公开(公告)号：US20230360232A1

公开(公告)日：2023-11-09

申请号：US17955827

申请日：2022-09-29

Applicant: NVIDIA Corporation

Inventor： Mehmet K. Kocamaz ,  Parthiv Parikh ,  Sangmin Oh

IPC: G06T7/246

CPC classification number: G06T7/248 ,  G06T2207/30261

Abstract: In various examples, systems and methods for tracking objects and determining time-to-collision values associated with the objects are described. For instance, the systems and methods may use feature points associated with an object depicted in a first image and feature points associated with a second image to determine a scalar change associated with the object. The systems and methods may then use the scalar change to determine a translation associated with the object. Using the scalar change and the translation, the systems and methods may determine that the object is also depicted in the second image. The systems and methods may further use the scalar change and a temporal baseline to determine a time-to-collision associated with the object. After performing the determinations, the systems and methods may output data representing at least an identifier for the object, a location of the object, and/or the time-to-collision.

公开(公告)号：US20240428596A1

公开(公告)日：2024-12-26

申请号：US18074708

申请日：2022-12-05

Applicant: NVIDIA Corporation

Inventor： Parthiv Parikh ,  Mehmet K. Kocamaz

IPC: G06V20/58

Abstract: In various examples, object track management for autonomous or semi-autonomous systems and applications is described herein. Systems and methods are disclosed that may limit the number of objects that are tracked based on one or more criteria. For instance, the number of objects that are tracked may be limited to a threshold number of objects when a number of detected objects exceeds a threshold. The systems and methods may use parameters associated with the detected objects to determine priority scores associated with the detected objects, and may then determine to only track the detected objects with the highest scores (e.g., high priority objects). As a result, latency and compute of the system may be reduced while still maintaining tracking with respect to safety-critical objects.

公开(公告)号：US20240211748A1

公开(公告)日：2024-06-27

申请号：US18146671

申请日：2022-12-27

Applicant: NVIDIA Corporation

Inventor： Neeraj Sajjan ,  Mehmet Kocamaz ,  Parthiv Parikh

IPC: G06N3/08

CPC classification number: G06N3/08

Abstract: In various examples, systems and methods are disclosed relating to determining associations between objects represented in sensor data and predicted states of the objects in multi-sensor systems such as autonomous or semi-autonomous vehicle perception systems. Systems and methods are disclosed that employ neural network models, such as multi-layer perceptron (MLP) models or other deep neural network (DNN) models, in learning association costs between sensor measurements and predicted states of objects. During training, the systems and methods can generate data for updating parameters of the neural network models such that, during deployment, the neural network models can receive sensor data and predicted states, and provide corresponding association costs.