This invention describes how an autonomous vehicle can improve its ability to predict where other objects are going. It does this by constantly checking how accurate its previous predictions were against its own estimate of uncertainty. If the prediction error was larger than expected, the vehicle increases its uncertainty for future predictions; if the error was smaller, it decreases it. This refined uncertainty then helps the vehicle's planning system make better decisions about how to maneuver.
Why it matters: Filed before the latest wave of AI/ML models for autonomous driving. The claims describe a feedback loop for dynamically adjusting prediction uncertainty, which is now more effectively implemented with current deep learning architectures and real-time processing capabilities.
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