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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 傅立成 | |
| dc.contributor.author | Kuo-Hsin Tu | en |
| dc.contributor.author | 塗國星 | zh_TW |
| dc.date.accessioned | 2021-05-13T06:39:07Z | - |
| dc.date.available | 2020-08-24 | |
| dc.date.available | 2021-05-13T06:39:07Z | - |
| dc.date.copyright | 2017-08-24 | |
| dc.date.issued | 2017 | |
| dc.date.submitted | 2017-08-16 | |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/2313 | - |
| dc.description.abstract | 在視覺式自動駕駛系統中，感知與控制是兩個重要且待解決的議題。此外，由於深度卷積神經網路在解決感知與控制問題上有非常好能力，使得深度卷積神經網路成為視覺式自動駕駛系統的解決方案之一。在本論文中，我們證明語義分割可以用來提升視覺式自動駕駛系統的效能。論文中提出了一個使用語義感知並基於端對端深度卷積神經網路的方法來解決自動駕駛中的視覺式控制問題。所提出的方法具有兩個階段並透過影像輸入來預測汽車轉向操控。在第一個階段中，使用一個深度卷積神經網路從輸入影像產生語義分割的結果，在第二個階段中則使用另一個深度卷積神經網路從語義分割資訊來預測出汽車轉向操控。在實驗中，我們使用一個公開的汽車駕駛資料集來評估所提出的方法，實驗結果顯示該方法能達到比一般端對端的深度卷積神經網路方法更好的結果。 | zh_TW |
| dc.description.abstract | In vision based autonomous driving systems, perception and control tasks are two critical problems to be solved. The effectiveness of deep convolutional neural networks (CNNs) in solving visual perception and control tasks has made CNNs a desirable solution for autonomous driving. In this thesis, we show that semantic segmentation can be applied to enhance the performance of a vision based autonomous driving system. We propose an end-to-end CNN architecture with semantic perception to solve the vision based control problem in autonomous driving. The proposed approach is a two-stage CNN architecture that takes a monocular image and outputs a steering angle. In the first stage, a CNN module is used to generate semantic segmentation from the input image. In the second stage, another CNN module is used to take advantage of the semantic perception to predict steering angles. In the experiment, a publicly available dataset of human driving data is used to evaluate the proposed method. Experimental results demonstrate that the proposed method enhance the results of the typical end-to-end CNN approach. | en |
| dc.description.provenance | Made available in DSpace on 2021-05-13T06:39:07Z (GMT). No. of bitstreams: 1 ntu-106-P04922004-1.pdf: 2959469 bytes, checksum: 1b0b0ba368fa10312e7ccc848c4b14d9 (MD5) Previous issue date: 2017 | en |
| dc.description.tableofcontents | 誌謝 i
中文摘要 ii Abstract iv Contents v List of Figures vii List of Tables ix Chapter 1 Introduction 1 1.1 Motivation 1 1.2 Related Work 4 1.3 Contributions 7 1.4 Thesis Organization 7 Chapter 2 Preliminaries 9 2.1 Vision-based Autonomous Driving Systems 9 2.2 Imitation Learning 10 2.3 Transfer Learning 11 2.4 Convolutional Neural Networks (CNNs) 13 2.5 Semantic Segmentation 19 Chapter 3 Methodology 23 3.1 System Overview 23 3.2 Semantic Segmentation Generation 24 3.2.1 Encoder Network 25 3.2.2 Decoder Network 27 3.3 Car Steering Angle Prediction 30 Chapter 4 Experiments 34 4.1 Environments 34 4.2 Dataset 35 4.2.1 Cityscapes Dataset 36 4.2.2 Udacity Self-Driving Car Challenge 2 Dataset 35 4.3 Evaluation Metrics 38 4.4 Implementation Details 38 4.4.1 Semantic Segmentation Annotation for Udacity Dataset 38 4.4.2 Baseline Model 39 4.4.3 Perception Network 39 4.4.4 Control Network 40 4.5 Results 40 4.5.1 Overall Performance 40 4.5.2 Analysis of Error Cases 43 4.5.3 Effects of Different Perception Network Models 54 Chapter 5 Conclusion 56 References 58 | |
| dc.language.iso | en | |
| dc.subject | 車輛轉向 | zh_TW |
| dc.subject | 深度學習 | zh_TW |
| dc.subject | 卷積神經網路 | zh_TW |
| dc.subject | 語義分割 | zh_TW |
| dc.subject | 自動駕駛 | zh_TW |
| dc.subject | Deep learning | en |
| dc.subject | Vehicle steering | en |
| dc.subject | Autonomous driving | en |
| dc.subject | Semantic segmentation | en |
| dc.subject | Convolutional neural networks | en |
| dc.title | 基於深度學習語義分割之城市道路汽車轉向操控 | zh_TW |
| dc.title | A Deep Learning Based Semantic Segmentation Approach
for Car Steering on Urban Roads | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 105-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.coadvisor | 蕭培墉 | |
| dc.contributor.oralexamcommittee | 傅楸善,黃世勳,方瓊瑤 | |
| dc.subject.keyword | 深度學習,卷積神經網路,語義分割,自動駕駛,車輛轉向, | zh_TW |
| dc.subject.keyword | Deep learning,Convolutional neural networks,Semantic segmentation,Autonomous driving,Vehicle steering, | en |
| dc.relation.page | 62 | |
| dc.identifier.doi | 10.6342/NTU201703573 | |
| dc.rights.note | 同意授權(全球公開) | |
| dc.date.accepted | 2017-08-17 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 資訊工程學研究所 | zh_TW |
| 顯示於系所單位： | 資訊工程學系 | |
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