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:
Apr. 14, 2026

Filed:

Apr. 25, 2023
Applicant:

Shanghai Artificial Intelligence Innovation Center, Shanghai, CN;

Inventors:

Hongyang Li, Shanghai, CN;

Li Chen, Shanghai, CN;

Jiazhi Yang, Shanghai, CN;

Yihan Hu, Shanghai, CN;

Chonghao Sima, Shanghai, CN;

Tianyu Li, Shanghai, CN;

Lewei Lu, Shanghai, CN;

Yu Liu, Shanghai, CN;

Qiang Liu, Shanghai, CN;

Junchi Yan, Shanghai, CN;

Dahua Lin, Shanghai, CN;

Yu Qiao, Shanghai, CN;

Xiaogang Wang, Shanghai, CN;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06T 7/246 (2017.01); B60W 60/00 (2020.01); G01C 21/00 (2006.01); G06T 7/11 (2017.01);
U.S. Cl.
CPC ...
G06T 7/246 (2017.01); G06T 7/11 (2017.01); B60W 60/0027 (2020.02); B60W 2420/403 (2013.01); B60W 2556/40 (2020.02); G01C 21/3811 (2020.08); G06T 2207/30241 (2013.01); G06T 2207/30252 (2013.01);
Abstract

The present application provides a method and a unified framework system for full-stack autonomous driving planning. The method comprises: acquiring an image of a scene and converting the image into an image feature, and converting the image feature into a bird's-eye view feature map; detecting agents from the bird's-eye view feature map through track query vectors, and continuously tracking the agents; segmenting different types of map elements from the bird's-eye view feature map through map query vectors, and continuously updating the map elements; predicting a future trajectory of each agent using an interaction between the agents and the different map elements; predicting, according to the predicted future trajectory of each agent, an occupancy grid map over multi-steps into the future; and decoding an ego-vehicle query vector to generate a planned path of an ego-vehicle, and optimizing the planned path using the predicted future multi-step occupancy grid map.


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