自動(dòng)駕駛（AD）是提升舒適性,道路安全性和效率的最新興領(lǐng)域。自動(dòng)駕駛基于對人類駕駛員行為的研究。駕駛行為是駕駛員在不同環(huán)境下,加速和減速,速度,剎車踏板,經(jīng)度和橫向運(yùn)動(dòng)以及車輛之間的最小安全距離方面的概念性研究。本文大綱基于以人為中心的自動(dòng)駕駛汽車。我們特別關(guān)注換道時(shí)的交通流量的安全性與效率。現(xiàn)代社會(huì)要求安全無障礙道路。根據(jù)車聯(lián)網(wǎng)（VANET）的各種用途所需的特定需求,在大多數(shù)情況下進(jìn)行換道,設(shè)計(jì)穩(wěn)定的避撞（CA）和安全車道變換已經(jīng)轉(zhuǎn)變?yōu)樵诿芗h(huán)境中控制車輛的基礎(chǔ)。為了減少現(xiàn)代交通系統(tǒng)中的碰撞并提高效率,高效的嵌入式系統(tǒng)和汽車之間進(jìn)行有效的通信是非常重要。本文將VANET和車載系統(tǒng)安裝在一起,以實(shí)現(xiàn)我們的目標(biāo)。本文根據(jù),速度和各類車輛之間的距離,提出了高效的換道選擇和無碰撞的車輛運(yùn)輸系統(tǒng)。在本文中,我們首先描述了基于耐心的策略,并利用博弈論方法重新確立了交通流的安全性和效率。然后,我們利用從車輛通信中得到的汽車的距離和速度信息,改進(jìn)了用于車輛換道和碰撞控制的算法。本文算法在分析了兩種不同的軌道變換和無碰撞駕駛算法之后;考慮各種交通密度,形成基于隨機(jī)耐心的算法和汽車跟隨算法。因此,在實(shí)驗(yàn)... 

【文章來源】：大連理工大學(xué)遼寧省 211工程院校 985工程院校 教育部直屬院校

【文章頁數(shù)】：65 頁

【學(xué)位級別】：碩士

【文章目錄】：
摘要
Abstract
1 Introduction
    1.1 Research Background
    1.2 Research Objective and Significance
        1.2.1 Research Objective
        1.2.2 Research Significance
    1.3 Domestic and Overseas Progress
        1.3.1 Significance of Artificial Intelligence in Lane Change Maneuvering
        1.3.2 The Recent Development of VANET in Autonomous Vehicles
        1.3.3 Existing Methods to Overcome Safety and Efficiency Issues
    1.4 Story Outline and Methodology of Dissertation
        1.4.1 Story Outline
        1.4.2 Research Methods
2 Challenges and Existing Models
    2.1 Characterization of Driving Automation
    2.2 Challenges in Driving Automation
        2.2.1 Collision Avoidance and Trajectory Generation
        2.2.2 Drivers Behavior Prediction
        2.2.3 Intend Recognition and Skill Learning
        2.2.4 Detection of Potential Threats
        2.2.5 Minimum Safety Spacing
    2.3 Existing Methods to Overcome Safety and Efficiency Issues
        2.3.1 Stochastic Switched Autoregressive Exogenous
        2.3.2 Cerebellar Model Articulation Controller（CMAC）
        2.3.3 Hidden Markov Model
        2.3.4 Neural Network
        2.3.5 Gauss Mixture Model
        2.3.6 Fuzzy System
        2.3.7 Bayesian Network
        2.3.8 Support Vector Machine
        2.3.9 Car-Following Model
3 Set up a Trajectory Planning Model via Artificial Neural Network and VANET
    3.1 Lane Change Analysis and Evaluation
        3.1.1 Lane Change Maneuvering
        3.1.2 General Methods of Lane Change Maneuvering
    3.2 Setup Safety Model via VANET
        3.2.1 Data Collection and Individual Situation Analysis
        3.2.2 Cooperative Situation Analysis
        3.2.3 Disseminated Knowledge Management
        3.2.4 Individual Consequences and Driver Interface
        3.2.5 Consistent Assessment of the Existing Driving Situation
        3.2.6 Communication and Information Broadcasting
    3.3 Learning Approach in Bayesian Networks
        3.3.1 Parameter learning
        3.3.2 Structure Learning
4 Bayesian Structure Employment
    4.1 Data Preprocessing
    4.2 Traffic Scenario
    4.3 Application of Bayesian Network in Lane Change Maneuvering
        4.3.1 Pre Mature Model
        4.3.2 Mathematical Model Setup
        4.3.3 Algorithm Design
    4.4 Summary
5 Experimental Setup and Results
    5.1 Simulation Environment
    5.2 Simulation result
        5.2.1 Input data
        5.2.2 Output/results
        5.2.3 For Safety Application
        5.2.4 Improvement in Traffic Efficiency in the traffic
Conclusions
References
Research Projects and Publications in Master Study
Acknowledgments


【參考文獻(xiàn)】：
期刊論文
[1]應(yīng)用于換道預(yù)警的駕駛風(fēng)格分類方法[J]. 王暢,付銳,彭金栓,毛錦.  交通運(yùn)輸系統(tǒng)工程與信息. 2014(03)



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