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:
Sep. 19, 2023

Filed:

Sep. 09, 2022
Applicant:

Uatc, Llc, Mountain View, CA (US);

Inventors:

Henggang Cui, Allison Park, PA (US);

Junheng Wang, North York, CA;

Sai Bhargav Yalamanchi, Pittsburgh, PA (US);

Mohana Prasad Sathya Moorthy, San Francisco, CA (US);

Fang-Chieh Chou, San Francisco, CA (US);

Nemanja Djuric, Pittsburgh, PA (US);

Assignee:

UATC, LLC, Mountain View, CA (US);

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G05D 1/02 (2020.01); G06N 20/00 (2019.01); B60W 60/00 (2020.01); G06N 3/084 (2023.01);
U.S. Cl.
CPC ...
G05D 1/0221 (2013.01); B60W 60/0011 (2020.02); G06N 3/084 (2013.01); G06N 20/00 (2019.01); G05D 2201/0213 (2013.01);
Abstract

Systems and methods for training machine-learned models are provided. A method can include receiving a rasterized image associated with a training object and generating a predicted trajectory of the training object by inputting the rasterized image into a first machine-learned model. The method can include converting the predicted trajectory into a rasterized trajectory that spatially corresponds to the rasterized image. The method can include utilizing a second machine-learned model to determine an accuracy of the predicted trajectory based on the rasterized trajectory. The method can include determining an overall loss for the first machine-learned model based on the accuracy of the predictive trajectory as determined by the second machine-learned model. The method can include training the first machine-learned model by minimizing the overall loss for the first machine-learned model.


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