{
  "$type": "site.standard.document",
  "description": "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…",
  "path": "/patents/1364181",
  "publishedAt": "2024-05-30T00:00:00.000Z",
  "site": "at://did:plc:oql6ds5vnff4ugar6rruliwd/site.standard.publication/3mn3ohu7oxx5w",
  "tags": [
    "B60W60/001",
    "GUANGDONG UNIVERSITY OF TECHNOLOGY"
  ],
  "textContent": "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.",
  "title": "POSITIONING METHOD AND SYSTEM FOR AUTONOMOUS DRIVING  THROUGH LONG SHORT-TERM MEMORY (LSTM)-BASED DEEP REINFORCEMENT LEARNING (DRL)"
}