公开(公告)号：US20240096014A1

公开(公告)日：2024-03-21

申请号：US18518654

申请日：2023-11-24

Applicant: Cognata Ltd.

Inventor： Dan ATSMON ,  Guy TSAFRIR ,  Eran ASA

IPC: G06T17/05 ,  G06T19/00 ,  G09B9/04 ,  G09B9/048

CPC classification number: G06T17/05 ,  G06T19/003 ,  G09B9/04 ,  G09B9/048 ,  G06N20/10

Abstract: A computer implemented method of creating data for a host vehicle simulation, comprising: in each of a plurality of iterations of a host vehicle simulation using at least one processor for: obtaining from an environment simulation engine a semantic-data dataset representing a plurality of scene objects in a geographical area, each one of the plurality of scene objects comprises at least object location coordinates and a plurality of values of semantically described parameters; creating a 3D visual realistic scene emulating the geographical area according to the dataset; applying at least one noise pattern associated with at least one sensor of a vehicle simulated by the host vehicle simulation engine on the virtual 3D visual realistic scene to create sensory ranging data simulation of the geographical area; converting the sensory ranging data simulation to an enhanced dataset emulating the geographical area, the enhanced dataset comprises a plurality of enhanced scene objects.

公开(公告)号：US20230316789A1

公开(公告)日：2023-10-05

申请号：US18022556

申请日：2021-09-14

Applicant: Cognata Ltd.

Inventor： Ilan TSAFRIR ,  Guy TSAFRIR ,  Ehud SPIEGEL ,  Dan ATSMON

IPC: G06V20/70 ,  G06V20/58 ,  G06V10/77

CPC classification number: G06V20/70 ,  G06V20/58 ,  G06V10/7715

Abstract: There is provided a method for annotating digital images for training a machine learning model, comprising: generating, from digital images and a plurality of dense depth maps, each associated with one of the digital images, an aligned three-dimensional stacked scene representation of a scene, where the digital images are captured by sensor(s) at the scene, and where each point in the three-dimensional stacked scene is associated with a stability score indicative of a likelihood the point is associated with a static object of the scene, removing from the three-dimensional stacked scene unstable points to produce a static three-dimensional stacked scene, detecting in at least one of the digital images static object(s) according to the static three-dimensional stacked scene, and classifying and annotating the static object(s). The machine learning model may be trained on the images annotated with a ground truth of the static object(s).