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US Patent 11648945 Intersection detection and classification in autonomous machine applications

Patent 11648945 was granted and assigned to NVIDIA on May, 2023 by the United States Patent and Trademark Office.

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Is a
Patent
Patent
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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
116489450
Patent Inventor Names
Igor Tryndin0
Junghyun Kwon0
Neda Cvijetic0
Sayed Mehdi Sajjadi Mohammadabadi0
Trung Pham0
Minwoo Park0
Berta Rodriguez Hervas0
David Nister0
...
Date of Patent
May 16, 2023
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Patent Application Number
168143510
Date Filed
March 10, 2020
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Patent Citations
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US Patent 11042163 Guiding vehicles through vehicle maneuvers using machine learning models
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US Patent 10936902 Training bounding box selection
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US Patent 11182916 Distance to obstacle detection in autonomous machine applications
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US Patent 11210537 Object detection and detection confidence suitable for autonomous driving
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US Patent 11321924 Caching and updating of dense 3D reconstruction data
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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 10133274 Adaptive road model manager
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Patent Citations Received
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US Patent 11995900 Method on identifying indicia orientation and decoding indicia for machine vision systems
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US Patent 12008788 Evaluating spatial relationships using vision transformers
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US Patent 12008822 Using neural networks for 3D surface structure estimation based on real-world data for autonomous systems and applications
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US Patent 11967022 3D surface structure estimation using neural networks for autonomous systems and applications
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Patent Primary Examiner
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Babar Sarwar
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CPC Code
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G01C 21/30
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G01C 21/3602
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G01C 21/32
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G01S 19/41
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G01S 13/86
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G01S 13/89
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G01S 17/86
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G01S 17/89
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In various examples, live perception from sensors of a vehicle may be leveraged to detect and classify intersections in an environment of a vehicle in real-time or near real-time. For example, a deep neural network (DNN) may be trained to compute various outputs—such as bounding box coordinates for intersections, intersection coverage maps corresponding to the bounding boxes, intersection attributes, distances to intersections, and/or distance coverage maps associated with the intersections. The outputs may be decoded and/or post-processed to determine final locations of, distances to, and/or attributes of the detected intersections.

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