提高交通效率的常用方法是控制交通信號燈以確保交通暢通,然而由于車輛行為的不可控,實際效果有限。隨著智能網(wǎng)聯(lián)汽車技術(shù)的發(fā)展,交通系統(tǒng)的云端控制中心不僅可以控制交通信號燈,還有可能直接控制車輛。在這種以云端為控制中心的交通管理模式下,云端決策能力是決定交通系統(tǒng)效率的關(guān)鍵因素。云端決策算法將是未來智能交通系統(tǒng)的關(guān)鍵技術(shù)。由于云端決策研究涉及多車協(xié)同,使用真實車輛進行研究的難度和危險性都很高。因此本文將以智能模型車為載體重點研究基于深度強化學(xué)習(xí)的云端決策算法。本文的研究工作可以大致分為三大部分:首先,本文提出了融合視覺與UWB的室內(nèi)定姿定位算法,解決了模擬車位姿信息的準確獲取。目前已有的室內(nèi)定位方法是基于相機的檢測,缺點是對目標的光線和顏色過于敏感,導(dǎo)致檢測定位不夠穩(wěn)定可靠。因此,本文研究了不依賴相機的無線定位方式UWB,構(gòu)建了基站自適應(yīng)選擇的UWB定位系統(tǒng),解決多基站時間難同步和定位精度不穩(wěn)定的問題。在此基礎(chǔ)上,本文進一步研究了實時定位信息與地圖先驗信息的融合定位方法,實現(xiàn)了多目標的位置檢測與跟蹤。本文基于UWB定位系統(tǒng)實現(xiàn)了良好的定位效果,然而無法獲取模型車姿態(tài)信息。因此進一步研究了相機檢... 

【文章來源】：清華大學(xué)北京市 211工程院校 985工程院校 教育部直屬院校

【文章頁數(shù)】：94 頁

【學(xué)位級別】：碩士

【文章目錄】：
摘要
Abstract
Chapter1 Introduction
    1.1 Background
    1.2 Related Work
    1.3 Problem Statement
    1.4 Thesis Outline
Chapter2 Theory of Deep Reinforcement Learning
    2.1 Reinforcement Learning
        2.1.1 Element of Reinforcement Learning
        2.1.2 Markov Decision Process
        2.1.3 Dynamic Programming
        2.1.4 Learning Method
    2.2 Neural Network
        2.2.1 Basics of Neural Network
        2.2.2 Neural Network in Reinforcement Learning(Deep Reinforcement Learning)
Chapter3 The localization based on Fusion of uwb and camera
    3.1 Overall Structure of Localization System
    3.2 UWB Localization
        3.2.1 Ranging Process
        3.2.2 False Ranging Detection
        3.2.3 Solving Trilateration Algorithm
    3.3 Camera Localization
        3.3.1 Fish Eye Calibration
        3.3.2 Camera Detection
    3.4 Sensor Fusion Between Camera,UWB and Map Information
Chapter4 Deep Reinforcement Learning Method
    4.1 Training Environment
        4.1.1 Map Drawing
        4.1.2 Dynamics Model
        4.1.3 Steering Control Model
        4.1.4 State Action Space
        4.1.5 Reward Function
        4.1.6 Termination Stage
    4.2 Reinforcement Learning Models
        4.2.1 Deep Q learning with Experience Replays
        4.2.2 Asynchronous Advantage Actor Critic(A3C)
    4.3 Training Results
        4.3.1 Deep Q learning Network
        4.3.2 Asynchronous Advantage Actor Critic(A3C)
Chapter5 Deep Reinforcement Learning Validation and Evaluation
    5.1 Experimental Setup
    5.2 Motion Control of Intelligent Vehicle
    5.3 Localization Result
    5.4 Validation Decision Making Experiment Result
        5.4.1 Validation of RL Based Decision Through Simulation Software
        5.4.2 Validation of RL based Decision Through Model Cars
Chapter6 Conclusion
References
Acknowledgement
RESUME



本文編號：3406794