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:
Oct. 27, 2020

Filed:

Oct. 16, 2018
Applicants:

Toyota Motor Engineering & Manufacturing North America, Inc., Plano, TX (US);

The Regents of the University of Michigan, Ann Arbor, MI (US);

Inventors:

Kuan X. Liu, Ann Arbor, MI (US);

Mike X. Huang, Ann Arbor, MI (US);

Ilya V. Kolmanovsky, Novi, MI (US);

Attorneys:
Primary Examiner:
Int. Cl.
CPC ...
B60W 40/105 (2012.01); B60W 50/00 (2006.01); G05D 1/00 (2006.01); G06N 5/04 (2006.01); G06N 3/08 (2006.01); H04W 4/40 (2018.01); G05D 1/02 (2020.01); G06N 20/00 (2019.01);
U.S. Cl.
CPC ...
B60W 40/105 (2013.01); B60W 50/0097 (2013.01); G05D 1/0088 (2013.01); G05D 1/0221 (2013.01); G06N 3/08 (2013.01); G06N 5/046 (2013.01); G06N 20/00 (2019.01); H04W 4/40 (2018.02); G05D 2201/0213 (2013.01);
Abstract

Some implementations of the disclosure are directed to reducing or removing time lag in vehicle velocity prediction by training a model for vehicle velocity prediction using labeled features that provide indication of a feature associated with a vehicle acceleration or deacceleration event. In one implementation, a method includes: receiving multiple time series datasets, each of the time series datasets including sensor data, GPS data, and vehicle state data collected over time; extracting features from each of the time series datasets that are indicative of a future velocity of a vehicle; labeling the extracted features of each of the time series datasets to indicate vehicle acceleration or deacceleration events; and after labeling the extracted features of each of the time series datasets, using at least a subset of the extracted and labeled time series datasets to train a machine learning model that predicts vehicle velocity some time into the future.


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