Browse Topic: Air traffic control
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.
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.
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.
Currently the VTOL world sees a high number of players investing in electrification, especially coming from the lower end of the market: like commercial drones and startups. Electric engines allow for new architectures and configurations, and partially simplify the design. Simplifying design, lowering cost of development and maintenance, has led to imagine the use of the eVTOL in the passengers' air transportation market, currently reserved to VIP and high net worth individuals. The shift is potentially so radical that a new name has been coined: Urban Air Mobility. This new market is widely imagined as a radical change compared to the current situation: with more traffic, simplified procedures for boarding and in some cases the use of unmanned or remotely piloted vehicles. This leads to the conclusion that the whole transportation system architecture will have to be upgraded or modified to allow for this to happen. Many projects are already running in this direction regarding specific topics, like SESAR JU [1] for Air Traffic Management. This paper treats the Ground Infrastructure, what is commonly referred as Heliport. In the paper a novel architecture envisioned for Urban Air Mobility is proposed and analyzed.
Convective weather systems, i.e., thunderstorms, are the leading cause of flight delay in U.S. airspace. Airline dispatchers must file their flight plans 1 to 2 hours before takeoff, and are often required to incorporate large buffers to forecast weather. Weather changes as flights progress, and airline dispatchers, Federal Aviation Administration (FAA) traffic managers, and air traffic controllers are especially busy during weather events. Workable opportunities for more efficient routes around bad weather are often missed, and automation does not exist to help operators determine when weather avoidance routes have become stale and could be updated to reduce delay.
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