaled to be resident onboard or offboard the vehicle.
Aerospace is an industry where competition is high and the need to ensure safety and security while managing costs is foremost. Stakeholders, who gain the most by working together, do not necessarily trust each other. Changing backbone technologies that drive enterprise systems and secure historical records does not happen quickly (if at all). At best, businesses adapt incrementally, building customized applications on top of legacy systems. The complexity of these legacy systems leads to duplication of efforts and data storage, making them very inefficient. Technology that augments, rather than replaces, is needed to transform these complex systems into efficient, digital processes. Blockchain technology offers collaborative opportunities for solving some of the data problems that have long challenged the aerospace industry. The industry has been slow to adopt the technology even though experts agree that it has real potential to revolutionize the global supply chain—including maintenance, repair, and overhaul (MRO)—driving tremendous cost, excess inventory, and inefficiencies out of the system. This chapter discusses how the adoption of blockchain technology could have a significant impact on the aerospace industry and addresses some of the unsettled concerns surrounding the implementation of the technology.
This article addresses the critical need for enhanced weather observation and prediction systems for rotary-wing aircraft. Current weather systems lack granularity in low-altitude airspace, posing safety risks. The application of the ASTM F3673 - 23 Weather Standard Specification is proposed to standardize weather data collection and transition towards a weather sensor performance-based approach rather than instrument certifications, facilitating the deployment of advanced weather sensors. Today, heliports have a binary weather measurement system choice, expensive certified surface weather stations or a windsock. The standard has the potential to change this paradigm, by allowing the deployment of cost-effective digital sensor technology to reduce uncertainty about what is happening at a heliport or vertiport/vertiplex destination. Operationalizing this specification requires rigorous testing and collaboration through public-private partnerships. Bridging the weather educational gap is essential for enhancing safety in low-altitude aviation. Additionally, the integration of Digital Flight Rules (DFR) alongside the ASTM F3673 - 23 Weather Standard presents opportunities for modernizing air traffic management.
Before airplanes even reach the runway, pilots must file a plan to inform air traffic controllers where they’re going and the path they are going to take. When planes are in the air, however, that plan often changes. From turbulence causing passenger discomfort and additional fuel use to unexpected weather patterns blocking the original path, pilots have to think on the fly and inform air traffic controllers of any modifications to their routes.
A novel method which has the potential for improving the U.S. Navy's ability to perform continuous assurance on autonomous and other cyberphysical systems. Naval Postgraduate School, Monterey, CA Autonomous systems are poised to provide transformative benefits to society. Autonomous vehicles (AVs) have the potential to reduce the frequency and severity of collisions, enhance mobility for blind, disabled, and underage drivers, lower energy consumption and environmentally harmful emissions, and reduce population density in metropolitan regions. In civilian aviation, increasingly autonomous systems could mitigate two of the most costly features of human pilots: the cost associated with training and paying highly skilled operators, and the reduced efficiency incurred by flight time limitations and crew rest requirements. Additionally, autonomous air traffic management systems could reduce the cognitive burden on air traffic controllers by automating the monitoring and analysis of high volumes of data, alerting a human operator only when complex decisions must be made to mitigate risk. Within the power distribution industry, innovations in “micro-grid” technology can allow better utilization of alternate energy sources while decreasing vulnerability to failure compared to current centralized power distribution, but such decentralization necessitates highly adaptive autonomous systems to carefully synchronize energy production and consumption. Medical devices are currently designed to function for a large group of patients with similar conditions, but adaptive patient-specific algorithms could respond more effectively to individual patient needs, increasing lifespan and quality of life.
An Air Traffic Controller(ATC) is a person responsible for the proper Take-Off and Landing of an Aircraft from the runway, and for relaying continuous vital information back and forth from Pilots. The proposed ATC will automate this entire process to reduce human-generated errors and save costs. The entire system will be made using Artificial Intelligence and will use Natural Language Processing and Artificial Neural Networks to create a human-like, but a better-prepared system. The model needed to create the ATC, can be trained on already available crucial flight data. The data must include flight take-off and landing time, along with altered time based on weather, climate and other physical factors. The back-end system of the ATC, can be then made to work on this trained model, and produce correct and calculated flight path and timings for the take-off and Landing. The system will do an automatic Pre-flight checkup, based on weather and other clear-sky conditions, such as birds and overhead flights. If there are no problematic conditions, a flight can be allowed to take-off. Similarly, a flight can be allowed to land, based on a clear runaway and good weather conditions. Also, the system will use Artificial Neural Networks, to pan out an optimized flight path for the aircraft to follow, so as to reach a particular destination by avoiding extra air traffic, and saving fuel.The AI-inspired ATC will also be responsible, to tackle problems faced by pilots based on their requests, voice & mood conditions, which will be processed using a customized NLP Component. Implementing the proposed AI-inspired Air Traffic Controller can significantly reduce errors, save costs, and reduce the overhead of extra time in panic situations.
This SAE Aerospace Information Report (AIR) provides general information to aircraft engineers, regarding the types of Protective Breathing Equipment (PBE) configurations which are available, the intended functions of such equipment, and the technical approaches which may be used in accomplishing these functions. The term "PBE" or "Protective Breathing Equipment" has been used to refer to various types of equipment, which are used in a variety of applications. This way of using the terminology has been a source of confusion in the aviation industry. One objective of this AIR is to assist the reader in distinguishing between the types of PBE applications. A further objective is to assist in understanding the technical approaches which can be used in each of the major applications. Principles of PBE design are reviewed briefly. However, discussion of specific performance specifications and information regarding the details of manufacture and testing of such equipment is beyond the scope of this document.
