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:
Nov. 17, 2020

Filed:

Mar. 23, 2018
Applicant:

Sf Motors, Inc., Santa Clara, CA (US);

Inventors:

Xinhua Xiao, Santa Clara, CA (US);

Yifan Tang, Santa Clara, CA (US);

Assignees:

SF MOTORS, INC., Santa Clara, CA (US);

CHONGQING JINKANG NEW ENERGY VEHICLE CO., LTD, Chongqing, CN;

Attorneys:
Primary Examiner:
Int. Cl.
CPC ...
B60W 30/06 (2006.01); B60W 30/00 (2006.01); B62D 15/02 (2006.01); G05D 1/02 (2020.01); G06K 9/00 (2006.01); G06N 3/00 (2006.01); G06N 3/04 (2006.01); G05D 1/00 (2006.01); G08G 1/01 (2006.01); G06K 9/62 (2006.01); G06N 3/08 (2006.01); G08G 1/14 (2006.01);
U.S. Cl.
CPC ...
B60W 30/06 (2013.01); B60W 30/00 (2013.01); B62D 15/02 (2013.01); B62D 15/0285 (2013.01); G05D 1/0088 (2013.01); G05D 1/0221 (2013.01); G05D 1/0274 (2013.01); G05D 1/0278 (2013.01); G06K 9/00 (2013.01); G06K 9/00805 (2013.01); G06K 9/00812 (2013.01); G06K 9/6267 (2013.01); G06N 3/00 (2013.01); G06N 3/0454 (2013.01); G06N 3/084 (2013.01); G08G 1/0112 (2013.01); G08G 1/0133 (2013.01); G08G 1/0141 (2013.01); G08G 1/14 (2013.01); G08G 1/143 (2013.01); G08G 1/146 (2013.01); B60W 2530/14 (2013.01); B60W 2554/00 (2020.02); G05D 1/0248 (2013.01); G05D 2201/0213 (2013.01);
Abstract

Systems and methods of deep neural network based parking assistance is provided. A system can receive data sensed by one or more sensors mounted on a vehicle located at a parking zone. The system generates, from a first neural network, a digital map based on the data sensed by the one or more sensors. The system generates, from a second neural network, a first path based on the three-dimensional dynamic map. The system receives vehicle dynamics information from a second one or more sensors located on the vehicle. The system generates, with a third neural network, a second path to park the vehicle based on the first path, vehicle dynamics information and at least one historical path stored in vehicle memory. The system provides commands to control the vehicle to follow the second path to park the vehicle in the parking zone.


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