Federal employees say that flying is still safe despite the strain on air traffic controllers. But expect even more airport delays ahead. It hasn't been a good year for federal aviation safety workers. January saw the worst US commercial airline disaster in decades, quickly followed by sudden layoffs, staffing shortfalls, major technology glitches at one of the nation's busiest airports, and short timelines to rebuild the systems that govern national airspace. It somehow got worse this month, when a stalemate between congressional Republicans and Democrats led to a government shutdown.

Understanding how air traffic controllers construct a mental 'picture' of complex air traffic situations is crucial but remains a challenge due to the inherently intricate, high-dimensional interactions between aircraft, pilots, and controllers. Previous work on modeling the strategies of air traffic controllers and their mental image of traffic situations often centers on specific air traffic control tasks or pairwise interactions between aircraft, neglecting to capture the comprehensive dynamics of an air traffic situation. To address this issue, we propose a machine learning-based framework for explaining air traffic situations. Specifically, we employ a Transformer-based multi-agent trajectory model that encapsulates both the spatio-temporal movement of aircraft and social interaction between them. By deriving attention scores from the model, we can quantify the influence of individual aircraft on overall traffic dynamics. This provides explainable insights into how air traffic controllers perceive and understand the traffic situation. Trained on real-world air traffic surveillance data collected from the terminal airspace around Incheon International Airport in South Korea, our framework effectively explicates air traffic situations. This could potentially support and enhance the decision-making and situational awareness of air traffic controllers.

One indicator of well-being can be the person's subjective time perception. In our project ChronoPilot, we aim to develop a device that modulates human subjective time perception. In this study, we present a method to automatically assess the subjective time perception of air traffic controllers, a group often faced with demanding conditions, using their physiological data and eleven state-of-the-art machine learning classifiers. The physiological data consist of photoplethysmogram, electrodermal activity, and temperature data. We find that the support vector classifier works best with an accuracy of 79 % and electrodermal activity provides the most descriptive biomarker. These findings are an important step towards closing the feedback loop of our ChronoPilot-device to automatically modulate the user's subjective time perception. This technological advancement may promise improvements in task management, stress reduction, and overall productivity in high-stakes professions.

Los Angeles, California – The United States military shot down a flurry of objects this month: a large object it identified as a Chinese surveillance balloon followed by three smaller objects that the government said might be "benign". The airborne objects were drifting through airspace increasingly crowded with commercial and amateur balloons, drones and possible aerial surveillance craft belonging to adversaries. Their rising numbers pose a challenge to aviators and government agencies. Experts say that while heavy commercial balloons must meet strict Federal Aviation Administration (FAA) regulations, lighter amateur balloons are exempt from most rules, and the FAA might not be able to track them. Military and intelligence officials found no evidence that the three smaller objects were conducting surveillance for another country, and they were not sending communication signals, National Security Council spokesman John Kirby said at a White House briefing on Monday.

Today’s air traffic management (ATM) system evolves around the air traffic controllers and pilots. This human-centered design made air traffic remarkably safe in the past. However, with the increase in flights and the variety of aircraft using European airspace, it is reaching its limits. It poses significant problems such as congestion, deterioration of flight safety, greater costs, more delays, and higher emissions. Transforming the ATM into the “next generation” requires complex human-integrated systems that provide better abstraction of airspace and create situational awareness, as described in the literature for this problem. This paper makes the following contributions: (a) It outlines the complexity of the problem. (b) It introduces a digital assistance system to detect conflicts in air traffic by systematically analyzing aircraft surveillance data to provide air traffic controllers with better situational awareness. For this purpose, long short-term memory (LSTMs) networks, which are a popular version of recurrent neural networks (RNNs) are used to determine whether their temporal dynamic behavior is capable of reliably monitoring air traffic and classifying error patterns. (c) Large-scale, realistic air traffic models with several thousand flights containing air traffic conflicts are used to create a parameterized airspace abstraction to train several variations of LSTM networks. The applied networks are based on a 20–10–1 architecture while using leaky ReLU and sigmoid activation function. For the learning process, the binary cross-entropy loss function and the adaptive moment estimation (ADAM) optimizer are applied with different learning rates and batch sizes over ten epochs. (d) Numerical results and achievements by using LSTM networks to predict various weather events, cyberattacks, emergency situations and human factors are presented.

