Multi-model target state prediction method and system based on deep reinforcement learning

A technology of reinforcement learning and target state, applied in radio wave measurement system, calculation model, machine learning, etc., can solve problems such as large prediction error, inability to cope with prediction accuracy, and short predictable time, so as to improve the length of time and improve the goal Effect of State Prediction Accuracy

Inactive Publication Date: 2020-07-10
TSINGHUA UNIV
View PDF4 Cites 7 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, prediction methods based on behavioral cognition have large prediction errors in a short period of time
[0004] At this stage, for intelligent driving technology, the target trajectory prediction has the following problems: 1) the prediction acc...

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Multi-model target state prediction method and system based on deep reinforcement learning
  • Multi-model target state prediction method and system based on deep reinforcement learning
  • Multi-model target state prediction method and system based on deep reinforcement learning

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0025] The present invention will be described in further detail below in conjunction with the embodiments given in the accompanying drawings.

[0026] Such as figure 1 As shown, the environmental target state scene environment involved in the present invention includes target T, historical states St (t=0, 1, 2...N) of each target, road physical boundary information, and road line marking information. The target T mainly includes: historical position information Pt of the target (t=0, 1, 2...N), historical shape information Lt of the target (t=0, 1, 2...N), historical orientation information Ot of the target (t= 0,1,2...N), historical speed information Vt of the target (t=0,1,2...N). Road physical boundary information Bt indicates impassable road boundaries, such as road railings, curbs, cliffs, etc. The road line marking information Mt represents the artificially drawn marking lines in the traffic rules, such as real lane lines, virtual lane lines, zebra crossings, stop lin...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention discloses a multi-model target state prediction method based on a deep neural network and reinforcement learning, effectively solves the problems of complex strong nonlinear environmentexpression of multi-model fusion and long-time prediction of a target state, facilitates the improvement of the prediction precision and prediction duration of the target state, and achieves the online parameter self-correction of multi-model fusion. The calibration method provided by the invention has the advantages: 1) adapting to environmental scene changes and self-adjusting multi-model targetstate prediction parameters; 2) improving the target state prediction precision; 3) improving the target state prediction time length; 4) performing online self-learning the multi-model prediction parameters and improving the target state prediction precision in a dynamic scene.

Description

technical field [0001] The present invention relates to an intelligent driving system-oriented target state prediction method and system, in particular to a multi-model target state prediction method and system based on deep reinforcement learning. Background technique [0002] Intelligent driving vehicles have a positive effect on traffic safety, traffic efficiency, environmental protection and energy saving. Intelligent driving vehicles use the perception system to sense the parameters of the driving environment and identify the type of target; through the cognitive system, the understanding of the driving environment, such as the understanding of driving behavior intentions, is improved, and future environmental changes are estimated and predicted, and other road users such as The decision-making mechanism of vehicles and pedestrians makes a correct understanding of the environment; completes driving behavior and path planning through the decision-making system and execut...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
IPC IPC(8): G06K9/62G06K9/00G06N20/00G01S7/41G01S7/48
CPCG06N20/00G01S7/417G01S7/4802G06V20/56G06F18/25
Inventor 谢国涛王晓伟秦晓辉徐彪边有钢胡满江杨泽宇周华健钟志华
Owner TSINGHUA UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap