Cars

DAYTONA BEACH—Last week, ahead of the annual Rolex 24 at Daytona and the start of the North American road racing season, IMSA (the sport’s organizers) held a tech symposium across the road from the vast speedway at Embry-Riddle University. Last year, panelists, including Crowdstrike’s CSO, explained the draw of racing to their employers; this time, organizations represented included NASA, Michelin, AMD, and Microsoft. And while they were all there to talk about racing, it seems everyone was also there to talk about simulation and AI.

I’ve long maintained that endurance racing, where grids of prototypes and road car-based racers compete over long durations—24 hours, for example—is the most relevant form of motorsport, the one that makes road cars better. Formula 1 has budgets and an audience to dwarf all others, and there’s no doubt about the level of talent and commitment required to triumph in that arena. The Indy 500 might have more history. And rallying looks like the hardest challenge for both humans and machines.

But your car owes its disc brakes to endurance racing, plus its dual-clutch transmission, if it’s one of the increasing number of cars fitted with such. But let’s not overblow it. Over the years, budgets have had to be reined in for the health of the sport. That—plus a desire for parity among the teams so that no one clever idea runs away with the series—means there are plenty of spec or controlled components on a current endurance racer. Direct technology transfer, then, happens less and less often—at least in terms of new mechanical bits or bobs you might find inside your next car.

Software has become a new competitive advantage for the teams that race hybrid sports prototypes from Acura, BMW, Cadillac, Porsche, and Lamborghini, just as it is between teams in Formula E.

But this year’s symposium shone a light on a different area of tech transfer, where Microsoft or NASA can use the vast streams of data that pour out of a 60-car, 24-hour race to build more accurate simulations and AI tools—maybe even ones that will babysit a crewed mission to Mars.

Sorry, did you say Mars?

“Critically, it takes light 20 minutes to make that trip, which has some really unfortunate operational impacts,” said Ian Maddox of NASA’s Marshall Space Flight Center’s Habitation office. A 40-minute delay between asking a question and getting an answer wouldn’t work for a team trying to win the Rolex 24, and “it certainly isn’t going to work for us,” he said.

“And so we’re placed in—I’ll be frank—the really uncomfortable position of having to figure out how to build AI tools to help the crew on board a Mars ship diagnose and respond to their own problems. So to be their own crew, to be their own engineering teams, at least for the subset of problems that can get really bad in the course of 45 minutes to an hour,” Maddox said.

Building those kinds of tools will require a “giant bucket of really good data,” Maddox said, “and that’s why we’ve come to IMSA.”

Individually, the hybrid prototypes and GT cars in an IMSA race are obviously far less complicated than a Mars-bound spacecraft. But when you get that data from all the cars in the race together, the size starts to become comparable.

“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.

That led to a new approach by the Alpine F1 team, which wanted to use Siddhartha’s AI to improve its simulation tools. F1 teams will run entirely virtual simulations on upgraded cars long before they fire those changes up in the big simulator and let their human drivers have a go (as described above). In Alpine’s case, they wanted something more realistic than a lap time simulator that just assumed perfect behavior.

The dreaded BoP

“Eventually, we are connected to IMSA, and IMSA is interested in a whole host of questions that are very interesting to us at Microsoft Research,” Siddhartha said. “They’re interested in what are the limits of driver and car? How do you balance that performance across different classes? How do you anticipate what might happen when people make different strategic decisions during the race? And how do you communicate all of this to a fan base, which has really blown me away, as John was saying, who are interested in following the sport and understanding what’s going on.”

“Sports car racing is inherently complex,” said Matt Kurdock, IMSA’s managing director of engineering. “We’ve got four different classes. We have, in each car, four different drivers. And IMSA’s challenge is to extract from this race data that’s being collected and figure out how to get an appropriate balance so that manufacturers stay engaged in the sport,” Kurdock said.

IMSA has the cars put through wind tunnels and runs CFD simulations on them as well. “We then plug all this information into one of Michelin’s tools, which is their canopy vehicle dynamic simulation, which runs in the cloud, and from this, we start generating a picture of where we believe the optimized performance of each platform is,” Kurdock said.

That’s something to think about the next time your favorite team gets the short end of the stick in the latest balance of performance—better known as BoP—update.