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:
Jul. 01, 2025

Filed:

Mar. 10, 2025
Applicant:

Tongji University, Shanghai, CN;

Inventors:

Jian Sun, Shanghai, CN;

Peng Hang, Shanghai, CN;

Shiyu Fang, Shanghai, CN;

Assignee:

TONGJI UNIVERSITY, Shanghai, CN;

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G06N 7/01 (2023.01); G06N 3/006 (2023.01); G06N 3/092 (2023.01); G06N 3/094 (2023.01); G06N 20/00 (2019.01);
U.S. Cl.
CPC ...
G06N 7/01 (2023.01); G06N 3/006 (2013.01); B60K 2360/175 (2024.01); B60Q 2800/10 (2022.05); G05B 2219/39146 (2013.01); G06N 3/092 (2023.01); G06N 3/094 (2023.01); G06N 20/00 (2019.01);
Abstract

The present invention relates to an autonomous driving test method based on multi-agent swarm adversarial, a device and a medium. The method includes: deducing a conflict topological relationship graph between a tested autonomous vehicle and an agent; deducing a feasible planning space of the tested autonomous vehicle according to the conflict topological relationship graph; establishing a multi-agent swarm adversarial model based on a potential game under the feasible planning space according to a correlation between an individual reward of the agent and a swarm adversarial test effect of a multi-agent system, and solving and obtaining an optimal adversarial strategy of the multi-agent system against the tested autonomous vehicle, where in the multi-agent swarm adversarial model, an adversarial intensity is introduced, and the adversarial intensity is adaptively adjusted according to an actual response of the tested autonomous vehicle; and repeatedly executing the S-Suntil an adversarial task is completed.


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