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:
Dec. 22, 2020

Filed:

Oct. 26, 2018
Applicant:

Volvo Car Corporation, Gothenburg, SE;

Inventors:

Sohini Roy Chowdhury, Mountain View, CA (US);

Srikar Muppirisetty, Gothenburg, SE;

Assignee:

Volvo Car Corporation, Göteborg, SE;

Attorneys:
Primary Examiner:
Int. Cl.
CPC ...
G06K 9/62 (2006.01); G06T 7/62 (2017.01); G06N 20/00 (2019.01); G05D 1/00 (2006.01); G05D 1/02 (2020.01); G06K 9/00 (2006.01);
U.S. Cl.
CPC ...
G06K 9/6256 (2013.01); G05D 1/0088 (2013.01); G05D 1/0221 (2013.01); G05D 1/0248 (2013.01); G06K 9/00805 (2013.01); G06N 20/00 (2019.01); G06T 7/62 (2017.01); G06T 2207/10028 (2013.01); G06T 2207/30252 (2013.01);
Abstract

The present invention relates to methods and systems for generating annotated data for training vehicular driver assist (DA) and autonomous driving (AD) active safety (AS) functionalities and the like. More specifically, the present invention relates to methods and systems for the fast estimation of three-dimensional (3-D) bounding boxes and drivable surfaces using LIDAR point clouds and the like. These methods and systems provide fast and accurate annotation cluster pre-proposals on a minimally-supervised or unsupervised basis, segment drivable surfaces/ground planes in a bird's-eye-view (BEV) construct, and provide fast and accurate annotation cluster pre-proposal labels based on the feature-based detection of similar objects in already-annotated frames. The methods and systems minimize the expertise, time, and expense associated with the manual annotation of LIDAR point clouds and the like in the generation of annotated data for training machine learning (ML) algorithms and the like.


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