AI technology drives the transformation of F1 racing


This post was written by Will Owen, BD Associate, at Valkyrie.

A brief history of F1 data

Until the 1980s, cars were all mechanical. Computers were too large and slow to be useful on racing cars, so the driver was the only source of “data” for the racing engineer. As surprising as drivers are to “feel” the car, it is difficult for any driver to remember objective measurements of how the car performed in a session when they are busy focusing on driving.

Once the electronics got small enough, it started to become critical to car operating systems, such as fuel management and engine timing. As more and more sensors were connected to various systems of the car, More data has been collected on both the performance and reliability of the car. In the beginning, cars only stored a small amount of sensor data in the memory built into the car’s computer. Engineers may access it after the race but not during the race. As technology progressed, cars acquired the ability to send small amounts of data to the pitlane while on the track, paving the way for a new era in motorsports.

In the early 1990s, cars became completely dependent on computer processing to generate lap time and performance. Active suspension, traction control, power-assisted controls, and many other systems all required some kind of digital processing. Many of these driver assistance systems were banned not long after their implementation for various sporting and budgetary reasons. As technology continued to advance, teams were able to mount more and more types of sensors on the car to get a complete digital picture of how the car was performing on the track.

Where are we now

Today, the use of data is prolific in all areas of decision making in Formula 1 racing, with the exception of the driving aids integrated into the car. From car development to race strategy, the telemetry transmitted by the race car is invaluable in finding performance and getting results. To utilize the huge amount of data the car generates when racing on the track, the F1 teams have created their own portable IT infrastructure that supports the IT needs of their engineers during the race event. In addition to the track staff, the Formula 1 team bases house permanent engineering and data centers, where dozens of engineers work tirelessly on the real-time telemetry coming from the race car. Every bit of data collected when the car is on the track is critical to providing the engineers who built the car with feedback on the work done. Time is of the essence on the race weekend, as decisions need to be made quickly about which parts to use. Modern innovations like cloud computing and data science enable humans to make critical decisions from large amounts of data.

Automotive design is a highly technical frontier that requires the best computer scientists, racing engineers and physicists in the world all to work together to produce the highest performing and most elite racing car possible by the rules. Race teams now program custom software to assist in car design. The process of developing a racing car looks very different today than it did in the past and now depends on computer-aided design (CAD) to identify improvements with the utmost precision. Specifically, the teams use computational fluid dynamics (CFD) to simulate the aerodynamics of their cars with different configurations and parts. All of these techniques require robust data systems that can handle the computing power required for the design.

Running to push boundaries

Formula 1 will continue to push the boundaries for all motorsports when it comes to using data to improve performance. All racing teams, but especially those in Formula 1, they must constantly innovate their methods to keep up with their competitors. As budget restrictions are increasingly imposed on teams to make the sport fairer, Formula 1 teams will have to rely more on simulations to test their new cars and subcomponents. The simulations are built on racing car models that allow engineers to “drive” the car in the computer based on certain parameters, resulting in data generated in the same format as the real race car. Building effective simulations depends on the availability of accurate models of the car’s performance in the real world and how external factors affect the car’s performance. Teams will need to experiment with new methods to simulate cars with greater accuracy, and these will undoubtedly involve both the powers of artificial intelligence (AI) and machine learning (ML) to access a level of detail beyond human engineers.

Because teams are on a tight budget, it simply isn’t possible to hire enough engineers to comprehensively review all sensor data that comes from racing cars. Current AI capabilities help process large amounts of data and highlight areas where human engineers may seek performance improvements. The next generation of artificial intelligence techniques integrated into racing will play a leading role in the configuration and design decisions of the car that will produce the best results on the track.

The complexity of the race environment will be a real test bed for collaboration between human engineers and artificial intelligence. Getting the right information about the car’s performance from the data takes more than just processing the sensors. Data-driven racing requires a deep understanding of how the racing environment works and what trade-offs are acceptable for all other elements of racing beyond pure performance. Engineers informing AI systems will need to become more “aware” of the context in which cars operate. Otherwise, they will always rely on the brains of racing engineers.

This story originally appeared on Copyright 2021


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