IDEAS home Printed from https://ideas.repec.org/a/eee/tefoso/v196y2023ics0040162523005024.html
   My bibliography  Save this article

Assessing the technological trajectory of 5G-V2X autonomous driving inventions: Use of patent analysis

Author

Listed:
  • Su, Yu-Shan
  • Huang, Hsini
  • Daim, Tugrul
  • Chien, Pan-Wei
  • Peng, Ru-Ling
  • Karaman Akgul, Arzu

Abstract

Cars and the transportation industry are undergoing a radical transformation. In the past, cars were independent means of transportation, and motor vehicles could not communicate with each other or the nearby environment. Nowadays, the Internet of Vehicles (IoV) technology and autonomous driving vehicles are gradually becoming a reality. Thanks to its high data throughput, fast data transmission rate, and low latency, 5G mobile communication technology lies at the heart of this technological transformation. The Internet of Vehicles service promoted by the 5G Automotive Association (5GAA) is recognized as a market‑leading technological development strategy. In this service, which integrates 5G mobile data with the Internet of Vehicles, Connected Autonomous Vehicles (CAV) will play a key role in the next generation of Cooperative Intelligent Transport Systems (C-ITS).

Suggested Citation

  • Su, Yu-Shan & Huang, Hsini & Daim, Tugrul & Chien, Pan-Wei & Peng, Ru-Ling & Karaman Akgul, Arzu, 2023. "Assessing the technological trajectory of 5G-V2X autonomous driving inventions: Use of patent analysis," Technological Forecasting and Social Change, Elsevier, vol. 196(C).
    • Handle: RePEc:eee:tefoso:v:196:y:2023:i:c:s0040162523005024
    DOI: 10.1016/j.techfore.2023.122817
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0040162523005024
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.techfore.2023.122817?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Loet Leydesdorff & Duncan Kushnir & Ismael Rafols, 2014. "Interactive overlay maps for US patent (USPTO) data based on International Patent Classification (IPC)," Scientometrics, Springer;Akadémiai Kiadó, vol. 98(3), pages 1583-1599, March.
    2. Karvonen, Matti & Kässi, Tuomo, 2013. "Patent citations as a tool for analysing the early stages of convergence," Technological Forecasting and Social Change, Elsevier, vol. 80(6), pages 1094-1107.
    3. Li, Xin & Wen, Yang & Jiang, Jiaojiao & Daim, Tugrul & Huang, Lucheng, 2022. "Identifying potential breakthrough research: A machine learning method using scientific papers and Twitter data," Technological Forecasting and Social Change, Elsevier, vol. 184(C).
    4. Ahn, Sang-Jin, 2020. "Three characteristics of technology competition by IoT-driven digitization," Technological Forecasting and Social Change, Elsevier, vol. 157(C).
    5. Daim, Tugrul & Lai, Kuei Kuei & Yalcin, Haydar & Alsoubie, Fayez & Kumar, Vimal, 2020. "Forecasting technological positioning through technology knowledge redundancy: Patent citation analysis of IoT, cybersecurity, and Blockchain," Technological Forecasting and Social Change, Elsevier, vol. 161(C).
    6. Bridgelall, Raj & Stubbing, Edward, 2021. "Forecasting the effects of autonomous vehicles on land use," Technological Forecasting and Social Change, Elsevier, vol. 163(C).
    7. Penmetsa, Praveena & Adanu, Emmanuel Kofi & Wood, Dustin & Wang, Teng & Jones, Steven L., 2019. "Perceptions and expectations of autonomous vehicles – A snapshot of vulnerable road user opinion," Technological Forecasting and Social Change, Elsevier, vol. 143(C), pages 9-13.
    8. El-Moghazi, Mohamed & Whalley, Jason & Irvine, James, 2019. "Technology neutrality: Exploring the interaction between International Mobile Telecommunication and national spectrum management policies," Telecommunications Policy, Elsevier, vol. 43(6), pages 531-548.
