Intelligent agent cognitive decision-making integrated method and system based on wm and mlm

By using an integrated cognitive decision-making method combining WM and MLM, a game intention distribution map of interactive objects is generated and multi-step deduction is performed, which solves the problems of information delay and error accumulation in traditional autonomous driving and enables efficient and safe decision-making in complex traffic environments.

CN122166152APending Publication Date: 2026-06-09广州云趣信息科技有限公司
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
广州云趣信息科技有限公司
Filing Date
2026-05-12
Publication Date
2026-06-09

AI Technical Summary

Technical Problem

Traditional autonomous driving methods suffer from information transmission delays and error accumulation in modular pipeline architectures, limited ability to model dynamic interactive game scenarios, and lack of online adaptive calibration mechanisms. This leads to reduced decision robustness in complex traffic environments and makes it difficult to meet real-time and safety requirements.

Method used

An integrated cognitive decision-making method based on WM and MLM is adopted. The distribution map of the game intentions of interactive objects is generated by the traffic interaction situation coding network, which drives the time-series game inference network to perform multi-step inference, constructs the arbitration logic of conflict urgency, and optimizes the decision-making system through an online calibration mechanism.

Benefits of technology

It improves the accuracy of key target selection in complex traffic scenarios, achieves a dynamic balance between safety and traffic efficiency, enhances the system's robustness and self-evolution capability in long-term operation, and shortens the reaction delay from perception to execution.

✦ Generated by Eureka AI based on patent content.

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Abstract

The application relates to an agent cognitive decision integration method and system based on WM and MLM, and relates to the technical field of automatic driving, which comprises the following steps: acquiring self-vehicle sensing data and trajectories of traffic participants broadcast by a roadside unit, generating an interactive object game intention distribution graph through mask self-attention cross coding; taking the graph as a search boundary, driving a time sequence game deduction network to deduce, generating a collision time margin decay sequence of a candidate action and an equivalent passing deceleration; constructing conflict arbitration logic, outputting a risk avoidance instruction when the minimum value of the margin is lower than a linear mapping threshold, otherwise outputting an interactive instruction; combining the self-vehicle sensing data to generate an executable driving behavior; collecting the cumulative deviation of real trajectories and deduced trajectories, and reversely calibrating the time sequence game deduction network and the traffic interaction situation coding network. The application solves the technical problem that traditional methods lack inference ability for dynamic interaction intention, resulting in insufficient decision robustness, out-of-control safety and efficiency in strong game scenarios.
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