公开(公告)号：US20230110713A1

公开(公告)日：2023-04-13

申请号：US17497479

申请日：2021-10-08

Applicant: NVIDIA Corporation

Inventor： Alperen Degirmenci ,  Won Hong ,  Mariusz Bojarski ,  Jesper Eduard van Engelen ,  Bernhard Firner ,  Zongyi Yang ,  Urs Muller

IPC: G05D1/02 ,  G06N3/00 ,  B60W60/00

Abstract: In various examples, a plurality of poses corresponding to one or more configuration parameters within an environment—such as a location of a machine within an environment, an orientation of a machine within an environment, a sensor angle pose of a machine, or a sensor location of a machine—may be used to generate training data and corresponding ground truth data for training a machine learning model—such as a deep neural network (DNN). As a result, the machine learning model, once deployed, may more accurately compute one or more outputs—such as outputs representative of lane boundaries, trajectories for an autonomous machine, etc.—agnostic to machine and/or sensor poses of the machine within which the machine learning model is deployed.