    9. Youtie, Jan & Porter, Alan L. & Shapira, Philip & Woo, Seokkyun & Huang, Yayun, 2017. "Autonomous systems: A bibliometric and patent analysis," Studien zum deutschen Innovationssystem 14-2018, Expertenkommission Forschung und Innovation (EFI) - Commission of Experts for Research and Innovation, Berlin.
    10. Sungho Son & Nam-Wook Cho, 2020. "Technology Fusion Characteristics in the Solar Photovoltaic Industry of South Korea: A Patent Network Analysis Using IPC Co-Occurrence," Sustainability, MDPI, vol. 12(21), pages 1-19, October.
    11. Baumann, Manuel & Domnik, Tobias & Haase, Martina & Wulf, Christina & Emmerich, Philip & Rösch, Christine & Zapp, Petra & Naegler, Tobias & Weil, Marcel, 2021. "Comparative patent analysis for the identification of global research trends for the case of battery storage, hydrogen and bioenergy," Technological Forecasting and Social Change, Elsevier, vol. 165(C).
    12. Saeed, Tariq Usman & Burris, Mark W. & Labi, Samuel & Sinha, Kumares C., 2020. "An empirical discourse on forecasting the use of autonomous vehicles using consumers’ preferences," Technological Forecasting and Social Change, Elsevier, vol. 158(C).
    13. Caviggioli, Federico, 2016. "Technology fusion: Identification and analysis of the drivers of technology convergence using patent data," Technovation, Elsevier, vol. 55, pages 22-32.
    14. Wang, Chang & Geng, Hongjun & Sun, Rui & Song, Huiling, 2022. "Technological potential analysis and vacant technology forecasting in the graphene field based on the patent data mining," Resources Policy, Elsevier, vol. 77(C).
    15. Ardito, Lorenzo & D'Adda, Diego & Messeni Petruzzelli, Antonio, 2018. "Mapping innovation dynamics in the Internet of Things domain: Evidence from patent analysis," Technological Forecasting and Social Change, Elsevier, vol. 136(C), pages 317-330.
    16. Hess, David J., 2020. "Incumbent-led transitions and civil society: Autonomous vehicle policy and consumer organizations in the United States," Technological Forecasting and Social Change, Elsevier, vol. 151(C).
    17. Suzuki, Jun & Kodama, Fumio, 2004. "Technological diversity of persistent innovators in Japan: Two case studies of large Japanese firms," Research Policy, Elsevier, vol. 33(3), pages 531-549, April.
    18. McLeay, Fraser & Olya, Hossein & Liu, Hongfei & Jayawardhena, Chanaka & Dennis, Charles, 2022. "A multi-analytical approach to studying customers motivations to use innovative totally autonomous vehicles," Technological Forecasting and Social Change, Elsevier, vol. 174(C).
    19. Shu-Hao Chang & Chin-Yuan Fan, 2020. "Using Patent Technology Networks to Observe Neurocomputing Technology Hotspots and Development Trends," Sustainability, MDPI, vol. 12(18), pages 1-15, September.
    20. Li, Xin & Wu, Yundi & Cheng, Haolun & Xie, Qianqian & Daim, Tugrul, 2023. "Identifying technology opportunity using SAO semantic mining and outlier detection method: A case of triboelectric nanogenerator technology," Technological Forecasting and Social Change, Elsevier, vol. 189(C).
    21. Nastjuk, Ilja & Herrenkind, Bernd & Marrone, Mauricio & Brendel, Alfred Benedikt & Kolbe, Lutz M., 2020. "What drives the acceptance of autonomous driving? An investigation of acceptance factors from an end-user's perspective," Technological Forecasting and Social Change, Elsevier, vol. 161(C).
    22. Kim, Gabjo & Bae, Jinwoo, 2017. "A novel approach to forecast promising technology through patent analysis," Technological Forecasting and Social Change, Elsevier, vol. 117(C), pages 228-237.
    23. Zamani, Mehdi & Yalcin, Haydar & Naeini, Ali Bonyadi & Zeba, Gordana & Daim, Tugrul U, 2022. "Developing metrics for emerging technologies: identification and assessment," Technological Forecasting and Social Change, Elsevier, vol. 176(C).
