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公开(公告)号：US12266148B2
公开(公告)日：2025-04-01
申请号：US18309882
申请日：2023-05-01
Applicant: NVIDIA Corporation
Inventor： Yifang Xu , Xin Liu , Chia-Chih Chen , Carolina Parada , Davide Onofrio , Minwoo Park , Mehdi Sajjadi Mohammadabadi , Vijay Chintalapudi , Ozan Tonkal , John Zedlewski , Pekka Janis , Jan Nikolaus Fritsch , Gordon Grigor , Zuoguan Wang , I-Kuei Chen , Miguel Sainz
IPC: G06V10/44 , G05D1/00 , G06F18/2413 , G06N3/084 , G06T7/10 , G06V10/46 , G06V10/764 , G06V10/82 , G06V20/40 , G06V20/56
Abstract: In various examples, sensor data representative of an image of a field of view of a vehicle sensor may be received and the sensor data may be applied to a machine learning model. The machine learning model may compute a segmentation mask representative of portions of the image corresponding to lane markings of the driving surface of the vehicle. Analysis of the segmentation mask may be performed to determine lane marking types, and lane boundaries may be generated by performing curve fitting on the lane markings corresponding to each of the lane marking types. The data representative of the lane boundaries may then be sent to a component of the vehicle for use in navigating the vehicle through the driving surface.
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公开(公告)号：US20230267701A1
公开(公告)日：2023-08-24
申请号：US18309882
申请日：2023-05-01
Applicant: NVIDIA Corporation
Inventor： Yifang Xu , Xin Liu , Chia-Chin Chen , Carolina Parada , Davide Onofrio , Minwoo Park , Mehdi Sajjadi Mohammadabadi , Vijay Chintalapudi , Ozan Tonkal , John Zedlewski , Pekka Janis , Jan Nikolaus Fritsch , Gordon Grigor , Zuoguan Wang , I-Kuei Chen , Miguel Sainz
IPC: G06V10/44 , G06T7/10 , G05D1/00 , G06N3/084 , G05D1/02 , G06V20/56 , G06V10/46 , G06V20/40 , G06F18/2413 , G06V10/764 , G06V10/82
CPC classification number: G06V10/44 , G06T7/10 , G05D1/0088 , G06N3/084 , G05D1/0221 , G06V20/588 , G06V10/46 , G06V10/457 , G06V20/41 , G06F18/24143 , G06V10/764 , G06V10/82 , G06V10/471
Abstract: In various examples, sensor data representative of an image of a field of view of a vehicle sensor may be received and the sensor data may be applied to a machine learning model. The machine learning model may compute a segmentation mask representative of portions of the image corresponding to lane markings of the driving surface of the vehicle. Analysis of the segmentation mask may be performed to determine lane marking types, and lane boundaries may be generated by performing curve fitting on the lane markings corresponding to each of the lane marking types. The data representative of the lane boundaries may then be sent to a component of the vehicle for use in navigating the vehicle through the driving surface.
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公开(公告)号：US20190384304A1
公开(公告)日：2019-12-19
申请号：US16433994
申请日：2019-06-06
Applicant: NVIDIA Corporation
Inventor： Regan Blythe Towal , Maroof Mohammed Farooq , Vijay Chintalapudi , Carolina Parada , David Nister
Abstract: In various examples, a deep learning solution for path detection is implemented to generate a more abstract definition of a drivable path without reliance on explicit lane-markings—by using a detection-based approach. Using approaches of the present disclosure, the identification of drivable paths may be possible in environments where conventional approaches are unreliable, or fail—such as where lane markings do not exist or are occluded. The deep learning solution may generate outputs that represent geometries for one or more drivable paths in an environment and confidence values corresponding to path types or classes that the geometries correspond. These outputs may be directly useable by an autonomous vehicle—such as an autonomous driving software stack—with minimal post-processing.
