公开(公告)号：US11694388B2

公开(公告)日：2023-07-04

申请号：US17383465

申请日：2021-07-23

Applicant: Cognata Ltd.

Inventor： Dan Atsmon ,  Eran Asa ,  Ehud Spiegel

IPC: G06T15/10 ,  G06T7/55 ,  G06T7/579 ,  G06N3/08 ,  G06V20/56 ,  G06F18/21 ,  G06F18/24 ,  G06F18/28 ,  G05D1/00 ,  G05D1/02 ,  G06N3/04 ,  G06T3/00 ,  G06F18/214

CPC classification number: G06T15/10 ,  G05D1/0088 ,  G05D1/0246 ,  G06F18/217 ,  G06F18/2148 ,  G06F18/24 ,  G06F18/28 ,  G06N3/04 ,  G06N3/08 ,  G06T3/0018 ,  G06T7/55 ,  G06T7/579 ,  G06V20/56 ,  G05D2201/0213 ,  G06T2207/10028 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30252 ,  G06V2201/07

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：US11417057B2

公开(公告)日：2022-08-16

申请号：US16693534

申请日：2019-11-25

Applicant: Cognata Ltd.

Inventor： Dan Atsmon

IPC: G06T17/05 ,  G06T19/00 ,  G06F30/20 ,  G05D1/00 ,  G05D1/02

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：US12061965B2

公开(公告)日：2024-08-13

申请号：US17687720

申请日：2022-03-07

Applicant: Cognata Ltd.

Inventor： Dan Atsmon ,  Eran Asa ,  Ehud Spiegel

IPC: G06V10/82 ,  G06F18/20 ,  G06F18/21 ,  G06F18/214 ,  G06F18/2415 ,  G06N3/006 ,  G06N5/043 ,  G06N20/00 ,  G06T7/00 ,  G06V10/74 ,  G06V10/774 ,  G06V10/776 ,  G06V10/80 ,  G06V20/56 ,  G06V20/70

CPC classification number: G06N3/006 ,  G06F18/2148 ,  G06F18/2193 ,  G06F18/2415 ,  G06F18/285 ,  G06N5/043 ,  G06N20/00 ,  G06T7/0002 ,  G06V10/761 ,  G06V10/774 ,  G06V10/776 ,  G06V10/809 ,  G06V10/82 ,  G06V20/56 ,  G06V20/70 ,  G06T2207/30168

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.

公开(公告)号：US11270165B2

公开(公告)日：2022-03-08

申请号：US17286526

申请日：2019-10-15

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.

公开(公告)号：US10460208B1

公开(公告)日：2019-10-29

申请号：US16237806

申请日：2019-01-02

Applicant: Cognata Ltd.

Inventor： Dan Atsmon ,  Eran Asa

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

Abstract: A system for creating synthetic data for testing an autonomous system, comprising at least one hardware processor adapted to execute a code for: producing a plurality of synthetic training signals, each simulating one of a plurality of signals simultaneously captured from a common training scene by a plurality of sensors, and a plurality of training depth maps each qualifying one of the plurality of synthetic training signals according to the common training scene; training a machine learning model based on the plurality of synthetic training signals and the plurality of training depth maps; using the machine learning model to compute a plurality of computed depth maps based on a plurality of real signals, the plurality of real signals are captured simultaneously from a common physical scene, each of the plurality of real signals are captured by one of the plurality of sensors, each of the plurality of computed depth maps.

公开(公告)号：US20180349526A1

公开(公告)日：2018-12-06

申请号：US15990877

申请日：2018-05-29

Applicant: Cognata Ltd.

Inventor： Dan Atsmon ,  Guy Tsafrir ,  Eran Asa

IPC: G06F17/50 ,  G06T19/00 ,  G06N3/04 ,  G06N3/08

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：US12260489B2

公开(公告)日：2025-03-25

申请号：US18202970

申请日：2023-05-29

Applicant: Cognata Ltd.

Inventor： Dan Atsmon ,  Eran Asa ,  Ehud Spiegel

IPC: G06T15/10 ,  G05D1/00 ,  G06F18/21 ,  G06F18/214 ,  G06F18/24 ,  G06F18/28 ,  G06N3/04 ,  G06N3/08 ,  G06T3/047 ,  G06T7/55 ,  G06T7/579 ,  G06V20/56

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：US12112432B2

公开(公告)日：2024-10-08

申请号：US17885633

申请日：2022-08-11

Applicant: Cognata Ltd.

Inventor： Dan Atsmon

IPC: G06T19/00 ,  G01C21/00 ,  G05D1/00 ,  G06F30/20 ,  G06T17/05

CPC classification number: G06T17/05 ,  G01C21/3815 ,  G01C21/3826 ,  G01C21/3867 ,  G05D1/0088 ,  G06F30/20 ,  G06T19/006

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：US11100371B2

公开(公告)日：2021-08-24

申请号：US16594200

申请日：2019-10-07

Applicant: Cognata Ltd.

Inventor： Dan Atsmon ,  Eran Asa ,  Ehud Spiegel

IPC: G06K9/62 ,  G06T7/55 ,  G06T7/579 ,  G06D1/02 ,  G06N3/04 ,  G06N3/08 ,  G05D1/00 ,  G06K9/00 ,  G06T3/00 ,  G05D1/02

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.