    24. Lee Fleming, 2001. "Recombinant Uncertainty in Technological Search," Management Science, INFORMS, vol. 47(1), pages 117-132, January.
    25. Li, Shuying & Zhang, Xian & Xu, Haiyun & Fang, Shu & Garces, Edwin & Daim, Tugrul, 2020. "Measuring strategic technological strength :Patent Portfolio Model," Technological Forecasting and Social Change, Elsevier, vol. 157(C).
    26. Merfeld, Katrin & Wilhelms, Mark-Philipp & Henkel, Sven & Kreutzer, Karin, 2019. "Carsharing with shared autonomous vehicles: Uncovering drivers, barriers and future developments – A four-stage Delphi study," Technological Forecasting and Social Change, Elsevier, vol. 144(C), pages 66-81.
    27. BERTRANDIAS, Laurent & LOWE, Ben & SADIK-ROZSNYAI, Orsolya & CARRICANO, Manu, 2021. "Delegating decision-making to autonomous products: A value model emphasizing the role of well-being," Technological Forecasting and Social Change, Elsevier, vol. 169(C).
    28. Lindgren, Thomas & Pink, Sarah & Fors, Vaike, 2021. "Fore-sighting autonomous driving - An Ethnographic approach," Technological Forecasting and Social Change, Elsevier, vol. 173(C).
    29. Yu-Jing Chiu & Tao-Ming Ying, 2012. "A Novel Method for Technology Forecasting and Developing R&D Strategy of Building Integrated Photovoltaic Technology Industry," Mathematical Problems in Engineering, Hindawi, vol. 2012, pages 1-24, July.
    30. Akimoto, Keigo & Sano, Fuminori & Oda, Junichiro, 2022. "Impacts of ride and car-sharing associated with fully autonomous cars on global energy consumptions and carbon dioxide emissions," Technological Forecasting and Social Change, Elsevier, vol. 174(C).
    31. Zhang, Hao & Daim, Tugrul & Zhang, Yunqiu (Peggy), 2021. "Integrating patent analysis into technology roadmapping: A latent dirichlet allocation based technology assessment and roadmapping in the field of Blockchain," Technological Forecasting and Social Change, Elsevier, vol. 167(C).
    32. Alvarez León, Luis F. & Aoyama, Yuko, 2022. "Industry emergence and market capture: The rise of autonomous vehicles," Technological Forecasting and Social Change, Elsevier, vol. 180(C).
    33. Moehrle, Martin G. & Caferoglu, Hüseyin, 2019. "Technological speciation as a source for emerging technologies. Using semantic patent analysis for the case of camera technology," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 776-784.
    34. Yawei Wang & Frauke Urban & Yuan Zhou & Luyi Chen, 2018. "Comparing the Technology Trajectories of Solar PV and Solar Water Heaters in China: Using a Patent Lens," Sustainability, MDPI, vol. 10(11), pages 1-29, November.
    35. Yuan, Xiaodong & Cai, Yuchen, 2021. "Forecasting the development trend of low emission vehicle technologies: Based on patent data," Technological Forecasting and Social Change, Elsevier, vol. 166(C).
    36. Richter, Maximilian A. & Hagenmaier, Markus & Bandte, Oliver & Parida, Vinit & Wincent, Joakim, 2022. "Smart cities, urban mobility and autonomous vehicles: How different cities needs different sustainable investment strategies," Technological Forecasting and Social Change, Elsevier, vol. 184(C).
    37. Wang, Xiaoli & Daim, Tugrul & Huang, Lucheng & Li, Zhiqiang & Shaikh, Ruqia & Kassi, Diby Francois, 2022. "Monitoring the development trend and competition status of high technologies using patent analysis and bibliographic coupling: The case of electronic design automation technology," Technology in Society, Elsevier, vol. 71(C).
    38. Modis, Theodore, 2013. "Long-term GDP forecasts and the prospects for growth," Technological Forecasting and Social Change, Elsevier, vol. 80(8), pages 1557-1562.
    39. Ernst, Holger, 2003. "Patent information for strategic technology management," World Patent Information, Elsevier, vol. 25(3), pages 233-242, September.
