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. 04, 2026

Filed:

Dec. 20, 2024
Applicants:

Constructor Technology Ag, Schaffhausen, CH;

Constructor Education and Research Genossenschaft, Schaffhausen, CH;

Inventors:

Ilya Shimchik, Zurich, CH;

Ruslan Mustafin, Georgia, GE;

Maksim Liubimov, Belgrade, RS;

Aleksandr Buival, Bremen, DE;

Serg Bell, Singapore, SG;

Stanislav Protasov, Singapore, SG;

Nikolay Dobrovolskiy, Alanya, TR;

Laurent Dedenis, Geneva, CH;

Assignee:

Constructor Technology AG, Schaffhausen, CH;

Attorneys:
Primary Examiner:
Int. Cl.
CPC ...
B60W 60/00 (2020.01); B60W 50/00 (2006.01); G01C 21/00 (2006.01); G06V 10/26 (2022.01); G06V 10/75 (2022.01); G06V 10/77 (2022.01); G06V 10/82 (2022.01); G06V 20/58 (2022.01); G06V 10/774 (2022.01);
U.S. Cl.
CPC ...
B60W 60/001 (2020.02); B60W 50/00 (2013.01); G01C 21/3811 (2020.08); G01C 21/3837 (2020.08); G01C 21/3859 (2020.08); G06V 10/26 (2022.01); G06V 10/751 (2022.01); G06V 10/7715 (2022.01); G06V 10/82 (2022.01); G06V 20/58 (2022.01); B60W 2050/0028 (2013.01); B60W 2420/403 (2013.01); B60W 2554/20 (2020.02); B60W 2554/4029 (2020.02); G06V 10/774 (2022.01);
Abstract

Systems and methods include detecting obstacles and drivable areas by an autonomous vehicle by inputting image and map data into a neural network to extract feature vectors. A transformer encoder converts these vectors from camera space to Bird's Eye View (BEV) space. A detection head identifies objects, and a segmentation head generates a BEV map showing objects and drivable surfaces. Attributes from both heads are compared, and the segmentation head's weights are updated accordingly, resulting in an updated BEV segmentation map output by the updated segmentation head.


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