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. 21, 2018

Filed:

Jun. 15, 2016
Applicant:

Ford Global Technologies, Llc, Dearborn, MI (US);

Inventors:

Ashley Elizabeth Micks, Mountain View, CA (US);

Jinesh J Jain, Palo Alto, CA (US);

Kyu Jeong Han, Palo Alto, CA (US);

Harpreetsingh Banvait, Sunnyvale, CA (US);

Assignee:

FORD GLOBAL TECHNOLOGIES, LLC, Dearborn, MI (US);

Attorneys:
Primary Examiner:
Int. Cl.
CPC ...
G06K 9/62 (2006.01); G06K 9/66 (2006.01); G01S 13/00 (2006.01); G01S 13/86 (2006.01); G01S 13/93 (2006.01); G01S 17/00 (2006.01); G01S 17/93 (2006.01); G06F 17/50 (2006.01); G06N 99/00 (2010.01); G06K 9/00 (2006.01); G05D 1/00 (2006.01); G09B 9/54 (2006.01);
U.S. Cl.
CPC ...
G06K 9/66 (2013.01); G01S 13/006 (2013.01); G01S 13/865 (2013.01); G01S 13/931 (2013.01); G01S 17/006 (2013.01); G01S 17/936 (2013.01); G05D 1/0088 (2013.01); G06F 17/5009 (2013.01); G06K 9/00825 (2013.01); G06N 99/005 (2013.01); G09B 9/54 (2013.01);
Abstract

A machine learning model is trained by defining a scenario including models of vehicles and a typical driving environment. A model of a subject vehicle is added to the scenario and sensor locations are defined on the subject vehicle. A perception of the scenario by sensors at the sensor locations is simulated. The scenario further includes a model of a parked vehicle with its engine running. The location of the parked vehicle and the simulated outputs of the sensors perceiving the scenario are input to a machine learning algorithm that trains a model to detect the location of the parked vehicle based on the sensor outputs. A vehicle controller then incorporates the machine learning model and estimates the presence and/or location of a parked vehicle with its engine running based on actual sensor outputs input to the machine learning model.


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