公开(公告)号：US20200210779A1

公开(公告)日：2020-07-02

申请号：US16594200

申请日：2019-10-07

Applicant: Cognata Ltd.

Inventor： Dan ATSMON ,  Eran ASA ,  Ehud SPIEGEL

IPC: G06K9/62 ,  G06T7/579 ,  G06T3/00 ,  G06T7/55 ,  G06K9/00 ,  G05D1/02 ,  G05D1/00 ,  G06N3/08 ,  G06N3/04

Abstract: A system for creating synthetic data for testing an autonomous system, comprising at least one hardware processor adapted to execute a code for: using a machine learning model to compute a plurality of depth maps based on a plurality of real signals captured simultaneously from a common physical scene, each of the plurality of real signals are captured by one of a plurality of sensors, each of the plurality of computed depth maps qualifies one of the plurality of real signals; applying a point of view transformation to the plurality of real signals and the plurality of depth maps, to produce synthetic data simulating a possible signal captured from the common physical scene by a target sensor in an identified position relative to the plurality of sensors; and providing the synthetic data to at least one testing engine to test an autonomous system comprising the target sensor.

公开(公告)号：US20190228571A1

公开(公告)日：2019-07-25

申请号：US16313058

申请日：2017-05-29

Applicant: Cognata Ltd.

Inventor： Dan ATSMON

IPC: G06T17/05 ,  G05D1/02 ,  G05D1/00 ,  G06F17/50

Abstract: A computer implemented method of creating a simulated realistic virtual model of a geographical area for training an autonomous driving system, comprising obtaining geographic map data of a geographical area, obtaining visual imagery data of the geographical area, classifying static objects identified in the visual imagery data to corresponding labels to designate labeled objects, superimposing the labeled objects over the geographic map data, generating a virtual 3D realistic model emulating the geographical area by synthesizing a corresponding visual texture for each of the labeled objects and injecting synthetic 3D imaging feed of the realistic model to imaging sensor(s) input(s) of the autonomous driving system controlling movement of an emulated vehicle in the realistic model where the synthetic 3D imaging feed is generated to depict the realistic model from a point of view of emulated imaging sensor(s) mounted on the emulated vehicle.

公开(公告)号：US20250014276A1

公开(公告)日：2025-01-09

申请号：US18887069

申请日：2024-09-17

Applicant: Cognata Ltd.

Inventor： Dan ATSMON

IPC: G06T17/05 ,  G01C21/00 ,  G06F30/20 ,  G06T19/00

Abstract: A computer implemented method of creating a simulated realistic virtual model of a geographical area for training an autonomous driving system, comprising obtaining geographic map data of a geographical area, obtaining visual imagery data of the geographical area, classifying static objects identified in the visual imagery data to corresponding labels to designate labeled objects, superimposing the labeled objects over the geographic map data, generating a virtual 3D realistic model emulating the geographical area by synthesizing a corresponding visual texture for each of the labeled objects and injecting synthetic 3D imaging feed of the realistic model to imaging sensor(s) input(s) of the autonomous driving system controlling movement of an emulated vehicle in the realistic model where the synthetic 3D imaging feed is generated to depict the realistic model from a point of view of emulated imaging sensor(s) mounted on the emulated vehicle.

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).

公开(公告)号：US20220188579A1

公开(公告)日：2022-06-16

申请号：US17687720

申请日：2022-03-07

Applicant: Cognata Ltd.

Inventor： Dan ATSMON ,  Eran ASA ,  Ehud SPIEGEL

IPC: G06K9/62 ,  G06N20/00 ,  G06N5/04 ,  G06T7/00

Abstract: A method for training a model for generating simulation data for training an autonomous driving agent, comprising: analyzing real data, collected from a driving environment, to identify a plurality of environment classes, a plurality of moving agent classes, and a plurality of movement pattern classes; generating a training environment, according to one environment class; and in at least one training iteration: generating, by a simulation generation model, a simulated driving environment according to the training environment and according to a plurality of generated training agents, each associated with one of the plurality of agent classes and one of the plurality of movement pattern classes; collecting simulated driving data from the simulated environment; and modifying at least one model parameter of the simulation generation model to minimize a difference between a simulation statistical fingerprint, computed using the simulated driving data, and a real statistical fingerprint, computed using the real data.