How AI Helps Keep NASCAR Drivers Safe

When your race car is flying around the track at nearly 200 miles per hour, anything out of order —  even a candy wrapper stuck to the grill — can pose a danger to both car and driver.

NASCAR racing teams want to know about everything going on with their cars, but their high speed makes them hard to see clearly. That’s why the teams have photographers snapping thousands of photos throughout each race.

And with 40 cars on the track at a time, finding the car you care about quickly in all of those images can be the difference the checkered flag and a disastrous fire.

Neural Nets Fighting Fires at NASCAR

Bryan Goodman, an engineer with Argo AI / Ford Motor Company, spoke at the GPU Technology Conference last week about how his team applies deep learning originally built to develop self-driving cars to detect images of specific race cars.

Classifying NASCAR imagesFord’s deep learning neural network was trained on a manual training set of thousands of images labeled by humans. Once it was trained, it started outperforming humans in identifying cars correctly, particularly when the number on the car was unclear or when the image was blurry.

“We still have humans annotating data for training and testing, but we’re at a point where the networks are doing better than humans, even when the humans are highly trained,” said Goodman.

Picking the right race car isn’t as easy as one might think. NASCAR cars are repainted regularly; sometimes each week, according to Goodman, as sponsorships and other design elements on the car change. But two things stay the same: the car’s number and its manufacturer.

Goodman’s team suspected these were the items that the neural network prioritized in order to obtain such good results. To find out, the team applied activated filters to the network to determine which elements were heavily weighted. As suspected, the number on the car and the vehicle manufacturer stood out.

ARGO AI inspecting the neural network“Sometimes I hear people describe machine learning and, in particular, deep neural networks as a black box,” said Goodman. “But that’s not accurate, because we can get a lot of information out of them.” (Read more about how NVIDIA is peering into our own autonomous driving neural net.)

The Road Ahead

In February, Argo AI and Ford announced they would join forces to strengthen the commercialization of self-driving vehicles. The Pittsburgh-based Argo AI team is now working alongside the autonomous driving team at Ford.

Just before the announcement, Ken Washington, vice president of Research and Advanced Engineering at Ford, wrote about the company’s plans for an autonomous future.

“Ford’s plan to develop self-driving vehicles isn’t just about freeing up time otherwise spent driving. It’s about making life better,” wrote Washington.

We agree, and we look forward to working with all of our partners to bring this future to life.

For more on NVIDIA’s full stack of automotive solutions, including the DRIVE PX 2 AI car supercomputer and the NVIDIA DriveWorks open platform for developers, visit NVIDIA.com/drive.

AI Podcast: Where Deep Learning Will Take Driving Next

Want to hear where deep learning is taking driving next? Check out episode 4 of the AI Podcast, featuring NVIDIA’s Danny Shapiro in conversation with podcast host Michael Copeland.

Feature image by Royalbroil, licensed via Wikimedia Commons.

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