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：US20230360255A1

公开(公告)日：2023-11-09

申请号：US17955822

申请日：2022-09-29

Applicant: NVIDIA Corporation

Inventor： Mehmet K. Kocamaz ,  Daniel Per Olof Svensson ,  Hang Dou ,  Sangmin Oh ,  Minwoo Park ,  Kexuan Zou

IPC: G06T7/73 ,  G06T7/20

CPC classification number: G06T7/73 ,  G06T7/20 ,  G06V2201/07 ,  G06T2207/30241 ,  G06T2207/30252

Abstract: In various examples, techniques for multi-dimensional tracking of objects using two-dimensional (2D) sensor data are described. Systems and methods may use first image data to determine a first 2D detected location and a first three-dimensional (3D) detected location of an object. The systems and methods may then determine a 2D estimated location using the first 2D detected location and a 3D estimated location using the first 3D detected location. The systems and methods may use second image data to determine a second 2D detected location and a second 3D detected location of a detected object, and may then determine that the object corresponds to the detected object using the 2D estimated location, the 3D estimated location, the second 2D detected location, and the second 3D detected location. The systems and method then generate, modify, delete, or otherwise update an object track that includes 2D state information and 3D state information.

公开(公告)号：US20220379913A1

公开(公告)日：2022-12-01

申请号：US17827280

申请日：2022-05-27

Applicant: NVIDIA Corporation

Inventor： Berta Rodriguez Hervas ,  Hang Dou ,  Hsin-I Chen ,  Kexuan Zou ,  Nizar Gandy Assaf ,  Minwoo Park

IPC: B60W60/00 ,  G06V20/56 ,  G06V20/58

Abstract: In various examples, lanes may be grouped and a sign may be assigned to a lane in a group, then propagated to another lane in the group to associate semantic meaning corresponding to the sign with the lanes. The sign may be assigned to the most similar lane as quantified by a matching score subject to the lane meeting any hard constraints. Propagation of an assignment of the sign to a different lane may be based on lane attributes and/or sign attributes. Lane attributes may be evaluated and assignments of signs may occur for a lane as a whole, and/or for particular segments of a lane (e.g., of multiple segments perceived by the system). A sign may be a compound sign that is identified as individual signs, which are associated with one another. Attributes of the compound sign may provide semantic meaning used to operate a machine.

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：US20240230339A1

公开(公告)日：2024-07-11

申请号：US18615894

申请日：2024-03-25

Applicant: NVIDIA Corporation

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

IPC: G01C21/26 ,  G06N3/04 ,  G06N3/08 ,  G06V10/44 ,  G06V10/46 ,  G06V10/764 ,  G06V10/82 ,  G06V20/56

CPC classification number: G01C21/26 ,  G06N3/04 ,  G06N3/08 ,  G06V10/454 ,  G06V10/462 ,  G06V10/764 ,  G06V10/82 ,  G06V20/56 ,  G06F2218/12

Abstract: In various examples, live perception from sensors of a vehicle may be leveraged to generate potential paths for the vehicle to navigate an intersection in real-time or near real-time. For example, a deep neural network (DNN) may be trained to compute various outputs—such as heat maps corresponding to key points associated with the intersection, vector fields corresponding to directionality, heading, and offsets with respect to lanes, intensity maps corresponding to widths of lanes, and/or classifications corresponding to line segments of the intersection. The outputs may be decoded and/or otherwise post-processed to reconstruct an intersection—or key points corresponding thereto—and to determine proposed or potential paths for navigating the vehicle through the intersection.

公开(公告)号：US12013244B2

公开(公告)日：2024-06-18

申请号：US16848102

申请日：2020-04-14

Applicant: NVIDIA Corporation

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

IPC: G05D1/00 ,  G01C21/26 ,  G06N3/04 ,  G06N3/08 ,  G06V10/44 ,  G06V10/46 ,  G06V10/764 ,  G06V10/82 ,  G06V20/56 ,  B60W30/18 ,  B60W60/00 ,  G06F18/2413 ,  G06N3/02 ,  G06N3/044 ,  G06N3/045 ,  G06N3/047 ,  G06N3/048 ,  G06N3/088 ,  G06N5/01 ,  G06N7/01 ,  G06N20/00 ,  G06N20/10 ,  G08G1/16

