Described herein are a method of training a learning-based critic for tuning a rule-based motion planner of an autonomous driving vehicle, a method of tuning a motion planner using an automatic tuning framework that with the learning-based critic. The method includes receiving training data that incudes human driving trajectories and random trajectories derived from the human driving trajectories; training a learning-based critic using the training data; identifying a set of discrepant trajectories by comparing a first set of trajectories, and a second set of trajectories; and refining, at the neural network training platform, the learning-based critic based on the set of discrepant trajectories. The automatic tuning framework can remove human efforts in tedious parameter tuning, reduce tuning time, while retaining the physical and safety constraints of the ruled-based motion planner. Further, the automatic tuning framework can create personalized motion planners when the learning-based critic is trained using different human driving datasets.


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    Title :

    LEARNING-BASED CRITIC FOR TUNING A MOTION PLANNER OF AUTONOMOUS DRIVING VEHICLE


    Contributors:
    JIANG SHU (author) / XIONG ZIKANG (author) / LIN WEIMAN (author) / CAO YU (author) / LUO QI (author) / HU JIANGTAO (author) / MIAO JINGHAO (author)

    Publication date :

    2023-05-25


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



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