The next generation of AI in racing will play a prominent role in car setup and design decisions that yield the best results on track.
This post was written by Will Owen, BD Associate, at Valkyrie. Until the 1980s, cars were all mechanical. Computers were too large and slow to be useful on race cars, so the driver was the only source of “data” for the racing engineer. As amazing as drivers are at “feeling” the car, it’s tough for any driver to recall objective measurements about how the car performed in a session when they are busy focusing on driving. Once electronics became small enough, they started becoming critical to operating car systems, such as fuel management and engine timing. As more and more sensors were attached to various systems of the car, more data was gathered on both car performance and reliability. At first, cars stored only a small amount of sensor data in the memory built into the car’s computer. Engineers could access it after the race but not during it. As technology progressed, cars gained the ability to send small amounts of data back to the pitlane while they were on track, paving the way for a new era in motorsports. By 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 more systems all required some kind of digital processing. Many of these driver aid systems were banned not long after they were implemented for various sporting and budgetary reasons. As technology continued to progress, teams were able to mount more and more types of sensors on the car in order to get a complete digital picture of how the car was performing on the track. Nowadays, the use of data is prolific in all areas of decision-making in Formula 1 racing, except for driver aids built into the car. From car development to race strategy, the telemetry that is broadcast from the race car is invaluable to finding performance and achieving results.
