RAeS Article: AI, Autonomy, and Human-Machine Teaming Become Rising Forces of Change in Aviation Written 1 November 2023

By Shawn Weil, Chief Growth Officer, Aptima, and Member, AIAA SciTech Forum Guiding Coalition; and Scott Fouse, Aerospace R&D Domain Lead and AIAA SciTech Forum Executive Producer, AIAA

Originally published in the November issue of RAeS AEROSPACE

Aviation is where artificial intelligence (AI) and human-machine teaming with autonomy has become mainstream, beginning initially as assistance for fighter jets in terrain avoidance in the 1980s. Today we see it in commercial jet operations with automatic co-pilots.

Now it’s time to expand AI-powered machine-learning autonomy systems in aviation. There’s an opportunity to focus on applications training to help guide the human-machine teaming for improving aircraft performance and reducing pilot risk.

Engineers are tweaking the relationship between pilot and AI to address concerns about autonomy in a more complex aviation system. The early models of autonomy in aviation have given way to a much more nuanced and complex view of autonomy, where tradeoffs can be made in different ways for different circumstances.

While that autonomy work is still in its infancy, the development of vehicles with autonomous system operations actually dates back to the 1940s in the automobile industry. A blind automotive engineer invented cruise control, an autonomous system that began the process of a broader, more adaptive autonomous system for automobiles. Today, fully autonomous taxi cabs are roaming the streets of San Francisco, aggregating data and updating machine learning capabilities as they transport fares across the city.

Aviation engineers are taking a cue from the automotive industry and exploring the lessons learned about the process of AI-powered autonomy. How should it be implemented in a plane? How should a pilot interface with it? How can a pilot know and anticipate what the autonomy is actually in control of and what the pilot is responsible for?

To address those questions, engineers have honed their systems interfaces, re-designed workflow, and created other externalized cognition tools built on data collection and aggregation. Ultimately what engineers want to create is an autonomous system where they have both high levels of automation and high levels of control. They want to make autonomy a full-fledged member of the flight team that is in some sense omniscient because it’s receiving information from more sensors than the human partner could.

AI won’t replace people in the cockpit. But it may, in fact, amplify their efforts.

Meanwhile, aviation engineers are trying out new ways of using autonomy. One example the U.S. Air Force (USAF) is exploring is the concept of the automated wingman. Here, a piloted aircraft might be flying with two or three autonomous aircraft around it, anticipating the pilot’s next move. These drones are not just reacting to the pilot’s commands, but they’re reacting to the pilot’s intent. They can anticipate the pilot’s actions. The USAF is currently ramping up plans for using 1,000 autonomous drones to assist jetfighters, calling them collaborative combat aircraft.

Aviation engineers are beginning to understand that perhaps the whole science of human autonomy interaction from a cognitive systems point of view has to be rethought. Training should be created to help the pilots know more than just how to fly the aircraft, but also how to manage, understand, and anticipate the autonomous system.

Now the real work in AI-powered machine-learning autonomy for aviation begins. Learn more during the 2024 AIAA SciTech Forum, 8–12 January, Orlando, Florida. A number of panel discussions and technical papers presented throughout the forum will help the aviation industry move the autonomy and human-machine teaming work forward.