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:
Mar. 29, 2022

Filed:

Dec. 03, 2018
Applicant:

Cmmb Vision Usa Inc., Central Hong Kong, HK;

Inventors:

Guanbin Xing, Central Hong Kong, HK;

Hui Liu, Central Hong Kong, HK;

Jenq-Neng Hwang, Central Hong Kong, HK;

Assignee:

CMMB VISION USA INC., Central Hong Kong, HK;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G01S 13/86 (2006.01); G01S 13/931 (2020.01); G01S 7/41 (2006.01); G01S 7/00 (2006.01); G05D 1/00 (2006.01); G05D 1/02 (2020.01);
U.S. Cl.
CPC ...
G01S 13/867 (2013.01); G01S 7/003 (2013.01); G01S 7/41 (2013.01); G01S 13/931 (2013.01); G05D 1/0088 (2013.01); G05D 1/0214 (2013.01); G05D 1/0246 (2013.01); G05D 1/0257 (2013.01); G05D 2201/0213 (2013.01);
Abstract

A sensor fusion system for an autonomous vehicle and/or advanced driver assistance systems (ADAS) that combines measurements from multiple independent sensors to generate a better output. The multiple sensors can interact and perform 'cross-training' for enhanced sensor performance. The sensor fusion system utilizes data exchange and cross-training between sensors for enhanced sensor performance while constantly improving sensor fusion results. Some exemplary embodiments of the present disclosure provide a system for an autonomous vehicle and/or a vehicle ADAS that combines information from independent sensors in order to accurately distinguish environmental objects and movements. This allows the vehicle to accurately make informed navigation decisions based on the environmental surroundings. The sensor fusion system can exchange ground truth information between the sensors related to a same object, which allows for constant machine learning object classification and constant speed/distance estimation calibration.


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