The new rules have been extremely attractive to carmakers. In addition to causing Honda to reconsider its exit, Ford is also coming back (developing the hybrid system for Red Bull Powertrains), and both Audi and Cadillac are also entering the sport, although the American brand won’t have its own engines ready until 2028.
Audi and Cadillac will both count as new engine suppliers, so they are allowed some extra development resources. However, Honda is counted as an existing manufacturer and doesn’t get any special treatment.
When I asked Watanabe how the work was progressing, he said, “Not so easy. We are struggling. Now we are trying our best to show the result next year,” he said. “Everything is new. [The] motor is new, [developing] 350 kW—it’s a very compact one that we need. And also the lightweight battery is not so easy to develop. Also the small engine with big power. So everything is very difficult, but we try our best.”
Getting it right will be vital—although Aston Martin now has the advantage of legendary designer Adrian Newey among its staff. Newey is on record saying that the 2026 rules have a “big chance” of being an engine formula, where each car’s aerodynamics are far less important, unlike today’s situation.
Trickle-down
OEMs go racing to raise their profile and sell more cars, but they also do it as a way to learn how to make their products better. Honda and HRC are no exception to that. But concrete examples of technology transfer from track to road are rare these days—it’s more about cross-pollination between engineers.
“There is a group within Honda that shares technical information yearly. It’s not just the racing; it’s all across Honda, so I think there’s been some interest in the technology and software we’ve developed,” Fu said. “Whether it trickles down to road cars… it’s a big jump from a race car to road cars, but I think some of the fundamental technical ideas can propagate down there.”
“From the F1 project, we can learn how to improve the hybrid system itself, and of course, we can learn how to create high-efficiency batteries and motors for the future. That’s why we decided to reparticipate in Formula 1,” Watanabe said.
Racing has always been used to improve the breed, but now mostly with software.
Credit: Aurich Lawson | Getty Images | NASA
Credit: Aurich Lawson | Getty Images | NASA
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.
Jonathan is the Automotive Editor at Ars Technica. He has a BSc and PhD in Pharmacology. In 2014 he decided to indulge his lifelong passion for the car by leaving the National Human Genome Research Institute and launching Ars Technica’s automotive coverage. He lives in Washington, DC.
Enlarge/ The Cadillac V-Series.R is one of General Motors’ factory-backed racing programs.
James Moy Photography/Getty Images
It is hard to escape the feeling that a few too many businesses are jumping on the AI hype train because it’s hype-y, rather than because AI offers an underlying benefit to their operation. So I will admit to a little inherent skepticism, and perhaps a touch of morbid curiosity, when General Motors got in touch wanting to show off some of the new AI/machine learning tools it has been using to win more races in NASCAR, sportscar racing, and IndyCar. As it turns out, that skepticism was misplaced.
GM has fingers in a lot of motorsport pies, but there are four top-level programs it really, really cares about. Number one for an American automaker is NASCAR—still the king of motorsport here—where Chevrolet supplies engines to six Cup teams. IndyCar, which could once boast of being America’s favorite racing, is home to another six Chevy-powered teams. And then there’s sportscar racing; right now, Cadillac is competing in IMSA’s GTP class and the World Endurance Championship’s Hypercar class, plus a factory Corvette Racing effort in IMSA.
“In all the series we race we either have key partners or specific teams that run our cars. And part of the technical support that they get from us are the capabilities of my team,” said Jonathan Bolenbaugh, motorsports analytics leader at GM, based at GM’s Charlotte Technical Center in North Carolina.
Unlike generative AI that’s being developed to displace humans from creative activities, GM sees the role of AI and ML as supporting human subject-matter experts so they can make the cars go faster. And it’s using these tools in a variety of applications.
Enlarge/ One of GM’s command centers at its Charlotte Technical Center in North Carolina.
General Motors
Each team in each of those various series (obviously) has people on the ground at each race, and invariably more engineers and strategists helping them from Indianapolis, Charlotte, or wherever it is that the particular race team has its home base. But they’ll also be tied in with a team from GM Motorsport, working from one of a number of command centers at its Charlotte Technical Center.
