A system for refining a trained autonomous control model is disclosed. The system includes a computing device configured to execute a simulation of a trained autonomous control model for a vehicle model in a simulation environment based on a predefined dataset defining a virtual driving environment and implement a fallback layer configured to detect a failure. In response to the fallback layer detecting the failure of the trained autonomous control model under simulation, the computing device is configured to identify an event in the simulation environment corresponding to the failure of the trained autonomous control model, select additional training data from a data corpus, the additional training data is analogous to the event, and execute a training process to refine the trained autonomous control model using the additional training data such that the trained autonomous control model learns to handle the event with fewer failures.


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    Title :

    ACTIVE LEARNING ON FALLBACK DATA


    Contributors:

    Publication date :

    2024-05-02


    Type of media :

    Patent


    Type of material :

    Electronic Resource


    Language :

    English


    Classification :

    IPC:    B60W CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION , Gemeinsame Steuerung oder Regelung von Fahrzeug-Unteraggregaten verschiedenen Typs oder verschiedener Funktion / G05B Steuer- oder Regelsysteme allgemein , CONTROL OR REGULATING SYSTEMS IN GENERAL



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