公开(公告)号：US20240410705A1

公开(公告)日：2024-12-12

申请号：US18330145

申请日：2023-06-06

Applicant: NVIDIA Corporation

Inventor： Trung Pham ,  Minwoo Park ,  Ha Giang Truong ,  Atchuta Venkata Vijay Chintalapudi ,  Hae-Jong Seo

IPC: G01C21/34 ,  G06N20/00

Abstract: In various examples, path detection using machine learning models for autonomous or semi-autonomous systems and applications is described herein. Systems and methods are disclosed that use one or more machine learning models to determine a geometry associated with a path for a vehicle. To determine the geometry, the machine learning model(s) may process sensor data generated using the vehicle and, based at least on the processing, output points associated with the path. In some examples, the machine learning model(s) outputs a limited number of points, such as between five and twenty points. One or more algorithms, such as one or more Bezier algorithms, may then be used to generate the geometry based at least on the points. As such, in some examples, the geometry may correspond to a Bezier curve that represents the path.

公开(公告)号：US20240127454A1

公开(公告)日：2024-04-18

申请号：US18391276

申请日：2023-12-20

Applicant: NVIDIA Corporation

Inventor： Trung Pham ,  Berta Rodriguez Hervas ,  Minwoo Park ,  David Nister ,  Neda Cvijetic

IPC: G06T7/11 ,  G05B13/02 ,  G06F18/21 ,  G06F18/24 ,  G06N3/04 ,  G06N3/08 ,  G06T3/4046 ,  G06T5/70 ,  G06T11/20 ,  G06V10/26 ,  G06V10/34 ,  G06V10/44 ,  G06V10/82 ,  G06V20/56 ,  G06V30/19 ,  G06V30/262

CPC classification number: G06T7/11 ,  G05B13/027 ,  G06F18/21 ,  G06F18/24 ,  G06N3/04 ,  G06N3/08 ,  G06T3/4046 ,  G06T5/70 ,  G06T11/20 ,  G06V10/267 ,  G06V10/34 ,  G06V10/454 ,  G06V10/82 ,  G06V20/56 ,  G06V30/19173 ,  G06V30/274 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30252 ,  G06T2210/12

Abstract: In various examples, live perception from sensors of a vehicle may be leveraged to detect and classify intersection contention areas 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 outputs—such as signed distance functions—that may correspond to locations of boundaries delineating intersection contention areas. The signed distance functions may be decoded and/or post-processed to determine instance segmentation masks representing locations and classifications of intersection areas or regions. The locations of the intersections areas or regions may be generated in image-space and converted to world-space coordinates to aid an autonomous or semi-autonomous vehicle in navigating intersections according to rules of the road, traffic priority considerations, and/or the like.

公开(公告)号：US20240101118A1

公开(公告)日：2024-03-28

申请号：US18537527

申请日：2023-12-12

Applicant: NVIDIA Corporation

Inventor： Sayed Mehdi Sajjadi Mohammadabadi ,  Berta Rodriguez Hervas ,  Hang Dou ,  Igor Tryndin ,  David Nister ,  Minwoo Park ,  Neda Cvijetic ,  Junghyun Kwon ,  Trung Pham

IPC: B60W30/18 ,  B60W30/09 ,  B60W30/095 ,  B60W60/00 ,  G06N3/08 ,  G06V10/25 ,  G06V10/75 ,  G06V10/764 ,  G06V10/80 ,  G06V10/82 ,  G06V20/56 ,  G06V20/70 ,  G08G1/01

CPC classification number: B60W30/18154 ,  B60W30/09 ,  B60W30/095 ,  B60W60/0011 ,  G06N3/08 ,  G06V10/25 ,  G06V10/751 ,  G06V10/764 ,  G06V10/803 ,  G06V10/82 ,  G06V20/56 ,  G06V20/588 ,  G06V20/70 ,  G08G1/0125

Abstract: 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.

公开(公告)号：US20200293796A1

公开(公告)日：2020-09-17

申请号：US16814351

申请日：2020-03-10

Applicant: NVIDIA Corporation

Inventor： Sayed Mehdi Sajjadi Mohammadabadi ,  Berta Rodriguez Hervas ,  Hang Dou ,  Igor Tryndin ,  David Nister ,  Minwoo Park ,  Neda Cvijetic ,  Junghyun Kwon ,  Trung Pham

IPC: G06K9/00 ,  G06K9/62 ,  G06N3/08 ,  B60W30/09 ,  B60W30/095 ,  B60W60/00 ,  G08G1/01

Abstract: 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.

