Co-creating the future of air traffic management
LFV explores AI-driven autonomous air traffic control
Airport and airplanes from above

In 2017, a Tesla passed aviation engineer Robin Hughes. Knowing that the innovative company was testing self-driving vehicles, Hughes thought to himself, “If autonomous cars are feasible in a chaotic environment, could autonomous air traffic control be feasible in a controlled environment?” Insatiably curious, Hughes set out to find the answer.

To safely expedite the flow of traffic in an assigned airspace, air traffic controllers use radar to monitor aircrafts’ location and communicate with pilots by radio. To prevent collisions, controllers enforce separation rules that ensure each aircraft maintains a minimum amount of empty space around it. Air traffic controllers have a highly stressful job with little room for error. As the demand for air travel has steadily increased, controllers have had to handle a greater capacity of flights. For years, the aviation industry has sought to increase efficiencies in air traffic management to alleviate the strain on air traffic controllers and reduce related costs.

As the Head of Engineering at Luftfartsverket (LFV), a Swedish air navigation service provider, Hughes had all of this swirling in his mind, along with memories of watching the IBM Watson® computer defeat top contestants on the quiz show Jeopardy!. He thought, “If IBM can use AI to do that, they can surely help me with this.” A few months later, a team from the IBM Garage™—a framework for digital transformation—was at the LFV offices conducting technical discovery and architecture workshops. Through those workshops, the joint team validated that the concept of autonomous air traffic control should indeed be possible.

Increased capacity


Solution maintains separation in air traffic control simulations at approximately 200% the normal capacity

Increased power


In a single second, the app can run nearly 800 alternate conflict resolutions

The IBM Garage has been a direct and efficient route in setting the project up, running the project and gaining access to the skillsets that reside in IBM. Robin Hughes Head of Engineering Luftfartsverket
Airspace as a 3D chessboard

While LFV engineers worked on funding and data collection, IBM Garage experts from the Copenhagen, Denmark location pulled in the IBM Research® team and developers for the IBM® Streams analytics platform. The expanded LFV and IBM team collaborated during an IBM Enterprise Design Thinking Workshop™. Because the IBM Garage Methodology focuses on user-centered design, LFV included two air traffic controllers who provided insight about factors such as aircraft limitations, fuel load and pilot cooperation.

With a solid vision and technical plan, the team was ready to begin developing Advanced Autoplanner (AAP), an AI-driven autonomous air traffic control solution financed by the Swedish Transport Administration. But COVID-19 hit and the Nordic countries went into lockdown. Disruption is in the DNA of the agile IBM Garage Methodology, so the project remained on track. Team members across Europe and the US maintained a regular cadence of agile stand-ups, playbacks and technical status calls, leading to successful development of the first AAP minimum viable product (MVP).

Aviation standards require five nautical miles around each in-flight aircraft at all times. When building AAP, the LFV and IBM Garage team included a buffer, requiring six nautical miles. If planes are closer than this, it’s called a “loss of separation,” which—over time—can result in a collision. AAP operates in two phases as it oversees a specified airspace sector. First, a lattice-based 3D space exploration technique continuously tracks and forecasts aircraft locations in real time. If the app determines that a plane will experience a loss of separation, it is able to run nearly 800 possible scenarios—in one second—of slightly altering a plane’s direction, speed or altitude. AAP looks at how a scenario’s trajectory would affect the entire sector’s airspace, like a 3D chessboard, and then identifies safe actions that avoid future conflicts.

Second, the solution uses a rule-based approach to rank the actions identified in phase one and sends the best option to the pilot. The pilot can execute the instruction or communicate that it is not possible. For example, if the instruction is to increase the aircraft’s altitude by 1,000 feet and the pilot determines this is not feasible, the app would then provide an alternate instruction, such as adjust course five degrees east. AAP also tracks when the aircraft is safe to resume its original flight plan.

Forced to rethink AI development

Surprisingly, the pandemic positively impacted a vital aspect of the project. Because LFV controls both civil and military air traffic in Sweden, its data is highly secure and therefore inaccessible offsite. During lockdown, the extended team was no longer permitted onsite to access data needed to build the AAP solution’s AI model. Forced to rethink AI development, the group decided to build an AI model based on integration with an NLR (Dutch National Aviation and Aerospace Laboratory) Air Traffic Control Research Simulator (NARSIM).

Data scientists built a deterministic AI model with a custom algorithm on the IBM Streams analytics platform and then opened a connection between the NARSIM simulator and IBM Streams running on IBM Cloud®. They simulated traffic across the determined airspace, and based on the output, continued to refine the algorithm and iterate the AI model. An IBM Db2® database stores data for the IBM Streams application and an IBM Cloudant® database stores the AAP solution’s instructions for the air traffic controller and pilot.

Success at 200% typical capacity

LFV and IBM co-created the first AAP MVP in only four months, which impressed Hughes. “No one believes me when I tell them we did this in about 17 weeks. I mean, normally our development times are somewhere between two and five years.”

The depth, breadth and pace of work completed is a testament to the dedication of the LFV and IBM team members and the effectiveness of the IBM Garage approach. Hughes states, “The IBM Garage has been a direct and efficient route in setting the project up, running the project and gaining access to the skillsets that reside in IBM.” He was especially excited about the methodology’s ability to build, test and validate the riskiest components of the application before moving to next steps.

Safety will always be the aviation industry’s priority. So while development of innovative concepts such as autonomous air traffic control is exciting to see on simulators, implementation in the real world will take time and evolve incrementally. Future iterations of AAP will account for weather conditions and no-fly zones and incorporate non-deterministic AI and machine learning capabilities. LFV also plans to expand from the Swedish airspace to other parts of Europe, and European working groups are eager to get involved. AAP continues to run successful simulations at approximately 200% the normal capacity.

The solution is on track to alleviate some of the burden on air traffic controllers and improve efficiencies across airspaces. Decades from now, automated air traffic management may be standard. We’ll marvel at how stressful it once was and how one engineer’s insatiable curiosity transformed an industry.

Want to transform your business? Talk to an IBM Garage expert.

Luftfartsverket logo
About Luftfartsverket

Headquartered in Norrköping, Sweden, LFV (link resides outside of provides air traffic control and associated services for civil and military aviation in Sweden. During normal situations (pre-pandemic), LFV air traffic controllers manage approximately 2,000 aircraft daily within the Swedish airspace. LFV employs 1,100 people and has an annual turnover of SEK 3.1 billion.

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