CPC classification number: G01C21/26 ,  G05D1/0083 ,  G05D1/0246 ,  G06N3/04 ,  G06N3/08 ,  G06V10/454 ,  G06V10/462 ,  G06V10/764 ,  G06V10/82 ,  G06V20/56 ,  G06F2218/12

Abstract: In various examples, live perception from sensors of a vehicle may be leveraged to generate potential paths for the vehicle to navigate an intersection in real-time or near real-time. For example, a deep neural network (DNN) may be trained to compute various outputs—such as heat maps corresponding to key points associated with the intersection, vector fields corresponding to directionality, heading, and offsets with respect to lanes, intensity maps corresponding to widths of lanes, and/or classifications corresponding to line segments of the intersection. The outputs may be decoded and/or otherwise post-processed to reconstruct an intersection—or key points corresponding thereto—and to determine proposed or potential paths for navigating the vehicle through the intersection.

公开(公告)号：US11897471B2

公开(公告)日：2024-02-13

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 ,  G06V10/75

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：US20200341466A1

公开(公告)日：2020-10-29

申请号：US16848102

申请日：2020-04-14

Applicant: NVIDIA Corporation

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

IPC: G05D1/00 ,  G06N3/08 ,  G06N3/04 ,  G06K9/00 ,  G05D1/02

Abstract: In various examples, live perception from sensors of a vehicle may be leveraged to generate potential paths for the vehicle to navigate an intersection in real-time or near real-time. For example, a deep neural network (DNN) may be trained to compute various outputs—such as heat maps corresponding to key points associated with the intersection, vector fields corresponding to directionality, heading, and offsets with respect to lanes, intensity maps corresponding to widths of lanes, and/or classifications corresponding to line segments of the intersection. The outputs may be decoded and/or otherwise post-processed to reconstruct an intersection—or key points corresponding thereto—and to determine proposed or potential paths for navigating the vehicle through the intersection.

公开(公告)号：US12233854B2

公开(公告)日：2025-02-25

申请号：US17690865

申请日：2022-03-09

Applicant: NVIDIA Corporation

Inventor： Berta Rodriguez Hervas ,  Hang Dou ,  Kexuan Zou ,  Hsin-I Chen ,  Nizar Gandy Assaf ,  Minwoo Park

IPC: B60W30/06 ,  B60W50/14 ,  B60W60/00 ,  G06V20/56 ,  G06V20/58

Abstract: In various examples, perception-based parking assistance systems and methods for an ego-machine are presented. Example embodiments may determine a location of a real-world parking strip relative to an ego-machine and an associated parking rule for the parking strip. A virtual parking strip and one or more virtual parking signs may be generated based at least in part on one or more detected features in an environment of the ego-machine and a tracked motion of the ego machine, and the virtual parking strip may be used to track parking strip locations and associated parking rules. The virtual parking strips and associated rules may be relied upon by an ego-machine to determine parking locations and/or to navigate into a suitable parking spot.

公开(公告)号：US20230360231A1

公开(公告)日：2023-11-09

申请号：US17955814

申请日：2022-09-29

Applicant: NVIDIA Corporation

Inventor： Mehmet K. Kocamaz ,  Daniel Per Olof Svensson ,  Hang Dou ,  Sangmin Oh ,  Minwoo Park ,  Kexuan Zou

IPC: G06T7/246

CPC classification number: G06T7/246 ,  G06T2207/30252

Abstract: In various examples, techniques for multi-dimensional tracking of objects using two-dimensional (2D) sensor data are described. Systems and methods may use first image data to determine a first 2D detected location and a first three-dimensional (3D) detected location of an object. The systems and methods may then determine a 2D estimated location using the first 2D detected location and a 3D estimated location using the first 3D detected location. The systems and methods may use second image data to determine a second 2D detected location and a second 3D detected location of a detected object, and may then determine that the object corresponds to the detected object using the 2D estimated location, the 3D estimated location, the second 2D detected location, and the second 3D detected location. The systems and method then generate, modify, delete, or otherwise update an object track that includes 2D state information and 3D state information.