Loading...
|
Please use this identifier to cite or link to this item:
https://nccur.lib.nccu.edu.tw/handle/140.119/136688
|
| Title: | 透過專利數據進行技術預測：探討自駕車技術之擴散
Technology Forecasting with Patent Data: The Diffusion of Autonomous Vehicle Technology |
| Authors: | 張登凱
Chang, Teng-Kai |
| Contributors: | 宋皇志
Sung, Huang-Chih
張登凱
Chang, Teng-Kai |
| Keywords: | 自駕車
技術預測
專利數據
創新擴散
Autonomous Vehicles
Technology Forecasting
Patent Data
Diffusion of Innovation |
| Date: | 2021 |
| Issue Date: | 2021-08-04 16:27:43 (UTC+8) |
| Abstract: | 自駕車已不再只是科幻電影小說裡的情節，即將在21世紀當中問世。自駕車是一個跨技術、跨產業的整合，人工智慧技術的發展絕對是其中一個重要的因素。許多國家、企業都紛紛投入到自駕車領域當中，其中美國地區的發展可以算是全世界的領先指標，無論是政策、技術，甚至產業。而台灣也在自駕車產業當中扮演舉足輕重的角色。
本研究旨在探討是否能夠利用專利數據進行自駕車技術的預測預測。透過分析模型的研究，驗證是否符合過去學者對於技術預測模型的相關研究結論，並找出自駕車技術所適用的技術預測模型，為其進行技術預測推論。提供往後學術研究之參考價值，以及對相關產業貢獻。
經研究後發現，專利數據除了能夠分析本身的成長趨勢外，也能夠作為技術預測的數據來源，研究該領域技術的發展。在模型分析上，本研究回應過去學者對於能夠良好的解釋數據的模型是否代表能夠良好的預測數據未來發展所提出的質疑，證實在專利數據上最佳配適模型不代表具有最佳的預測能力。最後，自駕車整體技術發展即將從成長階段進入到成熟期，現階段正在快速成長當中，專利申請活動增加。而研究發現技術與市場存在遲滯期，自駕車市場現階段以Level 2以下與少量Level 3之自駕車技術為主，而Level 3以上之自駕車技術與市場皆尚未成熟，需要關鍵技術以驅動Level 3以上技術發展。
Autonomous vehicles are no longer just plots in science fiction movies and novels, as they have become a heated topic and are expected to be fully developed in the 21st century. Autonomous vehicle technology is interdisciplinary, and AI technology is the main core of its development. Many countries and companies have invested in the field of autonomous vehicles. Among them, the United States can be regarded as the world`s trailblazer in the field. Taiwan also plays a key role in the industry.
The purpose of this research is to explore whether patent data can be used to forecast the development of autonomous vehicle`s technology. Through model analyzing method, this research aims to verify whether it is in line with the conclusions reached by relevant research on technology forecasting, and to find out the best model for the diffusion of autonomous vehicle technology. This study hopes to provide a reference value for future academic research and related industries.
In this research, it is found that data from patents of autonomous vehicle can not only analyze its own growth trend but also be used for technology forecasting to understand its development. By using the model analyzing method to decide the proper model for patent data, this research responds to the doubts raised by past scholars about whether a model that can fit the data well can also predict future development. It proves that the best-fitting model for patent data does not necessarily mean that it`s the most predictive. One thing found in the research is that the entire autonomous vehicle technology is about to reach the maturity stage from the growth stage. At this stage, it is growing rapidly, and the number of patent applications is increasing. The last thing suggested by the research is a time lag between technology and the market. The autonomous vehicle market is currently dominated by level 2 and a small number of level 3 autonomous vehicles, while those above level 3 are still developing. Certain essential techniques might be needed to drive autonomous vehicles` development at level 3 or above. |
| Reference: | Bass, F. M. (1969). A New Product Growth for Model Consumer Durables. Management Science. doi:https://doi.org/10.1287/mnsc.15.5.215
Campbell, R. S. (1983). Patent trends as a technological forecasting tool. World Patent Information, 5(3), 137-143. doi:https://doi.org/10.1016/0172-2190(83)90134-5
Chapin, F. S. (1928). Cultural Change: Century Company.
Chen, M.-J. (1996). Competitor Analysis and Interfirm Rivalry: Toward a Theoretical Integration. The Academy of Management Review, 21(1), 100-134. doi:10.2307/258631
Chen, Y.-H., Chen, C.-Y., & Lee, S.-C. (2011). Technology forecasting and patent strategy of hydrogen energy and fuel cell technologies. International Journal of Hydrogen Energy, 36(12), 6957-6969. doi:https://doi.org/10.1016/j.ijhydene.2011.03.063
Daim, T. U., Rueda, G., Martin, H., & Gerdsri, P. (2006). Forecasting emerging technologies: Use of bibliometrics and patent analysis. Technological Forecasting and Social Change, 73(8), 981-1012. doi:https://doi.org/10.1016/j.techfore.2006.04.004
Davis, A. (1939). Technicways in American Civilization - Notes on a Method of Measuring Their Point of Origin. Social Forces, 18(3), 317-330. Retrieved from https://heinonline.org/HOL/P?h=hein.journals/josf18&i=325
https://heinonline.org/HOL/PrintRequest?handle=hein.journals/josf18&collection=journals&div=43&id=325&print=section&sction=43
Ernst, H. (1997). The Use of Patent Data for Technological Forecasting: The Diffusion of CNC-Technology in the Machine Tool Industry. Small Business Economics.
