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. 06, 2026

Filed:

Dec. 29, 2023
Applicant:

Tsinghua University, Beijing, CN;

Inventors:

Sifa Zheng, Beijing, CN;

Ziqing Gu, Beijing, CN;

Haitong MA, Beijing, CN;

Shengbo Li, Beijing, CN;

Jianqiang Wang, Beijing, CN;

Qing Xu, Beijing, CN;

Assignee:

TSINGHUA UNIVERSITY, Beijing, CN;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
B60W 60/00 (2020.01); B60W 50/00 (2006.01); B60W 50/06 (2006.01);
U.S. Cl.
CPC ...
B60W 60/0027 (2020.02); B60W 50/0097 (2013.01); B60W 50/06 (2013.01); B60W 2554/404 (2020.02); B60W 2556/00 (2020.02);
Abstract

A prediction-type intelligent vehicle decision control method and apparatus, a vehicle, and a storage medium are provided. The method includes: establishing a prediction model for surrounding traffic participants based on map information and historical tracks of the traffic participants, and performing parameter initialization on the prediction model by using a labeled data set to generate an initial surrounding vehicle motion prediction model; cyclically updating the initial surrounding vehicle motion prediction model according to continuous interaction data with an environment by taking a driving target of an intelligent vehicle as an optimization object, so as to generate a final surrounding vehicle motion prediction model; and embedding the final surrounding vehicle motion prediction model into a decision control system of the intelligent vehicle, the decision control system generating a decision control instruction according to a predicted surrounding vehicle motion and controlling the intelligent vehicle to execute the decision control instruction.


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