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:
Mar. 09, 2021

Filed:

Feb. 01, 2019
Applicants:

Baidu Usa Llc, Sunnyvale, CA (US);

Baidu.com Times Technology (Beijing) Co., Ltd., Beijing, CN;

Inventors:

Fan Zhu, Sunnyvale, CA (US);

Lin Ma, Beijing, CN;

Qi Kong, Sunnyvale, CA (US);

Assignees:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G05D 1/02 (2020.01); G05D 1/00 (2006.01); B60W 30/18 (2012.01); B60W 50/00 (2006.01); G05B 13/02 (2006.01);
U.S. Cl.
CPC ...
G05D 1/0221 (2013.01); B60W 30/18 (2013.01); B60W 50/00 (2013.01); G05B 13/0265 (2013.01); G05D 1/0088 (2013.01); B60W 2050/0083 (2013.01); B60W 2520/10 (2013.01); B60W 2520/105 (2013.01); B60W 2540/18 (2013.01);
Abstract

Embodiments of the present disclosure disclose a method for determining a vehicle control parameter, an apparatus for the same, a vehicle controller, and an autonomous vehicle. An embodiment of the method comprises: determining a current vehicle speed and an expected acceleration; determining, from a pre-generated parameter calibration table, a longitudinal control parameter corresponding to the current vehicle speed and the expected acceleration; wherein the parameter calibration table is obtained by: obtaining a training sample set, a training sample in the training sample set including the vehicle speed, the longitudinal control parameter and the acceleration; training a pre-established parameter calibration model with the vehicle speed and acceleration of respective samples in the training sample set as inputs and the longitudinal control parameter value in the training sample as an expected output; and obtaining the parameter calibration table based on the trained parameter calibration model.


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