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

IPC: G06K9/00 ,  G06K9/32 ,  G05D1/02 ,  G05D1/00 ,  G06N3/08 ,  G06T7/10

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.

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.

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.

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.

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.

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.