Check out all the on-demand sessions from the Intelligent Security Summit here.
As airline passengers slog through a summer of rampant flight delays and cancellations, airlines are grappling with a massive post-pandemic increase in air travel demand and a long-term pilot shortage – while also prioritizing safety. Aerospace leader Airbus is betting autonomous and AI-driven commercial flight functions can bridge that gap.
Wayfinder, a research project within Acubed, the Silicon Valley innovation center of Airbus, is developing autonomous flight and machine learning solutions for the next generation of aircraft. Its core mission is to build a “scalable, certifiable autonomy system capable of powering a range of self-piloted aircraft applications in single pilot operations.”
“Industry estimates expect passenger volume to grow from pre-COVID levels of 4 billion passengers a year to 8 billion – a little more than the current world population – in about 15-20 years,” Airbus’ Wayfinder project executive, Arne Stoschek, told VentureBeat. “It’s a massive, massive scaling topic.”
This means there will be more aircraft flying and more flights to manage, he explained, while at the same time Airbus and the commercial aviation industry need to keep increasing safety — which they say is a top priority — in mind.
Intelligent Security Summit On-Demand
Learn the critical role of AI & ML in cybersecurity and industry specific case studies. Watch on-demand sessions today.
Autonomous functions to increase safety on Airbus aircraft
Wayfinder’s immediate goal, said Stoschek, is to develop autonomous functions to make aircraft operations safer – which can include supporting the pilot to better understand the environment and come to the right decisions. There is also ongoing discussion around single-pilot operation, meaning reducing the crew from several pilots to one, which would also mean a shift in pilot responsibility.
“Autonomy is not a goal per se,” he said. “The main goal that we consider in the framework of technology is operational safety.”
The history of aviation, he added, is a continuum of automation – the current generation of aircraft is already highly automated and “each of those steps has substantially contributed to safety. This is the next big step.”
Amid a pilot shortage that is forcing airlines to reduce the number of flights and raise the retirement age, Airbus’ autonomous flight efforts are already moving toward the goal of single-pilot operations. Last year, Acubed began flights in California to advance autonomous technology that will make the next redesigned narrow body aircraft capable of single-pilot operation.
“We certainly believe that the next generation of single-aisle aircraft will be single-pilot capable,” former Acubed executive, Mark Cousin, told FlightGlobal in 2020, noting that any single-pilot commercial aircraft will need advanced autonomous systems capable of taking over and landing should the pilot become incapacitated.
Gathering aircraft data is a challenge
One of the biggest challenges Wayfinder faces, said Stoschek, is dealing with the complexity of aircraft data. It requires scaling the data to reflect all types of conditions – including takeoff, landing, daytime, nighttime, snow storms and several thousands of different airports.
“AI and machine learning technologies have to promise to provide this type of robustness, so the key is to have training and testing data,” said Stoschek.
In an autonomous car, that is fairly straightforward with human drivers, or by purchasing high-quality annotated data. For large commercial aircraft, however, it’s a different story.
“There are many steps you need to take in order to operate in a safe way,” he said. “So a lot of it is building the groundwork to get the ball rolling. We spend a lot of time collecting relevant data, determining what is relevant data and processing data in terms of safety certification.”
In a blog post, Stoschek described Wayfinder’s goal this way: “We plan to observe the work that experienced pilots are doing every day, and then aggregate this data on a massive scale to train our machine learning models. As a rule, when a pilot retires, their experience is lost forever, but with our model, their contributions will endure. The historical data we gain from them will always be accessible to our AI systems, contributing to their continual learning and improvement. With this approach, we believe our AI system can eventually match the abilities of human pilots, bringing to life the business case of urban mobility and easing the strain put on pilots already in the field.”
Airbus seeks commercial viability of autonomous solutions
As Wayfinder continues to advance its ML and autonomous software solutions, Stoschek says its technologies will likely be commercially viable in the not-too-distant future.
In 2020, he explained, Wayfinder had the opportunity to apply its ML and autonomy technologies developed in an Airbus technology demonstrator for autonomous taxi takeoff and landing.
“Since then, we have been focusing on the next steps beyond a technology demonstrator and towards a commercially viable solution, which includes aspects of scalability and the demonstration of safety,” he said. “This is a big undertaking that requires tens of petabytes of global data and encompasses all schemes and modes of operations that an aircraft is experiencing.”
Wayfinder developed the processes and tools to create simulation runs in the order of millions, continuously improving and expanding its data, as well as testing its software against many scenarios so that it can expand the technology to any airport Airbus’ aircraft fly into, under all conditions the aircraft can experience.
Recent achievements, Stoschek added, include developing a new framework for assessing the safety and robustness of ML models that he expects significantly to improve the certification process. It received support, he said, both within and outside Airbus.
“Our detection models also achieved very high levels of accuracy while being an order of magnitude smaller and faster than standard detection models in the industry,” he said. “Having compact and fast detection models is an important consideration for product realizations to run on on-aircraft computers.”
Wayfinder is also setting up toolchains that will help rapidly move algorithm development to hardware-software implementation in a physical lab to hardware-software implementation on an actual aircraft, he explained.
“The focus is to create processes that utilize the advantages of agile software development while ensuring stringent development and testing procedures mandated in our industry are followed” he said.
From proof-of-concept to product-ready
The bottom line, says Stoschek, is that the goal is to move from a proof of concept, where Wayfinder shows AI applicable for a couple of test flights, to the realization that it’s really product-ready.
“That means it works for a global rollout. It works with the aircraft systems. It’s safe. It gets signed off by the regulatory bodies and is integrated into a product,” he said. “Once those types of core technologies are resolved, there’s a whole new world of applications and opportunities you can put on top of that. The availability of technology is just a first step in transforming the industry. That is the holy grail.”
VentureBeat's mission is to be a digital town square for technical decision-makers to gain knowledge about transformative enterprise technology and transact. Discover our Briefings.