Automating reinforcement learning for autonomous vehicles may include assigning a probability with a scenario and varying that probability based at least in part on changes in performance by the autonomous vehicle associated with that scenario. The amount of time and computational bandwidth required to train a machine-learned component of an autonomous vehicle and the accuracy of the machine-learned component may be improved by determining a reward for performance of the autonomous vehicle in a scenario based at least in part on an severity metric. The impact severity metric may be determined based at least in part on a velocity, angle, and/or interaction area associated with the impact.


    Access

    Download


    Export, share and cite



    Title :

    Automated reinforcement learning scenario variation and impact penalties


    Contributors:

    Publication date :

    2024-07-16


    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 / G06F ELECTRIC DIGITAL DATA PROCESSING , Elektrische digitale Datenverarbeitung





    Making a failure scenario using adversarial reinforcement learning background

    WACHI AKIFUMI | European Patent Office | 2022

    Free access

    Automated scenario generation

    Koziarz, W.A. / Krause, L.S. / Lehman, L.A. | Tema Archive | 2003


    OSHA IS INCREASING PENALTIES

    British Library Online Contents | 2016