How GM is using AI to boost performance on the racetrack

Every second counts on the racetrack.

That’s one reason General Motors is integrating artificial intelligence into just about every aspect of its motorsports division.

Tech Brew recently got a peek behind the scenes of where the machine learning magic happens: GM’s Charlotte Technical Center in North Carolina. Jonathan Bolenbaugh, manager of data and analytics for GM Motorsports, gave us a virtual tour and explained how AI is changing the way the organization races.

It “took a leap of faith when we started the team,” he said. “Like, ‘Hey, we’re gonna use AI to change racing.’ And then people are like, ‘Why would we do that?’ Well, because it’s cool and it’s fun and I like winning races.”

Performance review: Bolenbaugh—whose experience spans both racing and production vehicles—formed the data and analytics team two years ago.

The idea is to use technology to assist the teams that GM Motorsports supports across racing series like NASCAR, IndyCar, and IMSA.

Data scientists and engineers use machine learning to create models based on vehicle performance data, track conditions, and competitors’ behaviors to inform race strategy. AI also is used in real-time race analysis to help the team make decisions and improve outcomes.

“It’s literally hundreds of people per car to get that out on the track,” Bolenbaugh said. “This team was founded with the intent of using technology—specifically AI and [machine learning] and some statistical methods—to give those people the tools that they need to have the peak outcome every weekend.”

Some of the AI-powered tools may seem basic, like real-time audio transcription of conversations drivers have over the radio, but consider the challenge of getting an accurate transcript amid noisy racetrack conditions.

The team also uses image analysis to get insights into as many as tens of thousands of photos that might be captured during a typical NASCAR event, according to Bolenbaugh.

“All the categorization and derived information is available seconds later,” he said. To determine the size of a wheel, for example, a process that used to take minutes—literally using a ruler to calculate the diameter in a photo—now takes a matter of seconds.

The team employs machine learning to predict competitors’ behaviors, and then, throughout a weeklong design cycle, the team quickly makes performance-boosting tweaks to race cars.

A typical week kicks off with a Monday morning report card on how the previous weekend’s races went. By Tuesday, Bolenbaugh said, the team is already making changes to vehicle models. It all leads up to Sunday, when changes are deployed and the data and analytics team is on hand to support the race team.

“We’re having a pretty dynamic conversation while this race is running about, ‘Hey, we think that the tool is overestimating this parameter or underestimating this parameter,’ and then we can tweak that,” Bolenbaugh said.

He insisted that these tools complement the work of human engineers without replacing them.

“We’re doing these things to cover the basics so the race engineers can do the things that only the humans can do. Obviously, technology is advancing, but sitting on a pit box, making calls, understanding how that works out, that intuition and that subject matter expertise still very much matters,” he said. “It matters more than ever, because a lot of times people will take AI and ML tools and say, ‘Well, AI told me to do this, it’s gotta be right.’”

Logan McLeod, who heads up software engineering at GM Motorsports, in a presentation earlier this year spelled out the demanding nature of racing. The organization participates in over 130 races a year, supporting dozens of race team partners. AI advancements, he said, enable the team to be more efficient by freeing up resources that can be used elsewhere.

“Whether it’s in multi-physics simulation or model-based collaboration, AI represents an acceleration opportunity across everything we do in technology,” he said.

Business case: One of the goals is to improve results on the track. But it’s also to feed data, lessons, and new tech to the teams that design GM’s production vehicles. Automakers use motorsports to test out tech that may one day make their way into mass-market vehicles.

“This design, build, test, iterate cycle—we get to do that every week. And the learnings from that translate back into production cars,” Bolenbaugh said. “Retooling a plant costs a lot of money. We can retool here and try things out, and the things that work, we can send right back and make our on-the-road cars much better for our customers.”