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:
Feb. 05, 2019

Filed:

Oct. 24, 2016
Applicant:

International Business Machines Corporation, Armonk, NY (US);

Inventors:

Wei Shan Dong, Beijing, CN;

Peng Gao, Beijing, CN;

Jian Li, Beijing, CN;

Chang Sheng Li, Beijing, CN;

Wen Han Luo, Shenzhen, CN;

Chun Yang Ma, Beijing, CN;

Renjie Yao, Beijing, CN;

Ting Yuan, Beijing, CN;

Jun Zhu, Shanghai, CN;

Attorneys:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G06N 3/08 (2006.01); B60W 40/08 (2012.01); B60W 50/06 (2006.01); G01S 19/42 (2010.01); G01S 19/14 (2010.01); G06N 3/04 (2006.01);
U.S. Cl.
CPC ...
G06N 3/08 (2013.01); B60W 40/08 (2013.01); B60W 50/06 (2013.01); G01S 19/42 (2013.01); G01S 19/14 (2013.01); G06N 3/0454 (2013.01);
Abstract

Systems and methods for obtaining vehicle operational data and driving context data from one or more monitoring systems, including converting the obtained vehicle operational data and driving context data into sequential vehicle operational feature data and sequential driving context feature data, calibrating the sequential vehicle operational feature data and the sequential driving context feature data temporally to form calibrated sequential vehicle operational feature data and calibrated sequential driving context feature data, constructing a sequence table of temporal sample points based on the calibrated sequential vehicle operational feature data and the calibrated sequential driving context feature data, feeding the sequence table into a deep neural network model for applying network learning to form a trained deep neural network model, extracting driving behavior features from the trained deep neural network model and analyzing the extracted driving behavior features to determine driving behavior characteristics of the driver.


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