An imitation learning-based machine-learned (ML) model to augment or replace the prediction and/or planner components of an autonomous vehicle may be trained using a two stage and multi-discipline approach. A first stage of training may include training the ML component to output a predicted action associated with a target vehicle and modifying the ML component to reduce a difference between the predicted action and the observed action taken by the target vehicle. A second stage may use reinforcement learning to further tun the ML component. The resultant model may be used on its own, with enough training data, or to rank or weight candidate trajectories generated by a planning component of the vehicle. The ML component may provide embeddings of environment features to first transformer(s) that output to a long short-term memory that outputs to second transformer(s) to determine the predicted action.


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    Title :

    Machine-learned component hybrid training and assistance of vehicle trajectory generation


    Contributors:

    Publication date :

    2024-10-15


    Type of media :

    Patent


    Type of material :

    Electronic Resource


    Language :

    English


    Classification :

    IPC:    G06V / 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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