A team of researchers at Carnegie Mellon University believe they have developed the first AI pilot that enables autonomous aircraft to navigate a crowded airspace. The artificial intelligence can safely avoid collisions, predict the intent of other aircraft, track aircraft and coordinate with their actions, and communicate over the radio with pilots and air traffic controllers. The researchers aim to develop the AI so the behaviors of their system will be indistinguishable from those of a human pilot.
Letter from the Guest Editors
The general English speech recognition is based on the techniques of n-grams where the words before and after are predicted and the utterance prediction is produced. At the same time, having a significantly lengthier n-gram has its own impact in training and the accuracy. Shorter n-grams require the utterances to be split and predicted than using the complete utterance. This article discusses specific techniques to address the specific problems in Air Traffic Speech, which is a medium length utterance domain. Moving from the adapted language models (LMs) to rescored LM, a combined technique of syntax analysis along with a deep learning model is proposed, which improves the overall accuracy. It is explained that this technique can help to adapt the proposed method for different contexts within the same domain and can be successful.
Unmanned aerial vehicles (UAVs) are envisioned to operate much closer to each other in low-altitude airspace than in the conventional high-altitude air traffic system and therefore impose challenges not only to the vehicle design but also to the development of a safe yet efficient low-altitude air traffic system. NASA Ames developed an air traffic simulation tool known as Flexible engine for Fast time evaluation of Flight environments (Fe3).
Rotorcrafts are generally subject to a higher fatal accident rate than other segments of aviation, including commercial and general aviation. The safety improvement for rotorcrafts would directly improve the efficiency of air traffic control, since rotorcrafts operate primarily within low-level airspace; an area that is becoming increasingly complex with new entrants, such as unmanned aircraft systems and urban air mobility. The recent impact of artificial intelligence and deep learning algorithms on various aspects of our lives has led to the investigation of the application of these algorithms in the aviation domain; as it may offer a prime opportunity to enhance safety within the aviation community. In this research, we explore the efficacy, reliability, and, more importantly, the explainability of modern deep learning algorithms. We use machine learning models to predict the attitude (pitch and yaw) of rotorcrafts using video data recorded with ordinary cameras. The cameras were mounted inside the helicopter cockpit and recorded outside view through windshield continually during the flight. We train four different architectures of convolutional neural networks (CNNs), i.e., VGG16, VGG19, ResNet50, and Xception. The models achieved 90%, 91%, 88%, and 88%, respectively, average attitude prediction accuracy on the test video dataset. Furthermore, we use gradient class activation maps (grad-CAM) to ascertain the features and regions of the image that influenced the model to make a specific prediction. We show that CNNs learn to focus on similar features as human operators (pilots), i.e., the natural horizon curve. Our findings demonstrate the feasibility of using deep learning models for attitude prediction from f light videos recorded using ordinary inexpensive cameras. The proposed video analytics framework provides a cost-effective means to supplement traditional Flight Data Recorders (FDR); a technology that is often beyond the financial reach of most general aviation rotorcraft operators.
This document specifies requirements for an Approach to Landing Guidance System (ALGS) electronic device. This equipment shall display relative aircraft position and situation information for flight along precision three-dimensional paths within the appropriate coverage area. The precision three-dimensional path may be an ILS straight-in look-alike path or a complex, curved path. The requirements are applicable to electronic devices capable of receiving signals or other information from one or more sources, including but not limited to ILS, GNSS, or IRU inputs.
Contemporary air traffic management (ATM) challenges are both (1) acute and (2) growing at rates far outpacing established ways for absorbing technological innovation. Lack of timely response will guarantee failure to meet demands. Immediately that creates a necessity to identify means of coping and judging new technologies based on possible speed of adoption. Paralleling the challenges are developments in capability, both recent and decades old. Some steps (e.g., Global Positioning System (GPS) backup) are well known and, in fact, should have progressed further long ago. Others (e.g., sharing raw measurements instead of position fixes) are equally well known and, if followed by further flight tests initiated (and successful) years ago, would have produced a wealth of in-flight experience by now if development had continued. Other possibilities (e.g., automated pilot override) are much less common and are considered largely experimental. This SAE EDGE™ Research Report is aimed at focusing industry attention on unsettled ATM issues and activities that appear most likely to offer solutions, starting with the near term and continuing on toward increasing versatility and confidence as experience accumulates. In general, the more familiar developments tend to suggest quicker acceptance of test trial initiation, while comparatively unexplored techniques call for a more gradual assimilation. Flexibility for growth is needed in any event, without the pervasive delays that have obstructed progress for so long. NOTE: SAE EDGE™ Research Reports are intended to identify and illuminate key issues in emerging, but still unsettled, technologies of interest to the mobility industry. The goal of SAE EDGE™ Research Reports is to stimulate discussion and work in the hope of promoting and speeding resolution of identified issues. SAE EDGE™ Research Reports are not intended to resolve the challenges they identify or close any topic to further scrutiny. Click here to access the full SAE EDGETM Research Report portfolio.
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