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Author: 張菀容
Chang, Wan-Jung
Thesis Title: 以基於混合多準則決策模式之情境分析與專利探勘定義無人電動車平台
Defining an Autonomous Vehicle Platform by Using Hybrid MCDM Methods Based Scenario Writing and Patent Mining Techniques
Advisor: 黃啟祐
Huang, Chi-Yo
Degree: 碩士
Master
Department: 工業教育學系
Department of Industrial Education
Thesis Publication Year: 2018
Academic Year: 106
Language: 英文
Number of pages: 161
Keywords (in Chinese): 專利探勘關聯規則分析情境分析法無人駕駛車自動駕駛多準則決策分析平台發展
Keywords (in English): Patent Mining, Association Rule Mining, Autonomous Vehicle, Scenario Analysis, Platform Development, Automatic driving, MCDM
DOI URL: http://doi.org/10.6345/THE.NTNU.DIE.041.2018.E01
Thesis Type: Academic thesis/ dissertation
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  • 隨著科技的快速進展,汽車產業將在未來的15年經歷一場大革命,未來全球對於智慧駕駛、自動駕駛與電動車的重視,將會讓車用電子產業站穩更重要的地位。從2009年Google開始測試無人駕駛車後,越來越多科技巨擘及汽車製造商加入這場革命並相爭佈局專利,從光達技術、奈米天線感測技術、人工智慧到已成熟的先進駕駛輔助系統、資通訊娛樂系統等相關技術皆爭相投入大筆資金進行研發。基於未來無人駕駛車將成為市場的主流趨勢,然而目前研究並未揭示針對不同情境下的無人駕駛車平台架構,因此,本研究利用專利探勘技術,針對近幾年所增加無人駕駛車的相關專利文獻進行文字探勘,找出專利的重要關鍵字或隱含的技術關鍵字,並透過關聯規則探勘方法模擬出無人電動車的平台。而後,基於混合多準則決策模式及情境分析法建立不同的情境,並針對所選出的情境,進而分析該情境中的無人電動車的可能著重的技術及平台架構為何。對於後進者發展能大幅降低研發成本及失敗風險,更準確抓住未來技術發展趨勢。

    With the rapid advancement of technology, the world will most certainly experience a revolution in the automotive industry within the next 15 years. This revolution is being driven by the concurrence of electrical vehicles, self-driving cars and driving intelligence, which if the pattern holds true, it would open the door to new leading-edge innovations that would put the automotive industry in a superior position in the future. Since 2009, Google began testing unmanned vehicles with one goal in mind; to build an autonomous vehicle by 2020. With this, other technology giants and car manufactures joined to compete in the growing industry by pouring large sums of money into research and development and obtaining patents including LiDAR technology, Nano-antenna sensing technology, artificial intelligence, Infotainment systems and Advanced Driver Assistance Systems (ADAS). Based on the future of autonomous vehicles there may be a market trend. However, current study does not reveal the existing standard platform architecture in different scenarios for autonomous vehicles. This study use Patent Mining to conduct research by obtaining relevant patent documents of autonomous vehicles in recent years to analyze key findings and information within mainstream technology. Moreover, through the Association Rule Mining method to simulate the platform of the autonomous vehicle. This study will also use Hybrid MCDM and Scenario Analysis to establish various scenarios then analyze and explore the platforms of autonomous vehicles. For late entrants, it may significantly reduce the cost of R & D, risk and enhance accuracy of future technology trends.

    摘要 i Abstract ii Table of Contents iii List of Table v Lists of Figure viii Chapter 1 Introduction 1 1.1 Research Backgrounds 1 1.2 Research Motivations and Problems 3 1.3 Research Objectives and Limitations 4 1.4 Research Method and Framework 6 Chapter 2 Literature Review 9 2.1 Data Mining 9 2.2 Patent Mining 11 2.3 Scenario Analysis 15 2.4 Platform-Based Design 19 Chapter 3 Research Methods 23 3.1 Modified Delphi Method 23 3.2 Decision Making Trial and Evaluation Laboratory 28 3.3 Analytic Network Process (ANP) 32 3.4 DEMATEL based Network Process (DNP) Technique 39 3.5 Association Rules Mining (ARM) 43 Chapter 4 Empirical Study 47 4.1 Autonomous Vehicle 47 4.2 Patent Searching 48 4.2.1 Set Patent Keywords 49 4.2.2 Search for patent 50 4.3 Patent Analysis 51 4.3.1 Text Mining 51 4.3.2 Find out the Correlation 51 4.4 Compose Platform 117 4.5 Steps of Scenario Analysis 120 4.5.1 Define the Scope 120 4.5.2 Key Factors in the Local Environment 121 4.5.3 Driving Forces 125 4.5.4 Degree of Uncertainty and Impact 127 4.5.5 Selecting the Scenario Logics 134 4.5.6 Selecting the Scenarios 139 4.5.7 Fleshing out the Scenarios 141 Chapter 5 Discussion 143 5.1 Combined Scenarios and Platform Deployment 143 5.2 Future Research 145 Chapter 6 Conclusion 147 Reference 149 Appendix A 153 Appendix B 161

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