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. 24, 2020

Filed:

Sep. 29, 2016
Applicants:

The Charles Stark Draper Laboratory, Inc., Cambridge, MA (US);

Veoneer Us Inc., Southfield, MI (US);

Inventors:

Matthew Graham, Arlington, MA (US);

Troy Jones, Somerville, MA (US);

Kyra Horne, Boston, MA (US);

Scott Lennox, Arlington, MA (US);

Jon D. Demerly, Byron, MI (US);

Stephen Sheldon Strong, Howell, MI (US);

Afrah Faiz Naik, Waltham, MA (US);

Assignees:

The Charles Stark Kraper Laboratory, Inc., Cambridge, MA (US);

VEONEER US INC., Southfield, MI (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06K 9/00 (2006.01); G05D 1/02 (2020.01); G06K 9/62 (2006.01); G01S 17/66 (2006.01); G01S 13/931 (2020.01); G06T 7/70 (2017.01); G01S 7/48 (2006.01); G01S 13/86 (2006.01); G01S 13/72 (2006.01); G01S 17/86 (2020.01); G01S 17/931 (2020.01);
U.S. Cl.
CPC ...
G05D 1/021 (2013.01); G01S 7/4808 (2013.01); G01S 13/726 (2013.01); G01S 13/865 (2013.01); G01S 13/867 (2013.01); G01S 13/931 (2013.01); G01S 17/66 (2013.01); G01S 17/86 (2020.01); G01S 17/931 (2020.01); G06K 9/00791 (2013.01); G06K 9/629 (2013.01); G06T 7/70 (2017.01); G01S 2013/932 (2020.01); G01S 2013/9322 (2020.01);
Abstract

Previous self-driving car systems can detect objects separately with either vision systems, RADAR systems or LIDAR systems. In an embodiment of the present invention, an object fusion module normalizes sensor output from vision, RADAR, and LIDAR systems into a common format. Then, the system fuses the object-level sensor data across all systems by associating all objects detected and predicting tracks for all objects. The present system improves over previous systems by using the data from all sensors combined to develop a single set of knowledge about the objects around the self-driving car, instead of each sensor operating separately.


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