The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Aug. 13, 2024

Filed:

Mar. 07, 2022
Applicant:

Cognata Ltd., Rehovot, IL;

Inventors:

Dan Atsmon, Rehovot, IL;

Eran Asa, Petach-Tikva, IL;

Ehud Spiegel, Petach-Tikva, IL;

Assignee:

Cognata Ltd., Rehovot, IL;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06V 10/82 (2022.01); G06F 18/20 (2023.01); G06F 18/21 (2023.01); G06F 18/214 (2023.01); G06F 18/2415 (2023.01); G06N 3/006 (2023.01); G06N 5/043 (2023.01); G06N 20/00 (2019.01); G06T 7/00 (2017.01); G06V 10/74 (2022.01); G06V 10/774 (2022.01); G06V 10/776 (2022.01); G06V 10/80 (2022.01); G06V 20/56 (2022.01); G06V 20/70 (2022.01);
U.S. Cl.
CPC ...
G06N 3/006 (2013.01); G06F 18/2148 (2023.01); G06F 18/2193 (2023.01); G06F 18/2415 (2023.01); G06F 18/285 (2023.01); G06N 5/043 (2013.01); G06N 20/00 (2019.01); G06T 7/0002 (2013.01); G06V 10/761 (2022.01); G06V 10/774 (2022.01); G06V 10/776 (2022.01); G06V 10/809 (2022.01); G06V 10/82 (2022.01); G06V 20/56 (2022.01); G06V 20/70 (2022.01); G06T 2207/30168 (2013.01);
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.


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