Sensor data collected from an autonomous vehicle can be labeled using sensor data collected from an additional vehicle. Labeled sensor data can generate targeted testing instances for a trained machine learning model, where the trained machine learning model is used in generating control signals for an autonomous vehicle. In many implementations, targeted training instances can generate an accuracy value for the trained neural network model. Additionally or alternatively, the sensor suite on the additional vehicle can include a removable hardware pod which can be mounted on a variety of vehicles.


    Access

    Download


    Export, share and cite



    Title :

    Generating Testing Instances for Autonomous Vehicles


    Contributors:

    Publication date :

    2022-01-27


    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 / G07C TIME OR ATTENDANCE REGISTERS , Zeit- oder Anwesenheitskontrollgeräte



    Generating Labeled Training Instances for Autonomous Vehicles

    VALOIS JEAN-SEBASTIEN / PILARSKI THOMAS / MUNOZ DANIEL | European Patent Office | 2022

    Free access

    Generating Labeled Training Instances for Autonomous Vehicles

    VALOIS JEAN-SEBASTIEN / PILARSKI THOMAS / MUNOZ DANIEL | European Patent Office | 2020

    Free access

    Generating labeled training instances for autonomous vehicles

    VALOIS JEAN-SEBASTIEN / PILARSKI THOMAS / MUNOZ DANIEL | European Patent Office | 2022

    Free access

    Generating Labeled Training Instances for Autonomous Vehicles

    VALOIS JEAN-SEBASTIEN / PILARSKI THOMAS / MUNOZ DANIEL | European Patent Office | 2025

    Free access

    GENERATING AND CHARACTERIZING SCENARIOS FOR SAFETY TESTING OF AUTONOMOUS VEHICLES

    Ghodsi, Zahra / Hari, Siva Kumar Sastry / Frosio, Iuri et al. | British Library Conference Proceedings | 2021