What did they say?
Connecting all three are streams and streams of data from the cars themselves (in series that allow car-to-pit telemetry) but also voice comms, text-based messaging, timing and scoring data from officials, trackside photographs, and more. And one thing Bolenbaugh’s team and their suite of tools can do is help make sense of that data quickly enough for it to be actionable.
“In a series like F1, a lot of teams will have students who are potentially newer members of the team literally listening to the radio and typing out what is happening, then saying, ‘hey, this is about pitting. This is about track conditions,'” Bolenbaugh said.
Instead of giving that to the internship kids, GM built a real time audio transcription tool to do that job. After trying out a commercial off-the-shelf solution, it decided to build its own, “a combination of open source and some of our proprietary code,” Bolenbaugh said. As anyone who has ever been to a race track can attest, it’s a loud environment, so GM had to train models with all the background noise present.
“We’ve been able to really improve our accuracy and usability of the tool to the point where some of the manual support for that capability is now dwindling,” he said, with the benefit that it frees up the humans, who would otherwise be transcribing, to apply their brains in more useful ways.
Take a look at this
Another tool developed by Bolenbaugh and his team was built to quickly analyze images taken by trackside photographers working for the teams and OEMs. While some of the footage they shoot might be for marketing or PR, a lot of it is for the engineers.
Two years ago, getting those photos from the photographer’s camera to the team was the work of two to three minutes. Now, “from shutter click at the racetrack in a NASCAR event to AI-tagged into an application for us to get information out of those photos is seven seconds,” Bolenbaugh said.
Enlarge/ Sometimes you don’t need a ML tool to analyze a photo to tell you the car is damaged.
Jeffrey Vest/Icon Sportswire via Getty Images
“Time is everything, and the shortest lap time that we run—the Coliseum would be an outlier, but maybe like 18 seconds is probably a short lap time. So we need to be faster than from when they pass that pit lane entry to when they come back again,” he said.
At the rollout of this particular tool at a NASCAR race last year, one of GM’s partner teams was able to avoid a cautionary pitstop after its driver scraped the wall, when the young engineer who developed the tool was able to show them a seconds-old photo of the right side of the car that showed it had escaped any damage.
“They didn’t have to wait for a spotter to look, they didn’t have to wait for the driver’s opinion. They knew that didn’t have damage. That team made the playoffs in that series by four points, so in the event that they would have pitted, there’s a likelihood where they didn’t make it,” he said. In cases where a car is damaged, the image analysis tool can automatically flag that and make that known quickly through an alert.
Not all of the images are used for snap decisions like that—engineers can glean a lot about their rivals from photos, too.
“We would be very interested in things related to the geometry of the car for the setup settings—wicker settings, wing angles… ride heights of the car, how close the car is to the ground—those are all things that would be great to know from an engineering standpoint, and those would be objectives that we would have in doing image analysis,” said Patrick Canupp, director of motorsports competition engineering at GM.
Enlarge/ Many of the photographers you see working trackside will be shooting on behalf of teams or manufacturers.
Steve Russell/Toronto Star via Getty Images
“It’s not straightforward to take a set of still images and determine a lot of engineering information from those. And so we’re working on that actively to help with all the photos that come in to us on a race weekend—there’s thousands of them. And so it’s a lot of information that we have at our access, that we want to try to maximize the engineering information that we glean from all of that data. It’s kind of a big data problem that AI is really geared for,” Canupp said.
The computer says we should pit now
Remember that transcribed audio feed from earlier? “If a bunch of drivers are starting to talk about something similar in the race like the track condition, we can start inferring, based on… the occurrence of certain words, that the track is changing,” said Bolenbaugh. “It might not just be your car… if drivers are talking about something on track, the likelihood of a caution, which is a part of our strategy model, might be going up.”
That feeds into a strategy tool that also takes lap times from timing and scoring, as well as fuel efficiency data in racing series that provide it for all cars, or a predictive model to do the same in series like NASCAR and IndyCar where teams don’t get to see that kind of data from their competitors, as well as models of tire wear.
“One of the biggest things that we need to manage is tires, fuel, and lap time. Everything is a trade-off between trying to execute the race the fastest,” Bolenbaugh said.