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公开(公告)号：US20230205219A1
公开(公告)日：2023-06-29
申请号：US18182060
申请日：2023-03-10
Applicant: NVIDIA Corporation
Inventor： Regan Blythe Towal , Maroof Mohammed Farooq , Vijay Chintalapudi , Carolina Parada , David Nister
CPC classification number: G05D1/0221 , G06T7/60 , G06N3/04 , G06V20/56 , G06V10/764 , G06V10/82 , G06V20/588
Abstract: In various examples, a deep learning solution for path detection is implemented to generate a more abstract definition of a drivable path—without reliance on explicit lane-markings—by using a detection-based approach. Using approaches of the present disclosure, the identification of drivable paths may be possible in environments where conventional approaches are unreliable, or fail—such as where lane markings do not exist or are occluded. The deep learning solution may generate outputs that represent geometries for one or more drivable paths in an environment and confidence values corresponding to path types or classes that the geometries correspond. These outputs may be directly useable by an autonomous vehicle—such as an autonomous driving software stack—with minimal post-processing.
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公开(公告)号：US20190266418A1
公开(公告)日：2019-08-29
申请号：US16286329
申请日：2019-02-26
Applicant: NVIDIA Corporation
Inventor： Yifang Xu , Xin Liu , Chia-Chih Chen , Carolina Parada , Davide Onofrio , Minwoo Park , Mehdi Sajjadi Mohammadabadi , Vijay Chintalapudi , Ozan Tonkal , John Zedlewski , Pekka Janis , Jan Nikolaus Fritsch , Gordon Grigor , Zuoguan Wang , I-Kuei Chen , Miguel Sainz
Abstract: In various examples, sensor data representative of an image of a field of view of a vehicle sensor may be received and the sensor data may be applied to a machine learning model. The machine learning model may compute a segmentation mask representative of portions of the image corresponding to lane markings of the driving surface of the vehicle. Analysis of the segmentation mask may be performed to determine lane marking types, and lane boundaries may be generated by performing curve fitting on the lane markings corresponding to each of the lane marking types. The data representative of the lane boundaries may then be sent to a component of the vehicle for use in navigating the vehicle through the driving surface.
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Patent Agency Ranking
公开(公告)号：US20250139934A1
公开(公告)日：2025-05-01
申请号：US19005672
申请日：2024-12-30
Applicant: NVIDIA Corporation
Inventor： Yifang Xu , Xin Liu , Chia-Chih Chen , Carolina Parada , Davide Onofrio , Minwoo Park , Mehdi Sajjadi Mohammadabadi , Vijay Chintalapudi , Ozan Tonkal , John Zedlewski , Pekka Janis , Jan Nikolaus Fritsch , Gordon Grigor , Zuoguan Wang , I-Kuei Chen , Miguel Sainz
IPC: G06V10/44 , G06F18/2413 , G06N3/084 , G06T7/10 , G06V10/46 , G06V10/764 , G06V10/82 , G06V20/40 , G06V20/56
Abstract: In various examples, sensor data representative of an image of a field of view of a vehicle sensor may be received and the sensor data may be applied to a machine learning model. The machine learning model may compute a segmentation mask representative of portions of the image corresponding to lane markings of the driving surface of the vehicle. Analysis of the segmentation mask may be performed to determine lane marking types, and lane boundaries may be generated by performing curve fitting on the lane markings corresponding to each of the lane marking types. The data representative of the lane boundaries may then be sent to a component of the vehicle for use in navigating the vehicle through the driving surface.
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公开(公告)号：US11676364B2
公开(公告)日：2023-06-13
申请号：US17222680
申请日：2021-04-05
Applicant: NVIDIA Corporation
Inventor： Yifang Xu , Xin Liu , Chia-Chih Chen , Carolina Parada , Davide Onofrio , Minwoo Park , Mehdi Sajjadi Mohammadabadi , Vijay Chintalapudi , Ozan Tonkal , John Zedlewski , Pekka Janis , Jan Nikolaus Fritsch , Gordon Grigor , Zuoguan Wang , I-Kuei Chen , Miguel Sainz
IPC: G06V10/44 , G06T7/10 , G05D1/00 , G06N3/084 , G05D1/02 , G06V20/56 , G06V10/46 , G06V20/40 , G06F18/2413 , G06V10/764 , G06V10/82
CPC classification number: G06V10/44 , G05D1/0088 , G05D1/0221 , G06F18/24143 , G06N3/084 , G06T7/10 , G06V10/457 , G06V10/46 , G06V10/764 , G06V10/82 , G06V20/41 , G06V20/588 , G06V10/471
Abstract: In various examples, sensor data representative of an image of a field of view of a vehicle sensor may be received and the sensor data may be applied to a machine learning model. The machine learning model may compute a segmentation mask representative of portions of the image corresponding to lane markings of the driving surface of the vehicle. Analysis of the segmentation mask may be performed to determine lane marking types, and lane boundaries may be generated by performing curve fitting on the lane markings corresponding to each of the lane marking types. The data representative of the lane boundaries may then be sent to a component of the vehicle for use in navigating the vehicle through the driving surface.
