Driverless startup Cruise today detailed a homegrown tool — the Continuous Learning Machine — that tackles on-the-road prediction tasks. Cruise claims the Continuous Learning Machine, which automatically labels and mines training data, allows some of the AI models guiding Cruise’s self-driving cars to predict things like whether bicycles will swerve into traffic or kids will run into streets.
From an article in Venture Beat by Kyle Wiggers.
One of the challenges of autonomous vehicles is predicting intent. People don’t always follow the rules of the road, and even when they do, they’re liable to bend those rules. According to the U.S. National Highway Traffic Safety Administration, 94% of serious crashes are due to drivers’ errors or dangerous choices.
That’s why Cruise built Continuous Learning Machine. Leveraging a technique called active learning, it automatically identifies errors made by perception models running on Cruise’s cars, and only scenarios with a significant difference between prediction and reality are added to the training data sets. Cruise says this enables extremely targeted data mining, minimizing the number of “easy” scenarios that enter the corpora.
The Continuous Learning Machine also labels data autonomously using model predictions as “ground truth” for all scenarios. Essentially, the framework observes what a person or vehicle might do in the future and compares that against what they actually end up doing. The final step is training a new model, running it through testing, and deploying it to the road while ensuring performance exceeds that of the previous model.
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