Method of training a machine learning (ML) perception model of an autonomy stack controlling an autonomous vehicle. The autonomy stack has a first processor component for generating a coarse reference trajectory based on detected surrounding obstacles, and a second processor component for refining the trajectory based on perception data, the perception module of the second processor including logic rule-based perception algorithm models and ML perception models. The training method involves identifying objects using rule-based perception model, labelling and annotating the objects, and training the ML perception model. Occupancy grid may be generated, labelling a cell being in occupied, occluded, or free-space state. Occupancy grids of future or previous time points may be temporally paired, such that the grid may include velocity as a state.


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    Title :

    Generating a trajectory for an autonomous vehicle


    Contributors:
    ANDREW ENGLISH (author) / NORINA RATIU (author) / CHI TONG (author) / BEN UPCROFT (author)

    Publication date :

    2024-06-26


    Type of media :

    Patent


    Type of material :

    Electronic Resource


    Language :

    English


    Classification :

    IPC:    G06V / 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



    Generating a trajectory for an autonomous vehicle

    ANDREW ENGLISH / CHI HAY TONG / BEN UPCROFT | European Patent Office | 2024

    Free access

    Generating a trajectory for an autonomous vehicle

    ANDREW ENGLISH / NORINA RATIU / CHI HAY TONG et al. | European Patent Office | 2024

    Free access

    Generating a trajectory for an autonomous vehicle

    ANDREW ENGLISH / NORINA RATIU / CHI TONG et al. | European Patent Office | 2024

    Free access

    Generating a trajectory for an autonomous vehicle

    ANDREW ENGLISH / NORINA RATIU / CHI TONG et al. | European Patent Office | 2024

    Free access