臺灣博碩士論文加值系統

由於自動駕駛技術的逐漸發展，自動駕駛車產品化及商業化的可能性近年來已被廣泛討論，為掌握參與自動駕駛技術開發相關企業間的知識流動、自動駕駛技術的發展演化，以及自動駕駛涉及的各種專利類型與技術方案，本研究檢索自動駕駛相關美國專利，並對該些專利引證資料進行交互引證分析、主路徑分析及專利分群分析。
關於主要專利權人間知識流動，交互引證網路結果顯示General Motors 及 Google扮演了知識流動與技術貢獻的重要角色。主路徑分析的三個主要技術發展階段衍生出幾個重要觀察，包括：通訊系統的發展將成為自動駕駛技術疊代的關鍵；自動駕駛感測技術將會導入更多人工智慧，以提升自駕能力；整車廠必須和資訊及通訊科技(ICT)公司合作，可補足自身技術缺口。最後，主要專利群組及群組內的主要專利技術顯示：感測資料處理能力的強化將提升自動駕駛技術整體發展；資料處理技術橫跨多個專利群組，為自動駕駛技術的核心；商用自動駕駛車可能為產品化的先驅，如物流及租賃應用；專利技術的多元化將驅使整車廠建構一套全新的專利佈局及商業策略，亦即，除了自行佈局申請專利外，亦需要強化其他的專利策略，包括交互授權、取得授權、購買專利及選擇專利含金量高的合作對象。
本研究透過縱向(主路徑分析)及橫向(專利分群)的分析，探討自駕車發展方向，並提供一些觀察供自動駕駛領域的研究開發者及專利管理者參考，包括對於自動駕駛技術掌握、專利佈局模式，以及專利交易買賣的策略規劃。此外，本研究也可能促使欲投入自動駕駛產業的人員或組織，重新思考如何推進與專利技術相關的商業決策。

Autonomous vehicles have been widely discussed recently due to the fast improvement of related technologies. This study surveys autonomous vehicle patents in a systematic and quantitative manner by applying three patent citation based analysis. Cross-citation analysis discloses the knowledge flow among companies, main path analysis uncovers the technology development trajectory, and patent group analysis shows the patent deployment of specific technologies.
Cross-citation analysis on the top 30 patent owners shows that General Motors and Google play important roles in knowledge transfer. Their patented technologies are good references for developers who are developing autonomous vehicle. Three possible development can be inferred from the results of main path analysis: 1) the communication system can be further developed to pursue vehicle-to-everything; 2) perception technologies will integrate artificial intelligence to enhance autonomy of vehicle; and 3) vehicle makers will cooperate with ICT companies to develop autonomous vehicles with data processing technologies support. When further analyzing the patent groups, we also present four observations: 1) sensing data processing may trigger improvement of some key self-driving technologies; 2) data processing is essential for the eco-system of an autonomous vehicle; 3) commercial autonomous vehicles may lead the way in autonomous vehicle development, such as applications for logistics and rental business; and 4) car makers may need new patent strategies for business operation.
In regard to IP management, this study discloses the patent development history from the vertical (patent trajectory) and horizontal (patent group) viewpoints, which can benefit IP practitioners in making plans on patent application, patent deployment, and patent transaction. With respect to business management, car makers’ strategies for autonomous vehicle development help participants in designing business and patent strategy in this industry

中文摘要 I
ABSTRACT II
誌謝 III
CONTENTS IV
LIST OF FIGURES VI
LIST OF TABLES VIII
1. INTRODUCTION 1
2. LITERATURE REVIEW 4
2.1 Background 4
2.2 Technologies of Autonomous Vehicle 6
2.3 Patent Citation and Knowledge Network 10
3. METHODOLOGY 12
3.1 Main Paths for Patents 12
3.2 Grouping of Patents 15
3.3 Text Clustering on the Patent Groups 16
4. BASIC STATISTICS 17
4.1 Data Collection 17
4.2 Major Patent Owner 18
5. KNOWLEDGE FLOW 20
5.1 Knowledge Flow among Nations 20
5.2 Knowledge Flow among Companies 20
5.3 Discussion 24
6. DEVELOPMENT TREND 26
6.1 Autonomous Vehicle Technology Development (key-route 10) 26
6.2 Three Evolving Phases of Autonomous Vehicle Technology Development (key-route 20) 29
6.2.1 Phase I: Vehicle moves with pre-determined trajectory 31
6.2.2 Phase II: Improve autonomy of vehicle by fusing more smart technologies 33
6.2.3 Phase III: Vehicle communications become main topic for patent inventions 36
6.3. Discussions 38
6.3.1 Communication system will be further developed to pursue vehicle-to-everything 38
6.3.2 The perception technologies will integrate with artificial intelligence 39
6.3.3 Vehicle makers are likely to cooperate with ICT companies to develop autonomous vehicles 41
7. TECHNOLOGY GROUP ANALYSIS 44
7.1 Group 1: Robot and Vehicle Path Planning 48
7.2 Group 2: Sensing Data Processing for Autonomous Vehicle Control 49
7.3 Group 3: Data Communication for Autonomous Vehicle Control 51
7.4 Group 4: Robot System and Control 52
7.5 Group 5: Fleet Management for Autonomous Vehicle 53
7.6 Group 6: Wireless Charging for Autonomous Vehicle 55
7.7 Group 7: Autonomy Control Based on Driver Behavior 56
7.8 Group 8: Data Application for Driver Assistance System 58
7.9 Group 9: LIDAR Technologies for Autonomous Vehicle 59
7.10 Group 10: Autonomous Vehicle Control Using Artificial Neural Network 61
7.11 Discussions 62
7.11.1 Sensing data processing may trigger improvement of some key self-driving technologies 63
7.11.2 Data processing becomes essential for the eco-system of autonomous vehicle 64
7.11.3 Commercial autonomous vehicle may lead the way 66
7.11.4 Car makers need new patent strategies for business operations 67
8. CONCLUSION 71
REFERENCES 74
APPENDIX 81

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