    40. Fritschy, Carolin & Spinler, Stefan, 2019. "The impact of autonomous trucks on business models in the automotive and logistics industry–a Delphi-based scenario study," Technological Forecasting and Social Change, Elsevier, vol. 148(C).
    41. Hain, Daniel S. & Jurowetzki, Roman & Buchmann, Tobias & Wolf, Patrick, 2022. "A text-embedding-based approach to measuring patent-to-patent technological similarity," Technological Forecasting and Social Change, Elsevier, vol. 177(C).
    42. Choi, Jinho & Hwang, Yong-Sik, 2014. "Patent keyword network analysis for improving technology development efficiency," Technological Forecasting and Social Change, Elsevier, vol. 83(C), pages 170-182.
    43. Li, Shuying & Garces, Edwin & Daim, Tugrul, 2019. "Technology forecasting by analogy-based on social network analysis: The case of autonomous vehicles," Technological Forecasting and Social Change, Elsevier, vol. 148(C).
    44. Modis, Theodore, 2013. "Long-Term GDP Forecasts and the Prospects for Growth," OSF Preprints aqcht, Center for Open Science.
    45. Lee, Changyong & Jeon, Daeseong & Ahn, Joon Mo & Kwon, Ohjin, 2020. "Navigating a product landscape for technology opportunity analysis: A word2vec approach using an integrated patent-product database," Technovation, Elsevier, vol. 96.
    46. Li, Xin & Xie, Qianqian & Daim, Tugrul & Huang, Lucheng, 2019. "Forecasting technology trends using text mining of the gaps between science and technology: The case of perovskite solar cell technology," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 432-449.
    47. Kumar, Girish & James, Ajith Tom & Choudhary, Krishna & Sahai, Rishi & Song, Weon Keun, 2022. "Investigation and analysis of implementation challenges for autonomous vehicles in developing countries using hybrid structural modeling," Technological Forecasting and Social Change, Elsevier, vol. 185(C).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Seok Jin Youn & Yong-Jae Lee & Ha-Eun Han & Chang-Woo Lee & Donggyun Sohn & Chulung Lee, 2024. "A Data Analytics and Machine Learning Approach to Develop a Technology Roadmap for Next-Generation Logistics Utilizing Underground Systems," Sustainability, MDPI, vol. 16(15), pages 1-32, August.
    2. Podrecca, Matteo & Culot, Giovanna & Tavassoli, Sam & Orzes, Guido, 2024. "Artificial intelligence for climate change: a patent analysis in the manufacturing sector," Papers in Innovation Studies 2024/12, Lund University, CIRCLE - Centre for Innovation Research.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Sungho Son & Nam-Wook Cho, 2020. "Technology Fusion Characteristics in the Solar Photovoltaic Industry of South Korea: A Patent Network Analysis Using IPC Co-Occurrence," Sustainability, MDPI, vol. 12(21), pages 1-19, October.
    2. Choi, Hyunhong & Woo, JongRoul, 2022. "Investigating emerging hydrogen technology topics and comparing national level technological focus: Patent analysis using a structural topic model," Applied Energy, Elsevier, vol. 313(C).
    3. Yong-Jae Lee & Young Jae Han & Sang-Soo Kim & Chulung Lee, 2022. "Patent Data Analytics for Technology Forecasting of the Railway Main Transformer," Sustainability, MDPI, vol. 15(1), pages 1-25, December.
    4. Koopo Kwon & Sungchan Jun & Yong-Jae Lee & Sanghei Choi & Chulung Lee, 2022. "Logistics Technology Forecasting Framework Using Patent Analysis for Technology Roadmap," Sustainability, MDPI, vol. 14(9), pages 1-30, April.
    5. Kraus, Sascha & Kumar, Satish & Lim, Weng Marc & Kaur, Jaspreet & Sharma, Anuj & Schiavone, Francesco, 2023. "From moon landing to metaverse: Tracing the evolution of Technological Forecasting and Social Change," Technological Forecasting and Social Change, Elsevier, vol. 189(C).
