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US Patent 11210537 Object detection and detection confidence suitable for autonomous driving

Patent 11210537 was granted and assigned to NVIDIA on December, 2021 by the United States Patent and Trademark Office.

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Patent
Patent
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Patent attributes

Current Assignee
NVIDIA
NVIDIA
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Patent Jurisdiction
United States Patent and Trademark Office
United States Patent and Trademark Office
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Patent Number
112105370
Patent Inventor Names
Tommi Koivisto0
William Zhang0
Colin Tracey0
Nizar Assaf0
Pekka Janis0
Sriya Sarathy0
Tero Kuosmanen0
Timo Roman0
Date of Patent
December 28, 2021
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Patent Application Number
162778950
Date Filed
February 15, 2019
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Patent Citations Received
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US Patent 11679780 Methods and systems for monitoring vehicle motion with driver safety alerts
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US Patent 11698272 Map creation and localization for autonomous driving applications
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US Patent 11713978 Map creation and localization for autonomous driving applications
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US Patent 11772658 Systems, media, and methods applying machine learning to telematics data to generate driver fingerprint
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US Patent 11776282 Method, apparatus, and system for removing outliers from road lane marking data
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US Patent 11788861 Map creation and localization for autonomous driving applications
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US Patent 11361201 Systems and methods for determining an object type and an attribute for an observation based on fused sensor data
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Patent Primary Examiner
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Syed Haider
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Patent abstract

In various examples, detected object data representative of locations of detected objects in a field of view may be determined. One or more clusters of the detected objects may be generated based at least in part on the locations and features of the cluster may be determined for use as inputs to a machine learning model(s). A confidence score, computed by the machine learning model(s) based at least in part on the inputs, may be received, where the confidence score may be representative of a probability that the cluster corresponds to an object depicted at least partially in the field of view. Further examples provide approaches for determining ground truth data for training object detectors, such as for determining coverage values for ground truth objects using associated shapes, and for determining soft coverage values for ground truth objects.

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