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:
Apr. 16, 2024

Filed:

Jul. 28, 2021
Applicant:

Argo Ai, Llc, Pittsburgh, PA (US);

Inventors:

Michael Nayhouse, Pittsburgh, PA (US);

Michael Pacilio, Pittsburgh, PA (US);

Thomas Carl Ackenhausen, Ann Arbor, MI (US);

Allen Edward Flick, Pittsburgh, PA (US);

Assignee:

Argo AI, LLC, Pittsburgh, PA (US);

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
B60W 30/095 (2012.01); B60W 30/18 (2012.01); G05D 1/00 (2024.01);
U.S. Cl.
CPC ...
G05D 1/0221 (2013.01); B60W 30/095 (2013.01); B60W 30/18163 (2013.01); G05D 1/0044 (2013.01); G05D 1/0088 (2013.01); G05D 1/0219 (2013.01); G05D 1/0238 (2013.01); B60W 2420/403 (2013.01); B60W 2420/408 (2024.01);
Abstract

Method and systems for generating vehicle motion planning model simulation scenarios are disclosed. The system receives a base simulation scenario with features of a scene through which a vehicle may travel. The system then generates an augmentation element with a simulated behavior for an object in the scene by: (i) accessing a data store in which behavior probabilities are mapped to object types to retrieve a set of behavior probabilities for the object; and (ii) applying a randomization function to the behavior probabilities to select the simulated behavior for the object. The system will add the augmentation element to the base simulation scenario at the interaction zone to yield an augmented simulation scenario. The system will then apply the augmented simulation scenario to an autonomous vehicle motion planning model to train the motion planning model.


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