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UC Berkeley AI researchers say they’ve created AI for autonomous vehicles driving in unseen, real-world landscapes that outperforms leading methods for delivery robots driving on sidewalks. Called LaND, for Learning to Navigate from Disengagements, the navigation system studies disengagement events, then predicts when disengagements will happen in the future. The approach is meant to provide what the researchers call a needed shift in perspective about disengagements for the AI community.
A disengagement describes each instance when an autonomous system encounters challenging conditions and must turn control back over to a human operator. Disengagement events are a contested, and some say outdated, metric for measuring the capabilities of an autonomous vehicle system. AI researchers often disengagements as a signal for troubleshooting or debugging navigation systems for delivery robots on sidewalks or autonomous vehicles on roads, but LaND treats disengagements as part of training data.
Doing so, according to engineers from Berkeley AI Research, allows the robot to learn from datasets collected naturally during the testing process. Other systems have learned directly from training data gathered from onboard sensors, but researchers say that can require a lot of labeled data and be expensive.
“Our results demonstrate LaND can successfully learn to …
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