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.
