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US Patent 10019011 Autonomous vehicles featuring machine-learned yield model

Patent 10019011 was granted and assigned to Uber on July, 2018 by the United States Patent and Trademark Office.

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Patent abstractTimelineTable: Further ResourcesReferences
Is a
Patent
Patent

Patent attributes

Patent Applicant
Uber
Uber
Current Assignee
Uber
Uber
Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
Patent Number
10019011
Date of Patent
July 10, 2018
Patent Application Number
15791646
Date Filed
October 24, 2017
Patent Citations Received
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US Patent 12130624 Discrete decision architecture for motion planning system of an autonomous vehicle
3
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US Patent 11507099 Systems and methods for graph-based AI training
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US Patent 12094355 Autonomous transportation system and methods
5
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US Patent 12115990 Trajectory modifications based on a collision zone
6
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US Patent 11492010 Vehicle and self-driving control device
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US Patent 10275752 Anticipatory creation of point-of-sale data structures
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US Patent 10458810 Traffic light state assessment
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US Patent 10460208 System and method for generating large simulation data sets for testing an autonomous driver
...
Patent Primary Examiner
‌
Muhammad Shafi
Patent abstract

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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