An intelligent cockpit task control method based on behavior tree arrangement and multi-source arbitration

The intelligent cockpit task control method using behavior tree orchestration and multi-source arbitration solves the problems of inaccurate triggering, resource conflicts and security risks in intelligent cockpit systems, realizes the stability and security of task execution, ensures the controllable launch of new functions and improves iteration efficiency.

CN122152389APending Publication Date: 2026-06-05CHONGQING LIFAN VEHICLE RESEARCH INSTITUTE CO LTD SHANGHAI BRANCH
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
CHONGQING LIFAN VEHICLE RESEARCH INSTITUTE CO LTD SHANGHAI BRANCH
Filing Date
2026-02-25
Publication Date
2026-06-05

AI Technical Summary

Technical Problem

Existing intelligent cockpit systems suffer from issues such as inaccurate triggering, resource conflicts, security risks, and insufficient functional verification, leading to unstable mission execution and security risks.

Method used

An intelligent cockpit task control method based on behavior tree orchestration and multi-source arbitration is adopted. By constructing a task compiler, shadow verification module, trigger arbitrator, behavior tree engine and execution scheduler, the method realizes the safety boundary retrieval of behavior tree, full-process logic simulation verification and multi-source data arbitration, so as to ensure the stability and security of task execution.

Benefits of technology

It achieves deterministic triggering, atomic execution, and closed-loop security, improving the stability and security of cockpit missions, ensuring that the risks of launching new features are controllable, and improving iteration efficiency.

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Abstract

The application discloses an intelligent cockpit task control method based on a behavior tree arrangement and multi-source arbitration, and the method comprises the following steps: a task compiler performs behavior tree conversion according to a task configuration file, and completes behavior tree building; a shadow verification module performs simulation verification on the behavior tree, and the behavior tree that passes the verification enters a gray-to-normal process; a trigger arbitrator acquires N trigger signals in a vehicle, when K trigger signals in the N trigger signals all satisfy a trigger condition, the trigger arbitrator generates a trigger proof with a time stamp and an event abstract; a behavior tree engine locks the context of this execution according to the trigger proof, activates action nodes corresponding to the trigger signals, and then injects trigger parameters at the trigger time in each action node to generate CAN signal instructions; and an execution scheduler controls corresponding vehicle-mounted physical hardware to run according to the CAN signal instructions. Effects: the method can solve trigger jitter through multi-source arbitration, solve execution safety through behavior tree injection, and solve online risk through a shadow mode.
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