“And fundamentally, you guys have things that roll and we have things that rotate, and you have things that get hot and cold, and so do we,” Maddox said. “When you get down to the actual measurement level, there are a lot of similarities between the stuff that you guys use to understand vehicle performance and the stuff we use to understand vehicle performance.”
Not just Mars
Other speakers pointed to areas of technology development—like tire development—that you may have read about recently here on Ars Technica. “[A tire is] a composite material made with more than 200 components with very non-linear behavior. It’s pressure-sensitive, it’s temperature-sensitive. It changes with wear… and actually, the ground interaction is also one of the worst mechanisms to try to anticipate and to understand,” said Phillippe Tramond, head of research of motorsport at Michelin.
For the past four years, Michelin has been crunching data gathered from cars racing on its rubber (and the other 199 components). “And eventually, we are able to build and develop a thermomechanical tire model able to mimic and simulate tire behavior, tire performance, whatever the specification is,” Tramond said.
That tool has been quite valuable to the teams racing in the GTP class of hybrid prototypes, as it means that their driver-in-the-loop simulators are now even more faithful to real life. But Michelin has also started using the tire model when developing road tires for specific cars with individual OEMs.
For Sid Siddhartha, a principal researcher at Microsoft Research, the data is again the draw. Siddhartha has been using AI to study human behavior, including in the game Rocket League. “We were able to actually show that we can really understand and home in on individual human behavior in a very granular way, to the point where if I just observe you for two or three seconds, or if I look at some of your games, I can tell you who played it,” Siddhartha said.
