A machine-learned model that uses sensor and/or perception data to directly determine controls for operating an autonomous vehicle may be trained by identifying a preferred trajectory between a human-driven and vehicle-controlled trajectory, and using a first loss determined between the vehicle-controlled trajectory and the path the autonomous vehicle ultimately ended up taking in a scenario and a second loss determined between the vehicle-controlled trajectory and the human-driven trajectory to refine the machine-learned model. The machine-learned model may additionally or alternatively be refined by a learned reward model constructed by replacing one or more output heads of the machine-learned model with a regression head that is trained using performance metrics determined for the vehicle-controlled trajectory.


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    Title :

    REFINEMENT TRAINING FOR MACHINE-LEARNED VEHICLE CONTROL MODEL


    Contributors:

    Publication date :

    2025-05-29


    Type of media :

    Patent


    Type of material :

    Electronic Resource


    Language :

    English


    Classification :

    IPC:    G06N COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS , Rechnersysteme, basierend auf spezifischen Rechenmodellen / 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



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