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:
Oct. 15, 2024

Filed:

Dec. 13, 2023
Applicant:

Tongji University, Shanghai, CN;

Inventors:

Hong Chen, Shanghai, CN;

Lin Zhang, Shanghai, CN;

Rongjie Yu, Shanghai, CN;

Qiang Meng, Shanghai, CN;

Jinlong Hong, Shanghai, CN;

Assignee:

TONGJI UNIVERSITY, Shanghai, CN;

Attorneys:
Primary Examiner:
Int. Cl.
CPC ...
B60W 60/00 (2020.01); B60W 40/04 (2006.01); B60W 50/00 (2006.01); G06N 3/04 (2023.01);
U.S. Cl.
CPC ...
B60W 60/00274 (2020.02); B60W 40/04 (2013.01); B60W 50/0097 (2013.01); G06N 3/04 (2013.01); B60W 2554/4041 (2020.02); B60W 2554/4045 (2020.02); B60W 2556/10 (2020.02);
Abstract

Disclosed is a decision-making and planning integrated method for a nonconservative intelligent vehicle in a complex heterogeneous environment, including the following steps: offline establishing and training a social interaction knowledge learning model; obtaining state data of the traffic participants and state data of an intelligent vehicle online in real time, and splicing the state data to obtain an environmental state; using the environmental state as an input to the trained social interaction knowledge learning model to obtain predicted trajectories of all traffic participants including the nonconservative intelligent vehicle; updating the environmental state based on the predicted trajectories; and inputting the updated environmental state to the social interaction knowledge learning model to complete trajectory decision-making and planning for the nonconservative intelligent vehicle by iteration, where a planned trajectory of the nonconservative intelligent vehicle is a splicing result of a first point of a predicted trajectory obtained by each iteration.


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