    6. Yoganathan, Vignesh & Osburg, Victoria-Sophie, 2024. "Heterogenous evaluations of autonomous vehicle services: An extended theoretical framework and empirical evidence," Technological Forecasting and Social Change, Elsevier, vol. 198(C).
    7. Yuan, Xiaodong & Cai, Yuchen, 2021. "Forecasting the development trend of low emission vehicle technologies: Based on patent data," Technological Forecasting and Social Change, Elsevier, vol. 166(C).
    8. Leminen, Seppo & Rajahonka, Mervi & Wendelin, Robert & Westerlund, Mika & Nyström, Anna-Greta, 2022. "Autonomous vehicle solutions and their digital servitization business models," Technological Forecasting and Social Change, Elsevier, vol. 185(C).
    9. Alvarez León, Luis F. & Aoyama, Yuko, 2022. "Industry emergence and market capture: The rise of autonomous vehicles," Technological Forecasting and Social Change, Elsevier, vol. 180(C).
    10. Xi, Xi & Ren, Feifei & Yu, Lean & Yang, Jing, 2023. "Detecting the technology's evolutionary pathway using HiDS-trait-driven tech mining strategy," Technological Forecasting and Social Change, Elsevier, vol. 195(C).
    11. Lee, Changyong, 2021. "A review of data analytics in technological forecasting," Technological Forecasting and Social Change, Elsevier, vol. 166(C).
    12. Ghaffari, Mohsen & Aliahmadi, Alireza & Khalkhali, Abolfazl & Zakery, Amir & Daim, Tugrul U. & Yalcin, Haydar, 2023. "Topic-based technology mapping using patent data analysis: A case study of vehicle tires," Technological Forecasting and Social Change, Elsevier, vol. 193(C).
    13. Wang, Xiaoli & Daim, Tugrul & Huang, Lucheng & Li, Zhiqiang & Shaikh, Ruqia & Kassi, Diby Francois, 2022. "Monitoring the development trend and competition status of high technologies using patent analysis and bibliographic coupling: The case of electronic design automation technology," Technology in Society, Elsevier, vol. 71(C).
    14. Puccetti, Giovanni & Giordano, Vito & Spada, Irene & Chiarello, Filippo & Fantoni, Gualtiero, 2023. "Technology identification from patent texts: A novel named entity recognition method," Technological Forecasting and Social Change, Elsevier, vol. 186(PB).
    15. Naeini, Ali Bonyadi & Zamani, Mehdi & Daim, Tugrul U. & Sharma, Mahak & Yalcin, Haydar, 2022. "Conceptual structure and perspectives on “innovation management”: A bibliometric review," Technological Forecasting and Social Change, Elsevier, vol. 185(C).
    16. Zhang, Hao & Daim, Tugrul & Zhang, Yunqiu (Peggy), 2021. "Integrating patent analysis into technology roadmapping: A latent dirichlet allocation based technology assessment and roadmapping in the field of Blockchain," Technological Forecasting and Social Change, Elsevier, vol. 167(C).
    17. Plantec, Quentin & Le Masson, Pascal & Weil, Benoît, 2021. "Impact of knowledge search practices on the originality of inventions: A study in the oil & gas industry through dynamic patent analysis," Technological Forecasting and Social Change, Elsevier, vol. 168(C).
    18. Youngjae Choi & Sanghyun Park & Sungjoo Lee, 2021. "Identifying emerging technologies to envision a future innovation ecosystem: A machine learning approach to patent data," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(7), pages 5431-5476, July.
    19. Mun, Changbae & Yoon, Sejun & Raghavan, Nagarajan & Hwang, Dongwook & Basnet, Subarna & Park, Hyunseok, 2021. "Function score-based technological trend analysis," Technovation, Elsevier, vol. 101(C).
    20. Lindgren, Thomas & Pink, Sarah & Fors, Vaike, 2021. "Fore-sighting autonomous driving - An Ethnographic approach," Technological Forecasting and Social Change, Elsevier, vol. 173(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:tefoso:v:196:y:2023:i:c:s0040162523005024. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.sciencedirect.com/science/journal/00401625 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.