近年來(lái),深度學(xué)習(xí)技術(shù)的研究促進(jìn)了人工智能在學(xué)術(shù)界和工業(yè)界的發(fā)展。深度學(xué)習(xí)算法起源于人工神經(jīng)網(wǎng)絡(luò),為多層神經(jīng)網(wǎng)絡(luò)在實(shí)際中的應(yīng)用提供了一種有效的途徑。得益于互聯(lián)網(wǎng)推動(dòng)下大數(shù)據(jù)的積累,以及基于圖形處理單元(GPU)的并行計(jì)算能力的提升,兩者正在促進(jìn)深度學(xué)習(xí)算法應(yīng)用到更廣泛的領(lǐng)域,如無(wú)人駕駛。無(wú)人駕駛因能帶了更安全的駕駛體驗(yàn),降低交通事故發(fā)生率,并能有效減少城市中的交通擁擠而受到廣泛關(guān)注。無(wú)人駕駛汽車(chē)是一個(gè)復(fù)雜的系統(tǒng),而視覺(jué)感知是無(wú)人駕駛中很重要的一個(gè)組成部分。無(wú)人駕駛中的視覺(jué)感知負(fù)責(zé)理解周?chē)h(huán)境中道路、車(chē)輛及行人等。在道路檢測(cè)方面,傳統(tǒng)方法多聚焦于結(jié)構(gòu)化道路和單一道路的情況。但當(dāng)無(wú)人駕駛汽車(chē)行駛在自然環(huán)境中,它通常需要應(yīng)對(duì)更復(fù)雜的道路條件,如邊界模糊,凹凸不平的道路,有樹(shù)陰遮擋的道路,有多條道路同時(shí)存在的路口情況等。另一方面,傳統(tǒng)算法通常采用淺層的特征提取用于物體的檢測(cè)與識(shí)別。而淺層特征難以表達(dá)物體本身具有的抽象特征,因此難以應(yīng)對(duì)同類(lèi)物體中多變的形態(tài),并且難以區(qū)分不同物體中相似的特征。另外,無(wú)人駕駛中的實(shí)時(shí)性要求也限制了計(jì)算復(fù)雜度較高的傳統(tǒng)方法在實(shí)際場(chǎng)景中的應(yīng)用。鑒于由多層神經(jīng)網(wǎng)絡(luò)計(jì)算出來(lái)的...

【文章頁(yè)數(shù)】：155 頁(yè)

【學(xué)位級(jí)別】：博士

【文章目錄】：
Abstract
摘要
List of Abbreviations
Chapter 1 Introduction
    1.1 Background
        1.1.1 Introduction of Self-driving Vehicle
        1.1.2 Visual Perception In Self-driving Vehicle System
        1.1.3 Road Detection
        1.1.4 Object Detection and Recognition
    1.2 Problems and Challenges
        1.2.1 Road Detection
        1.2.2 Object Detection and Recognition
    1.3 Dissertation Outline
Chapter 2 Fundamentals of Deep Learning
    2.1 Introduction
    2.2 Neural Network in Early Stage
        2.2.1 Multilayer Perception
        2.2.2 Restricted Boltzmann Machine
    2.3 Convolutional Neural Network
        2.3.1 Basic Modules in CNN structures
        2.3.2 Typical CNN-Based Models for Object Recognition
        2.3.3 Platforms for CNN Model Development
    2.4 Conclusion
Chapter 3 CNN-based Road-direction Point Detection
    3.1 Introduction
    3.2 Proposed Method
        3.2.1 Road-direction Point Representation
        3.2.2 Design of Road-direction Point Detection Model
        3.2.3 Loss function
        3.2.4 Convolutional Neural Network Structure
        3.2.5 Non-maximum suppression
        3.2.6 Training of This Model
    3.3 Simulation Results
        3.3.1 Design of Dataset About Road
        3.3.2 Model Simulation
        3.3.3 Performance Evaluation
        3.3.4 Performance Comparison
        3.3.5 Runtime Comparison
    3.4 Discussion
    3.5 Conclusion
Chapter 4 CNN-based Multiple Road-Points Detection
    4.1 Introduction
    4.2 Proposed Method
        4.2.1 Road Representation by Road Points
        4.2.2 Design of Road-Points Detection Model
        4.2.3 Loss Function
        4.2.4 Convolutional Neural Network Structure
        4.2.5 Non-maximum Suppression
        4.2.6 Training of This Model
        4.2.7 Metric Definition for Model Performance
    4.3 Simulation Results
        4.3.1 Design of Road Dataset
        4.3.2 Model Simulation
        4.3.3 Error Analysis
        4.3.4 Mean Performance on Different Categories
        4.3.5 Performance Comparison
        4.3.6 Runtime Comparison
    4.4 Conclusion
Chapter 5 Road-direction Point based Car Detection
    5.1 Introduction
    5.2 Proposed Method
        5.2.1 CNN-based Model with Sub-regions
        5.2.2 Information Integration
        5.2.3 Loss Function
        5.2.4 Convolutional Neural Network Structure
        5.2.5 Training of this model
    5.3 Simulatoin Results
        5.3.1 Preparation of Dataset
        5.3.2 Model Simulation
        5.3.3 Model Performance
        5.3.4 Analysis of Model Performance
        5.3.5 Runtime Comparison
    5.4 Conclusion
Chapter 6 Research on Invariant Object Recognition
    6.1 Introduction
    6.2 Proposed Method
        6.2.1 Sparse Deep Belief Network
        6.2.2 V2 Features Detection with 2-Stage DBN
        6.2.3 SOM Model with Trace Rule
        6.2.4 Metric Method for Model Performance
    6.3 Simulation Results
        6.3.1 Design of Simulation
        6.3.2 Influence of Trace Rule on Performance
        6.3.3 Influence of the Number of SOM Layers
        6.3.4 Influence of Random Order on Performance
        6.3.5 Comparison of Firing Rate of SSI-Top neurons
        6.3.6 Application of Learned SOM Layer
    6.4 Discussion
    6.5 Conclusion
Chapter 7 Conclusions and Future Works
    7.1 Conclusions
    7.2 Future Works
Appendix
References
Acknowledgements
Biography



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