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:
Jan. 05, 2021

Filed:

Sep. 13, 2018
Applicant:

Toyota Research Institute, Inc., Los Altos, CA (US);

Inventors:

Guy Rosman, Newton, MA (US);

Jonathan DeCastro, Arlington, MA (US);

Nikos Arechiga Gonzalez, San Mateo, CA (US);

John Joseph Leonard, Newton, MA (US);

Luke S. Fletcher, Cambridge, MA (US);

Daniel Stonier, Lexington, MA (US);

Assignee:

Toyota Research Institute, Inc., Los Altos, CA (US);

Attorneys:
Primary Examiner:
Int. Cl.
CPC ...
B60W 30/095 (2012.01); B60W 30/09 (2012.01); G06K 9/00 (2006.01); G05D 1/00 (2006.01); G06N 7/00 (2006.01);
U.S. Cl.
CPC ...
B60W 30/0956 (2013.01); B60W 30/09 (2013.01); G05D 1/0088 (2013.01); G06K 9/00805 (2013.01); G06N 7/005 (2013.01);
Abstract

System, methods, and other embodiments described herein relate to modeling dynamic agents in a surrounding environment of an ego vehicle. In one embodiment, a method includes, in response to receiving sensor data including present observations of a road agent of the dynamic agents in the surrounding environment, identifying previous observations of the road agent from an electronic data store. The method includes estimating a future state of the road agent using at least the present observations and the previous observations of the road agent to compute the future state according to a probabilistic model comprised of a transition model that accounts for dynamic behaviors of the road agent to characterize transitions between states, and an agent model that accounts for actions of the road agent. The method includes controlling one or more vehicle systems of the ego vehicle according to the future state of the road agent.


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