The present disclosure provides a positioning method and system for autonomous driving through Long Short-Term Memory (LSTM)-based Deep Reinforcement Learning (DRL). The method includes: performing normalization preprocessing on a complex environment of autonomous driving based on a Partially Observable Markov Decision Process (POMDP), to acquire a real-time kinematic (RTK) positioning result; inputting the RTK positioning result into an LSTM-based DRL model for correction to acquire an evaluated value of a position correction action; and performing position correction on an autonomous vehicle based on the evaluated value of the position correction action. The system includes a prediction module, a correction module, and an application module. The present disclosure considers that autonomous driving is highly dynamic, temporal, and complex in a complex environment, and generates a more accurate satellite positioning position. The present disclosure can be widely used in the technical field of satellite positioning for autonomous driving.


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    Titel :

    POSITIONING METHOD AND SYSTEM FOR AUTONOMOUS DRIVING THROUGH LONG SHORT-TERM MEMORY (LSTM)-BASED DEEP REINFORCEMENT LEARNING (DRL)


    Beteiligte:
    LI ZHENNI (Autor:in) / XIE SHENGLI (Autor:in) / XIE KAN (Autor:in) / CHEN CI (Autor:in) / KUZIN VICTOR FEDOROVICH (Autor:in) / ZHAO HAOLI (Autor:in) / LI BO (Autor:in)

    Erscheinungsdatum :

    30.05.2024


    Medientyp :

    Patent


    Format :

    Elektronische Ressource


    Sprache :

    Englisch


    Klassifikation :

    IPC:    G01S RADIO DIRECTION-FINDING , Funkpeilung / 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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