公开(公告)号：US20240020953A1

公开(公告)日：2024-01-18

申请号：US18353453

申请日：2023-07-17

Applicant: NVIDIA Corporation

Inventor： Minwoo Park ,  Trung Pham ,  Junghyun Kwon ,  Sayed Mehdi Sajjadi Mohammadabadi ,  Bor-Jeng Chen ,  Xin Liu ,  Bala Siva Sashank Jujjavarapu ,  Mehran Maghoumi

IPC: G06V10/77 ,  G06V20/56 ,  G06V10/82

CPC classification number: G06V10/7715 ,  G06V20/56 ,  G06V10/82

Abstract: In various examples, feature values corresponding to a plurality of views are transformed into feature values of a shared orientation or perspective to generate a feature map—such as a Bird's-Eye-View (BEV), top-down, orthogonally projected, and/or other shared perspective feature map type. Feature values corresponding to a region of a view may be transformed into feature values using a neural network. The feature values may be assigned to bins of a grid and values assigned to at least one same bin may be combined to generate one or more feature values for the feature map. To assign the transformed features to the bins, one or more portions of a view may be projected into one or more bins using polynomial curves. Radial and/or angular bins may be used to represent the environment for the feature map.

公开(公告)号：US20230166733A1

公开(公告)日：2023-06-01

申请号：US18162576

申请日：2023-01-31

Applicant: NVIDIA Corporation

Inventor： Sayed Mehdi Sajjadi Mohammadabadi ,  Berta Rodriguez Hervas ,  Hang Dou ,  Igor Tryndin ,  David Nister ,  Minwoo Park ,  Neda Cvijetic ,  Junghyun Kwon ,  Trung Pham

IPC: B60W30/18 ,  G06N3/08 ,  G08G1/01 ,  B60W30/095 ,  B60W60/00 ,  B60W30/09 ,  G06V20/56 ,  G06V10/25 ,  G06V10/764 ,  G06V10/80 ,  G06V10/82 ,  G06V20/70

CPC classification number: B60W30/18154 ,  G06N3/08 ,  G08G1/0125 ,  B60W30/095 ,  B60W60/0011 ,  B60W30/09 ,  G06V20/588 ,  G06V10/25 ,  G06V10/764 ,  G06V10/803 ,  G06V10/82 ,  G06V20/70 ,  G06V20/56

Abstract: 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.

公开(公告)号：US11648945B2

公开(公告)日：2023-05-16

申请号：US16814351

申请日：2020-03-10

Applicant: NVIDIA Corporation

Inventor： Sayed Mehdi Sajjadi Mohammadabadi ,  Berta Rodriguez Hervas ,  Hang Dou ,  Igor Tryndin ,  David Nister ,  Minwoo Park ,  Neda Cvijetic ,  Junghyun Kwon ,  Trung Pham

IPC: G06V20/56 ,  G06N3/08 ,  G08G1/01 ,  B60W30/095 ,  B60W60/00 ,  B60W30/09 ,  G06V10/75

CPC classification number: G06V20/588 ,  B60W30/09 ,  B60W30/095 ,  B60W60/0011 ,  G06N3/08 ,  G06V10/751 ,  G08G1/0125

Abstract: 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.

公开(公告)号：US20220351524A1

公开(公告)日：2022-11-03

申请号：US17864026

申请日：2022-07-13

Applicant: NVIDIA Corporation

Inventor： Trung Pham ,  Berta Rodriguez Hervas ,  Minwoo Park ,  David Nister ,  Neda Cvijetic

IPC: G06V20/56 ,  G06N3/04 ,  G05B13/02 ,  G06T5/00 ,  G06T3/40 ,  G06T7/11 ,  G06T11/20 ,  G06K9/62 ,  G06N3/08 ,  G06V30/262

Abstract: In various examples, live perception from sensors of a vehicle may be leveraged to detect and classify intersection contention areas 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 outputs—such as signed distance functions—that may correspond to locations of boundaries delineating intersection contention areas. The signed distance functions may be decoded and/or post-processed to determine instance segmentation masks representing locations and classifications of intersection areas or regions. The locations of the intersections areas or regions may be generated in image-space and converted to world-space coordinates to aid an autonomous or semi-autonomous vehicle in navigating intersections according to rules of the road, traffic priority considerations, and/or the like.