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:
Feb. 03, 2026

Filed:

Jan. 05, 2024
Applicant:

Connaught Electronics Ltd., Tuam, IE;

Inventors:

Lihao Wang, Bietigheim-Bissingen, DE;

Xinhua Xiao, Bietigheim-Bissingen, DE;

Thomas Heitzmann, Bietigheim-Bissingen, DE;

Deep Doshi, Bietigheim-Bissingen, DE;

Rachid Benmokhtar, Bietigheim-Bissingen, DE;

Sree Harsha Chowdary Gorantla, Bietigheim-Bissingen, DE;

Assignee:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06V 10/77 (2022.01); B60W 50/06 (2006.01); B60W 50/14 (2020.01); B60W 60/00 (2020.01); G06T 7/246 (2017.01); G06T 7/292 (2017.01); G06T 19/00 (2011.01); G06V 10/82 (2022.01); G06V 20/58 (2022.01); G06V 20/70 (2022.01); H04N 5/262 (2006.01); H04N 5/265 (2006.01); H04N 7/18 (2006.01);
U.S. Cl.
CPC ...
H04N 7/181 (2013.01); B60W 50/06 (2013.01); B60W 50/14 (2013.01); B60W 60/001 (2020.02); G06T 7/248 (2017.01); G06T 7/292 (2017.01); G06T 19/00 (2013.01); G06V 10/7715 (2022.01); G06V 10/82 (2022.01); G06V 20/58 (2022.01); H04N 5/2628 (2013.01); H04N 5/265 (2013.01); B60W 2420/403 (2013.01); G06T 2207/20221 (2013.01); G06T 2207/30241 (2013.01); G06T 2207/30252 (2013.01); G06V 20/70 (2022.01);
Abstract

A method includes obtaining image frames from each camera disposed along a vehicle, where each image frame corresponds to a same timestamp. The method further includes constructing a first birds-eye view (BEV) image from each image frame with a first BEV module and constructing a second BEV image from each image frame by Inverse Perspective Mapping (IPM) with a second BEV module. The first BEV module extracts features of an external environment of the vehicle from each image frame, transforms the features to a three-dimensional space, and projects the three-dimensional space onto an overhead two-dimensional plane. Subsequently, a merging module merges the first and second BEV images to produce a hybrid BEV image. Features of an external environment of the vehicle within the hybrid BEV image are detected by a deep learning neural network and the hybrid BEV image is displayed to a user in the vehicle.


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