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:
Dec. 30, 2025

Filed:

Nov. 17, 2023
Applicant:

Tongji University, Shanghai, CN;

Inventors:

Yanjun Huang, Shanghai, CN;

Jiatong Du, Shanghai, CN;

Shuo Yang, Shanghai, CN;

Yulong Bai, Shanghai, CN;

Xincheng Li, Shanghai, CN;

Hong Chen, Shanghai, CN;

Assignee:

TONGJI UNIVERSITY, Shanghai, CN;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06V 20/58 (2022.01); B60W 50/00 (2006.01); B60W 60/00 (2020.01); G06V 10/80 (2022.01); G06V 10/82 (2022.01);
U.S. Cl.
CPC ...
B60W 60/001 (2020.02); B60W 50/0097 (2013.01); G06V 10/803 (2022.01); G06V 10/82 (2022.01); G06V 20/58 (2022.01); B60W 2050/0028 (2013.01); B60W 2050/0083 (2013.01); B60W 2420/403 (2013.01); B60W 2556/05 (2020.02); B60W 2556/10 (2020.02); B60W 2556/35 (2020.02);
Abstract

The present invention relates to a learning-oriented transferable automatic driving method and system driven by a world model. The method includes the following steps: constructing a simulation environment; collecting a large batch of data in the simulation environment, and collecting a small batch of data in the real environment; constructing a world model, and performing model training in the simulation environment by using the large batch of data, wherein the world model takes a camera image as an input to model an environment by using a generative world model; storing and transmitting historical moment information by hidden variables, and outputting an aerial view and a control instruction; and performing domain adaptive transferring training in the real environment on the basis of the small batch of data, and deploying the model in an autonomous vehicle in the real world to achieve virtuality-to-reality transferring general integrated automatic driving.


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