air traffic controller
All six passengers presumed dead after plane went missing off US coast
Six people aboard a medical transport aircraft that went missing on Saturday while it was travelling from Bermuda to Boston are presumed dead, according to a statement from the flight's operator. On Sunday evening, the United States Coast Guard said it was ending its search for survivors off the coast of Nantucket, a Massachusetts island. We must now face the heartbreaking reality that the four members of our Latitude team, our patient and the patient's family escort, who were aboard Heartbeat 104, are presumed to have died, Latitude Air Ambulance said in a statement shortly thereafter. Our thoughts are first and foremost with their families, loved ones, friends, and colleagues. The Federal Aviation Administration lost contact with the Gulfstream G-100 plane just after 01:00 local time (05:00 GMT) on Saturday morning.
Furry Airline Pilots Are Just Minding Their Own Business
"We're furries at home, and we're airline pilots at work, and these are two separate things," says one member of the community in response to senator Tommy Tuberville's recent rant. The greatest threat to humanity's survival isn't climate change, nor is it AI . That seemed to be the takeaway from a bizarre speech made last week by Alabama Republican Tommy Tuberville on the Senate floor last week. During a lengthy anti-trans rant about "gender confusion," Tuberville started pontificating on the furry fandom, a subculture consisting of people who enjoy drawing and dressing as anthropomorphized animals. "Many airline pilots, flight attendants, and air traffic controllers are part of the furry community," Tuberville alleged.
The Shutdown Is Pushing Air Safety Workers to the Limit
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.
Learning to Explain Air Traffic Situation
Chai, Hong-ah, Yoon, Seokbin, Lee, Keumjin
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.
Automatic Classification of Subjective Time Perception Using Multi-modal Physiological Data of Air Traffic Controllers
Aust, Till, Balta, Eirini, Vatakis, Argiro, Hamann, Heiko
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.
Balloons, 'objects' – what's in the sky above the US?
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.
Augmented Air Traffic Control System--Artificial Intelligence as Digital Assistance System to Predict Air Traffic Conflicts
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.
Truly autonomous cars may be impossible without helpful human touch
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.
The Air Force plans to test an AI copilot on its cargo planes
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."