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US Patent 12072442 Object detection and detection confidence suitable for autonomous driving

Patent 12072442 was granted and assigned to NVIDIA on August, 2024 by the United States Patent and Trademark Office.

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Contents

Is a
Patent
Patent
0

Patent attributes

Patent Applicant
NVIDIA
NVIDIA
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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
120724420
Patent Inventor Names
Sriya Sarathy0
Tero Kuosmanen0
Timo Roman0
William Zhang0
Nizar Assaf0
Pekka Janis0
Colin Tracey0
Tommi Koivisto0
Date of Patent
August 27, 2024
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Patent Application Number
174560450
Date Filed
November 22, 2021
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Patent Citations
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US Patent 9701307 Systems and methods for hazard mitigation
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US Patent 9710714 Fusion of RGB images and LiDAR data for lane classification
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US Patent 9742869 Approach to adaptive allocation of shared resources in computer systems
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US Patent 10007269 Collision-avoidance system for autonomous-capable vehicle
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US Patent 10108867 Image-based pedestrian detection
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US Patent 7409295 Imminent-collision detection system and process
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US Patent 8204542 Methods for processing apparatus originated communication request and communication apparatuses utilizing the same
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US Patent 8204642 Driving assistance apparatus
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Patent Primary Examiner
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Syed Haider
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CPC Code
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G06N 3/084
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G06V 10/762
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G06V 20/58
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G06V 10/454
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G06V 20/584
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G06V 10/7715
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G06V 10/225
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G06V 10/774
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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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