An operator controls a Fetch driverless car from the office of Imperium Drive, during driverless car trials, in Milton Keynes, Britain, June 8, 2022. MILTON KEYNES, England (Reuters) -Autonomous vehicle (AV) startups have raised tens of billions of dollars based on promises to develop truly self-driving cars, but industry executives and experts say remote human supervisors may be needed permanently to help robot drivers in trouble. The central premise of autonomous vehicles – that computers and artificial intelligence will dramatically reduce accidents caused by human error – has driven much of the research and investment. But there is a catch: Making robot cars that can drive more safely than people is immensely tough because self-driving software systems simply lack humans' ability to predict and assess risk quickly, especially when encountering unexpected incidents or "edge cases." "Well, my question would be, 'Why?'" said Kyle Vogt, CEO of Cruise, a unit of General Motors (NYSE:GM), when asked if he could see a point where remote human overseers should be removed from operations.

On July 13, Boston's Merlin Labs announced that it would be working with the US Air Force to add autonomy to the C-130J Super Hercules cargo transport plane. Merlin's technology is a kind of advanced auto-copilot, designed to take over the responsibilities of one crew member in flight while being supervised by a human pilot. If the technology delivers as promised, it will allow planes that normally fly with two human pilots to operate with just one, and could even allow single-seater planes to fly fully autonomously. The same day that Merlin announced its partnership with the Air Force, it also announced a second round of $105 million in funding, which combined with a first round means the company has $130 million of runway to develop its technologies. This funding, says Merlin Labs CEO Matthew George, will help the company continue to develop "the world's most capable, safest and flexible pilot, that will eventually enable very large aircraft to fly with reduced crew and small aircraft to fly totally uncrewed."

Israeli startup Airwayz Drones Ltd., set up by veterans of the Israeli airforce, has developed software that knows how to safely steer hundreds of drones in the same airspace, orchestrating them in the sky autonomously, just as a traditional human-manned air traffic control station would. The technology of the Israeli company Airwayz managed some 20 drones from five companies simultaneously on Wednesday in the sky over an unpopulated area of the northern coastal city of Hadera. It was the first stage of a two-year initiative that is being touted by the Israel Innovation Authority and its partners in the event as one of the largest drone experiments ever conducted in the world. "This is one of the most progressive experiments in the world, in which drones from many companies are flying in a open and not controlled area," said Daniella Partem, head of the Center for the Fourth Industrial Revolution at the Israel Innovation Authority, which is in charge of fostering the nation's tech ecosystem. Get The Start-Up Israel's Daily Start-Up by email and never miss our top stories Free Sign Up The purpose of the large-scale government-backed experiment is to understand what our skies will look like in the future, as hundreds and thousands of drones pepper our firmament to meet various needs -- online deliveries, photography, security, agriculture and more.

Air traffic control is becoming a more and more complex task due to the increasing number of aircraft. Current air traffic control methods are not suitable for managing this increased traffic. Autonomous air traffic control is deemed a promising alternative. In this paper an air traffic control model is presented that guides an arbitrary number of aircraft across a three-dimensional, unstructured airspace while avoiding conflicts and collisions. This is done utilizing the power of graph based deep learning approaches. These approaches offer significant advantages over current approaches to this task, such as invariance to the input ordering of aircraft and the ability to easily cope with a varying number of aircraft. Results acquired using these approaches show that the air traffic control model performs well on realistic traffic densities; it is capable of managing the airspace by avoiding 100% of potential collisions and preventing 89.8% of potential conflicts.

In the fast-changing digital milieu, people like brands to treat them like humans and not merely as transactions.By Ajit Kumar and Vidhya Visweswarababu Imagine walking into a room of strangers, friends and family, expressing your intent to start an exciting new initiative and asking them for an investment. The strangers will likely ask you questions and make an investment if they see a benefit. Friends on the other hand feel happy for you knowing how hard you worked for it and may proceed to make an investment if there is mutual benefit or because you're a loyal friend. Family on the hand, knowing your personality, capabilities, dreams, desires and aspirations may choose to inquire, advice and collaborate to drive you to the right outcome. As a brand, your engagement with your customer might feel like a stranger, friend or family.