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. 31, 2021

Filed:

Jan. 19, 2017
Applicant:

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

Inventors:

John C. Anderson, Bridgewater, NJ (US);

Peter J. Arterburn, Frisco, TX (US);

Wei S. Dong, Beijing, CN;

Chang S. Li, Beijing, CN;

Philip L. Schwartz, Newburyport, MA (US);

Jun Zhu, Shanghai, CN;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06Q 30/02 (2012.01); G06F 16/33 (2019.01); G06F 16/38 (2019.01); G06Q 30/00 (2012.01); G06N 3/04 (2006.01); B60W 40/09 (2012.01); B60W 40/08 (2012.01);
U.S. Cl.
CPC ...
G06N 3/0454 (2013.01); B60W 40/09 (2013.01); B60W 2040/0809 (2013.01); B60W 2420/42 (2013.01); B60W 2420/52 (2013.01); B60W 2520/105 (2013.01); B60W 2552/00 (2020.02); B60W 2555/20 (2020.02); B60W 2555/60 (2020.02); B60W 2556/50 (2020.02);
Abstract

Methods and apparatus, including computer program products, implementing and using techniques for identifying a driver of a vehicle. Measurement values representing the movement of a vehicle are received from one or more sensors measuring features relating to the movement of the vehicle. Instantaneous dimensions for measuring driver identification are defined. For each dimension statistical features within a given time frame are calculated and a feature map is built, including the time frame and the statistical features. A set of driving features is extracted from the feature map. The set of extracted driving features is compared with previously extracted sets of driving features to create a set of similarity metrics between two drivers. A classification model is trained based on the similarity metrics and is used with the similarity metrics to determine whether data pertaining to a new trip segment should be associated with a known or unknown driver.


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