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:
Jun. 13, 2023

Filed:

Apr. 15, 2020
Applicant:

Baidu Usa Llc, Sunnyvale, CA (US);

Inventors:

Yu Wang, Sunnyvale, CA (US);

Qi Luo, Sunnyvale, CA (US);

Jiaxuan Xu, Sunnyvale, CA (US);

Jinyun Zhou, Sunnyvale, CA (US);

Shu Jiang, Sunnyvale, CA (US);

Jiaming Tao, Sunnyvale, CA (US);

Yu Cao, Sunnyvale, CA (US);

Wei-Man Lin, Sunnyvale, CA (US);

Kecheng Xu, Sunnyvale, CA (US);

Jinghao Miao, Sunnyvale, CA (US);

Jiangtao Hu, Sunnyvale, CA (US);

Assignee:

BAIDU USA LLC, Sunnyvale, CA (US);

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G06N 20/00 (2019.01); B60W 50/00 (2006.01); B60W 60/00 (2020.01); B60W 50/04 (2006.01); G06F 17/18 (2006.01);
U.S. Cl.
CPC ...
B60W 60/0025 (2020.02); B60W 50/045 (2013.01); G06F 17/18 (2013.01); G06N 20/00 (2019.01); B60W 2050/0014 (2013.01);
Abstract

In one embodiment, a computer-implemented method for optimizing a controller of an autonomous driving vehicle (ADV) includes obtaining several samples, each sample having a set of parameters, iteratively performing, until a predetermined condition is satisfied: determining, for each sample, a score according to a configuration of the controller based on the set of parameters of the sample, applying a machine learning model to the samples and corresponding scores to determine a mean function and a variance function, producing a new sample as a minimum of a function of the mean function and the variance function with respect to an input space of the set of parameters, adding the new sample to the several samples, and outputting the new sample as an optimal sample, where parameters of the optimal sample are utilized to configure the controller to autonomously drive the ADV.


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