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:
Sep. 23, 2025

Filed:

Jul. 01, 2022
Applicant:

Autobrains Technologies Ltd, Tel Aviv-Jaffa, IL;

Inventors:

Roi Saida, Acco, IL;

Shay Ieizerovitch, Mikhmoret, IL;

Igal Raichelgauz, Tel Aviv, IL;

Assignee:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06V 20/56 (2022.01); G06V 10/44 (2022.01); G06V 10/82 (2022.01);
U.S. Cl.
CPC ...
G06V 20/588 (2022.01); G06V 10/44 (2022.01); G06V 10/82 (2022.01); G06V 2201/10 (2022.01);
Abstract

A method for lane boundary detection. The method may include obtaining an image of an environment of a vehicle. The environment may include at least one lane boundary; the images may include image segments; calculating image segment lane boundary metadata for relevant image segments. The calculating is executed by a neural network and is based on the image segments; wherein each relevant image segment comprises one or more lane boundary points candidates; wherein image segment lane boundary metadata that is related to a relevant image segment which may include (i) location information about location of the one or more lane boundary points candidates of the relevant image segment, and (ii) angular information regarding an angle between lane boundary points candidates of the image; and determining an estimate of a lane boundary of the at least one lane boundary; wherein the determining is based on the image segment lane boundary metadata of the relevant image segments; wherein the determining comprises applying a process that differs from neural network processing.


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