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.
POSITIONING METHOD AND SYSTEM FOR AUTONOMOUS DRIVING THROUGH LONG SHORT-TERM MEMORY (LSTM)-BASED DEEP REINFORCEMENT LEARNING (DRL)
2024-05-30
Patent
Electronic Resource
English
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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