Autonomous Vehicles Featuring Machine-Learned Yield Model
AssigneeUatc, LLC
InventorsColin Jeffrey Green, Wei Liu, David McAllister Bradley, Vijay Subramanian
Life of this patent
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AssigneeUatc, LLC
InventorsColin Jeffrey Green, Wei Liu, David McAllister Bradley, Vijay Subramanian
Estimating the term…
The present disclosure provides autonomous vehicle systems and methods that include or otherwise leverage a machine-learned yield model. In particular, the machine-learned yield model can be trained or otherwise configured to receive and process feature data descriptive of objects perceived by the autonomous vehicle and/or the surrounding environment and, in response to receipt of the feature data, provide yield decisions for the autonomous vehicle relative to the objects. For example, a yield decision for a first object can describe a yield behavior for the autonomous vehicle relative to the first object (e.g., yield to the first object or do not yield to the first object). Example objects include traffic signals, additional vehicles, or other objects. The motion of the autonomous vehicle can be controlled in accordance with the yield decisions provided by the machine-learned yield model.
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A computing system for an autonomous vehicle, the computing system comprising:
The computing system of claim 1, wherein the autonomous vehicle is an autonomous truck.
A computer-implemented method comprising:
An autonomous vehicle comprising:
Dependencies are inferred from the claim text.