Fisher, J. C., & Pry, R. H. (1971). A simple substitution model of technological change. Technological Forecasting and Social Change, 3, 75-88. doi:https://doi.org/10.1016/S0040-1625(71)80005-7
Fourt, L. A., & Woodlock, J. W. (1960). Early Prediction of Market Success for New Grocery Products. Journal of Marketing, 25(2), 31-38. doi:10.1177/002224296002500206
Gilfillan, S. C. (1935). Inventing the Ship: A Study of the Inventions Made in Her History Between Floating Log and Rotorship; a Self-contained But Companion Volume to the Author`s "Sociology of Invention" with 80 Illustrations, Bibliographies, Notes and Index: Follett publishing Company.
Hardie, B. G. S., Fader, P. S., & Wisniewski, M. (1998). An empirical comparison of new product trial forecasting models. Journal of Forecasting, 17(3‐4), 209-229. doi:https://doi.org/10.1002/(SICI)1099-131X(199806/07)17:3/4<209::AID-FOR694>3.0.CO;2-3
Hilgard, E. R. (1956). Theories of learning (2nd ed. ed.). New York: Appleton-Century-Crofts.
Jennifer, S. (2019). SAE Standards News: J3016 automated-driving graphic update. Retrieved from https://www.sae.org/news/2019/01/sae-updates-j3016-automated-driving-graphic
Kato, S., Takeuchi, E., Ishiguro, Y., Ninomiya, Y., Takeda, K., & Hamada, T. (2015). An Open Approach to Autonomous Vehicles. IEEE Micro, 35(6), 60-68. doi:10.1109/MM.2015.133
Katz, E. (1962). Notes on The Unit of Adoption in Diffusion Research. Sociological Inquiry, 32(1), 3-9. doi:https://doi.org/10.1111/j.1475-682X.1962.tb00525.x
Katz, E., Levin, M. L., & Hamilton, H. (1963). Traditions of Research on the Diffusion of Innovation. American Sociological Review, 28(2), 237-252. doi:10.2307/2090611
Kenney, J. B. (2011). Dedicated Short-Range Communications (DSRC) Standards in the United States. Proceedings of the IEEE, 99(7), 1162-1182. doi:10.1109/JPROC.2011.2132790
Kim, D. H., Shin, Y. G., Park, S. S., & Jang, D. S. (2009). Forecasting Diffusion of Technology by using Bass Model. AIP Conference Proceedings, 1148(1), 149-152. doi:10.1063/1.3225258
Malthus, T. R. (1798). An Essay on the Principle of Population: London, J. Johnson.
Mansfield, E. (1961). Technical Change and the Rate of Imitation. Econometrica, 29(4), 741-766. doi:10.2307/1911817
Martino, J. P. (1993). Technological forecasting for decision making: McGraw-Hill, Inc.
Meade, N., & Islam, T. (1998). Technological Forecasting -- Model Selection, Model Stability, and Combining Models. Management Science, 44(8), 1115-1130. Retrieved from http://www.jstor.org/stable/2634690
Meade, N., & Islam, T. (2001). Forecasting the diffusion of innovations: Implications for time-series extrapolation. In Principles of forecasting (pp. 577-595): Springer.
Pearl, R. (1924). The Curve of Population Growth. Proceedings of the American Philosophical Society, 63(1), 10-17. Retrieved from http://www.jstor.org/stable/984438
Pearl, R., & Reed, L. J. (1920). On the Rate of Growth of the Population of the United States since 1790 and Its Mathematical Representation. Proceedings of the National Academy of Sciences of the United States of America, 6(6), 275-288. doi:10.1073/pnas.6.6.275
Porter, A. L., Roper, A. T., Mason, T. W., Rossini, F. A., & Banks, J. (1991). Forecasting and management of technology. New York: John Wiley.
Rhyne, R. (1974). Technological forecasting within alternative whole futures projections. Technological Forecasting and Social Change, 6, 133-162. doi:https://doi.org/10.1016/0040-1625(74)90014-6
Rogers, E. M. (1962). Diffusion of innovations. New York: Free Press of Glencoe.