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公开(公告)号：US11675359B2
公开(公告)日：2023-06-13
申请号：US16433994
申请日：2019-06-06
Applicant: NVIDIA Corporation
Inventor： Regan Blythe Towal , Maroof Mohammed Farooq , Vijay Chintalapudi , Carolina Parada , David Nister
CPC classification number: G05D1/0221 , G06N3/04 , G06T7/60 , G06V10/764 , G06V10/82 , G06V20/56 , G06V20/588
Abstract: In various examples, a deep learning solution for path detection is implemented to generate a more abstract definition of a drivable path without reliance on explicit lane-markings—by using a detection-based approach. Using approaches of the present disclosure, the identification of drivable paths may be possible in environments where conventional approaches are unreliable, or fail—such as where lane markings do not exist or are occluded. The deep learning solution may generate outputs that represent geometries for one or more drivable paths in an environment and confidence values corresponding to path types or classes that the geometries correspond. These outputs may be directly useable by an autonomous vehicle—such as an autonomous driving software stack—with minimal post-processing.
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公开(公告)号：US20210224556A1
公开(公告)日：2021-07-22
申请号：US17222680
申请日：2021-04-05
Applicant: NVIDIA Corporation
Inventor： Yifang Xu , Xin Liu , Chia-Chih Chen , Carolina Parada , Davide Onofrio , Minwoo Park , Mehdi Sajjadi Mohammadabadi , Vijay Chintalapudi , Ozan Tonkal , John Zedlewski , Pekka Janis , Jan Nikolaus Fritsch , Gordon Grigor , Zuoguan Wang , I-Kuei Chen , Miguel Sainz
IPC: G06K9/00 , G06K9/32 , G06T7/10 , G05D1/00 , G06N3/08 , G05D1/02 , G06K9/46 , G06K9/48 , G06K9/62
Abstract: In various examples, sensor data representative of an image of a field of view of a vehicle sensor may be received and the sensor data may be applied to a machine learning model. The machine learning model may compute a segmentation mask representative of portions of the image corresponding to lane markings of the driving surface of the vehicle. Analysis of the segmentation mask may be performed to determine lane marking types, and lane boundaries may be generated by performing curve fitting on the lane markings corresponding to each of the lane marking types. The data representative of the lane boundaries may then be sent to a component of the vehicle for use in navigating the vehicle through the driving surface.
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公开(公告)号：US10997433B2
公开(公告)日：2021-05-04
申请号：US16286329
申请日：2019-02-26
Applicant: NVIDIA Corporation
Inventor： Yifang Xu , Xin Liu , Chia-Chih Chen , Carolina Parada , Davide Onofrio , Minwoo Park , Mehdi Sajjadi Mohammadabadi , Vijay Chintalapudi , Ozan Tonkal , John Zedlewski , Pekka Janis , Jan Nikolaus Fritsch , Gordon Grigor , Zuoguan Wang , I-Kuei Chen , Miguel Sainz
IPC: G06K9/00 , G06K9/32 , G06T7/10 , G05D1/00 , G06N3/08 , G05D1/02 , G06K9/46 , G06K9/48 , G06K9/62
Abstract: In various examples, sensor data representative of an image of a field of view of a vehicle sensor may be received and the sensor data may be applied to a machine learning model. The machine learning model may compute a segmentation mask representative of portions of the image corresponding to lane markings of the driving surface of the vehicle. Analysis of the segmentation mask may be performed to determine lane marking types, and lane boundaries may be generated by performing curve fitting on the lane markings corresponding to each of the lane marking types. The data representative of the lane boundaries may then be sent to a component of the vehicle for use in navigating the vehicle through the driving surface.
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