Obviously races are dynamic situations, and so “multiple times a lap as the scenario changes, we’re updating our recommendation. So, with tire fall off [as the tire wears and loses grip], you’re following up in real time, predicting where it’s going to be. We are constantly evolving during the race and doing transfer learning so we go into the weekend, as the race unfolds, continuing to train models in real time,” Bolenbaugh said.
Enlarge/ The current crop of GTP hybrid prototypes look wonderful, thanks to rules that cap the amount of downforce they can generate in favor of more dramatic styling.
Porsche Motorsport
DAYTONA BEACH, Fla.—Near-summer temperatures greeted a record crowd at the Daytona International Speedway in Florida last weekend. At the end of each January, the track hosts the Rolex 24, an around-the-clock endurance race that’s now as high-profile as it has ever been during the event’s 62-year history.
Between the packed crowd and the 59-car grid, there’s proof that sports car racing is in good shape. Some of that might be attributable to Drive to Survive‘s rising tide lifting a bunch of non-F1 boats, but there’s more to the story than just a resurgent interest in motorsport. The dramatic-looking GTP prototypes have a lot to do with it—powerful hybrid racing cars from Acura, BMW, Cadillac, and Porsche are bringing in the fans and, in some cases, some pretty famous drivers with F1 or IndyCar wins on their resumes.
But IMSA and the Rolex 24 is about more than just the top class of cars; in addition to the GTP hybrids, the field also comprised the very competitive pro-am LMP2 prototype class and a pair of classes (one for professional teams, another for pro-ams) for production-based machines built to a global set of rules, called GT3. (To be slightly confusing, in IMSA, those classes are known as GTD-Pro and GTD. More on sports car racing being needlessly confusing later.)
Enlarge/ The crowd for the 2024 Rolex 24 was larger even than last year. This is the pre-race grid walk, which I chose to watch from afar.
Jonathan Gitlin
There was even a Hollywood megastar in attendance, as the Jerry Bruckheimer-produced, Joseph Kosinski-directed racing movie starring Brad Pitt was at the track filming scenes for the start of that movie.
GTP finds its groove
Last year’s Rolex 24 was the debut of the new GTP cars, and they didn’t have an entirely trouble-free race. These cars are some of the most complicated sports prototypes to turn a wheel due to hybrid systems, and during the 2023 race, two of the entrants required lengthy stops to replace their hybrid batteries. Those teething troubles are a thing of the past, and over the last 12 months, the cars have found an awful lot more speed, with most of the 10-car class breaking Daytona’s lap record during qualifying.
Most of that new speed has come from the teams’ familiarity with the cars after a season of racing but also from a year of software development. Only Porsche’s 963 has had any mechanical upgrades during the off-season. “You… will not notice anything on the outside shell of the car,” explained Urs Kuratle, Porsche Motorsport’s director of factory racing. “So the aerodynamics, all [those] things, they look the same… Sometimes it’s a material change, where a fitting used to be out of aluminum and due to reliability reasons we change to steel or things like this. There are minor details like this.”
This year, the Wayne Taylor Racing team had not one but two ARX-06s. I expected the cars to be front-runners, but a late BoP change added another 40 kg.
Jonathan Gitlin
The Cadillacs are fan favorites because of their loud, naturally aspirated V8s. I think the car looks better than the other GTP cars, too.
Jonathan Gitlin
Porsche’s 963 is the only GTP car that has had any changes since last year, but they’re all under the bodywork.
Jonathan Gitlin
Porsche is the only manufacturer to start selling customer GTP cars so far. The one on the left is the Proton Competition Mustang Sampling car; the one on the right belongs to JDC-Miller MotorSports.
Jonathan Gitlin
GTP cars aren’t as fast or even as powerful as an F1 single-seater, but the driver workload from inside the cockpit may be even higher. At last year’s season-ending Petit Le Mans, former F1 champion Jenson Button—then making a guest appearance in the privateer-run JDC Miller Motorsport Porsche 963—came away with a newfound respect for how many different systems could be tweaked from the steering wheel.