Ruttan, V. W. (1959). Usher and Schumpeter on Invention, Innovation, and Technological Change. The Quarterly Journal of Economics, 73(4), 596-606. doi:10.2307/1884305
Ryan, B., & Gross, N. C. (1943). The diffusion of hybrid seed corn in two Iowa communities. Rural Sociology. Retrieved from https://digital.library.cornell.edu/catalog/chla5075626_4294_001
Sundqvist, S., Frank, L., Puumalainen, K., & Kämäräinen, J. (2008). Forecasting the Critical Mass of Wireless Communications.
Trappey, C. V., Wu, H.-Y., Taghaboni-Dutta, F., & Trappey, A. J. C. (2011). Using patent data for technology forecasting: China RFID patent analysis. Advanced Engineering Informatics, 25(1), 53-64. doi:https://doi.org/10.1016/j.aei.2010.05.007
Vincent, M., Andrew, T., & Kess, M. (2020). 美重劃V2X頻譜掀波 DSRC亮技術優勢力守版圖. Retrieved from https://www.2cm.com.tw/2cm/zh-tw/tech/8E437E37CE3B4ACF90E060AB92A5961B
WIPO. PATENTSCOPE Artificial Intelligence Index. Retrieved from https://www.wipo.int/tech_trends/en/artificial_intelligence/patentscope.html#
WIPO. (2019). WIPO Technology Trends 2019: Artificial Intelligence. Retrieved from
Xavier, M., Thomas, D., Nikolaus, L., Michael, R., Antonella, M.-P., Rakshita, A., & Florian, S. (2015). Revolution in the Driver`s Seat: The Road to Autonomous Vehicles. Retrieved from https://www.bcg.com/publications/2015/automotive-consumer-insight-revolution-drivers-seat-road-autonomous-vehicles
马天旗, 黄文静, 李杰, 张丛, 李萍, 郝政宇, & 王冀. (2015). 专利分析 方法、图表解读与情报挖掘. 北京: 知识产权出版社.
冯延鑫. (2017). 基于符号回归的创新扩散模型研究. (碩士). 大連理工大學,
朱文伶. (2010). 行動電話擴散研究之模型選用及驅動因子分析. (博士). 國立政治大學, 台北市. Retrieved from https://hdl.handle.net/11296/b5j8r6
李思穎. (2020). 以專利分析探究新興領域之產業融合－以自駕車為例. (碩士). 國立政治大學, 台北市. Retrieved from https://hdl.handle.net/11296/4stk8n
卓立庭. (2020). 從專利談自駕車發展：跨技術、跨產業合作才是加速自駕車商業化的妙方. Retrieved from https://www.bnext.com.tw/article/58618/self-driving-patent-ict-ai
翁國樑, 李玉忠, 柯明寬, & 徐錦衍. (2019). 自駕車發展趨勢與關鍵技術. 工程, 92, 23-41.
張凱喬. (2017). 美國各州自駕車測試法規訂定現況. Retrieved from https://www.artc.org.tw/chinese/03_service/03_02detail.aspx?pid=3173
郭庭昱. (2019). 最懂自駕車的台灣人詹景堯：自駕車不會跳躍式的成長. Retrieved from https://www.wealth.com.tw/home/articles/22806
陳建次. (2019). 國際自駕車運用與技術趨勢. 車輛研測專刊.
陳敬典. (2018). 自動駕駛車發展現況與未來趨勢. 車輛研測專刊.
陳敬典. (2020). 全球自駕車產業發展現況與未來趨勢. 車輛研測專刊.
黃威陞. (2019). 智慧時代來臨 車聯網技術的選擇. Retrieved from https://www.artc.org.tw/chinese/03_service/03_02detail.aspx?pid=13371
劉湝沂. (2018). 影音串流平台之創新擴散模型研究－以Netflix和Spotify為例. (碩士). 國立政治大學, Retrieved from http://thesis.lib.nccu.edu.tw/record/#G1063641331%22.
鄭幼民. (2008). 數位相機產業的技術發展趨勢研究. (碩士). 國立交通大學, 新竹市. Retrieved from https://hdl.handle.net/11296/db4ttz
鄭俊彥. (2020). 透過專利文字探勘辨識潛在競爭者之方法：以金融科技產業為例. (碩士). 國立政治大學, 台北市. Retrieved from https://hdl.handle.net/11296/4ys8ry
蕭鴻凱. (2020). 建構下世代行車安全藍圖 DSRC/C-V2X標準細比拚. Retrieved from https://www.2cm.com.tw/2cm/zh-tw/tech/1BE4026BE9BB4E13814A2832AB76AED8 |
| Description: | 碩士
國立政治大學
科技管理與智慧財產研究所
108364119 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0108364119 |
| Data Type: | thesis |
| DOI: | 10.6814/NCCU202101010 |
| Appears in Collections: | [科技管理與智慧財產研究所] 學位論文
|
Files in This Item:
| File |
Description |
Size | Format | |
|---|
| 411901.pdf | | 4999Kb | Adobe PDF2 | 455 | View/Open |
|
All items in 政大典藏 are protected by copyright, with all rights reserved.
|
