The Journal of The Korea Institute of Intelligent Transport Systems (한국ITS학회 논문지)

The Korea Institute of Intelligent Transport Systems (한국ITS학회)
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A Study of the Trend Analysis of National Automated Vehicle Research Using NTIS Data

NTIS 데이터를 이용한 국내 자율주행 연구 동향 분석에 관한 연구

  • In-Seok Jeong (Dept. of Road Transport.Research, Korea Transport Institute) ;
  • Jiwon Kang (Dept. of Road Transport.Research, Korea Transport Institute) ;
  • Jongdeok Lee (Dept. of Road Transport.Research, Korea Transport Institute) ;
  • Sangmin Park (Dept. of Road Transport.Research, Korea Transport Institute)
  • 정인석 (한국교통연구원 도로교통연구본부) ;
  • 강지원 (한국교통연구원 도로교통연구본부) ;
  • 이종덕 (한국교통연구원 도로교통연구본부) ;
  • 박상민 (한국교통연구원 도로교통연구본부)
  • Received : 2022.11.24
  • Accepted : 2023.02.07
  • Published : 2023.04.30
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Abstract

Recently, there has been an increase in the research and development of automated vehicles worldwide. Research focused on automated vehicles in Korea is steadily progressing as a national R&D project. Since automated driving technology comprises diverse technology fields, it is necessary to identify the current position of the research. In this study, we propose a methodology for analyzing research trends using the NTIS data. In addition, we review the effectiveness of the currently developed research trend methodology by deriving primary keywords and major topics using the proposed method. We expect that the methodology developed in this study can be applied to identify and analyze future automated vehicle research trends.

최근 전 세계적으로 첨단 이동 수단인 자율주행자동차에 대한 연구가 활발하다. 국내에서도 첨단 이동 수단 기술을 12대 국가 전략기술로 선정하였으며, 자율주행자동차와 관련된 국가 R&D 사업을 통해 연구가 꾸준히 진행되고 있다. 자율주행자동차 기술의 경우 다양한 분야의 기술이 집합된 결과물로 다양한 방향성을 보이고 있다. 그렇기에 자율주행 연구의 현 위치를 파악하고 향후 방향성을 정립하는 것이 필요하다. 본 연구에서는 국가과학기술지식정보서비스(National Science and Technology Information Service, NTIS)에서 제공하는 국가 R&D 사업에 등록된 성과 정보 중 논문 초록을 활용하여 연구 동향을 분석하는 방법론을 제시하였다. 또한, 제시된 방법론을 이용하여 주요 키워드 및 주요 토픽을 도출하여 개발된 연구 동향 방법론의 유효성을 검토하였다. 본 연구에서 개발된 방법론은 향후 자율주행자동차 연구 동향 파악 및 분석에 활용될 수 있을 것으로 기대된다.

Keywords

Acknowledgement

본 연구는 국토교통부/국토교통과학기술진흥원의 지원으로 수행되었음(과제번호 RS-2022-00141102).

References

  1. Blei, D. M.(2012), "Probabilistic topic models", Communications of the ACM(Association for Computing Machinery), vol. 55, no. 4, pp.77-84. https://doi.org/10.1145/2133806.2133826
  2. Blei, D. M., Ng, A. Y. and Jordan, M. I.(2003), "Latent dirichlet allocation", Journal of Machine Learning Research, vol. 3, no. Jan, pp.993-1022.
  3. Bouma, G.(2009), "Normalized (pointwise) mutual information in collocation extraction", Proceedings of GSCL(German Society for Computational Linguistics), vol. 30, pp.31-40.
  4. Faisal, A., Yigitcanlar, T., Kamruzzaman, M. and Paz, A.(2021), "Mapping two decades of autonomous vehicle research: A systematic scientometric analysis", Journal of Urban Technology, vol. 28, no. 3-4, pp.45-74. https://doi.org/10.1080/10630732.2020.1780868
  5. Gandia, R. M., Antonialli, F., Cavazza, B. H., Neto, A. M., Lima, D. A. D., Sugano, J. Y., Nicolai, I. and Zambalde, A. L.(2019), "Autonomous vehicles: Scientometric and bibliometric review", Transport Reviews, vol. 39, no. 1, pp.9-28. https://doi.org/10.1080/01441647.2018.1518937
  6. Hacohen, S., Medina, O. and Shoval, S.(2022), "Autonomous Driving: A Survey of Technological Gaps Using Google Scholar and Web of Science Trend Analysis", IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 11, pp.21241-21258. https://doi.org/10.1109/TITS.2022.3172442
  7. Im, I., Song, J. I., Lee, J. Y. and Hwang, K. Y.(2017), "Analysis of the perception of autonomous vehicles using text mining technique", The Journal of the Korea Institute of Intelligent Transport Systems, vol. 16, no. 6, pp.231-243. https://doi.org/10.12815/kits.2017.16.6.231
  8. Jang, S. Y. and Jung, S. H.(2021), "An Analysis of the Research Trends for Urban Study using Topic Modeling", Journal of the Korea Academia-Industrial Cooperation Society, vol. 22, no. 3, pp.661-670. https://doi.org/10.5762/KAIS.2021.22.3.661
  9. Kim, A., Jeong, S. H., Choi, H. B. and Kim, H. H.(2018), "Analysis of response to transportation policy for particulate matter reduction using regression analysis and text mining", Korea Information Processing Society, The KIPS Fall Conference, pp.277-280.
  10. Kim, G. L.(2021), "A Study on the Analysis of R&D Trends and the Development of Logic Models for Autonomous Vehicles", Journal of Digital Convergence, vol. 19, no. 5, pp.31-39. https://doi.org/10.14400/JDC.2021.19.5.031
  11. Kim, N., Lee, D., Choi, H. and Wong, W. X. S.(2017), "Investigations on techniques and applications of text analytics", The Journal of Korean Institute of Communications and Information Sciences, vol. 42, no. 2, pp.471-492. https://doi.org/10.7840/kics.2017.42.2.471
  12. Lim, S. Y., Yi, M. S., Jin, G. H. and Shin, D. B.(2014), "A study on the research trends in the area of geospatial-information using text-mining technique focused on national R&D reports and theses", Spatial Information Research, vol. 22, no. 4, pp.11-20. https://doi.org/10.12672/ksis.2014.22.4.011
  13. Na, S. T., Kim, J. H., Jung, M. H. and Ahn, J. E.(2016), "Trend Analysis using Topic Modeling for Simulation Studies", Journal of the Korea Society for Simulation, vol. 25, no. 3, pp.107-116. https://doi.org/10.9709/JKSS.2016.25.3.107
  14. National Science & Technology Information Service, https://www.ntis.go.kr/ThAbout.do, 2022.11.10.
  15. Oh, C. S., Lee, Y. T. and Ko, M.(2016), "Establishment of ITS Policy Issues Investigation Method in the Road Section applied Textmining", The Journal of the Korea Institute of Intelligent Transport Systems, vol. 15, no. 6, pp.10-23. https://doi.org/10.12815/kits.2016.15.6.010
  16. Park, J. S., Hong, S. G. and Kim, J. W.(2017), "A study on science technology trend and prediction using topic modeling", Journal of the Korea Industrial Information Systems Research, vol. 22, no. 4, pp.19-28. https://doi.org/10.9723/JKSIIS.2017.22.4.019
  17. Park, S., Park, S., Jeong, H., Yun, I. and So, J.(2021), "Scenario-mining for level 4 automated vehicle safety assessment from real accident situations in urban areas using a natural language process", Sensors, vol. 21, no. 20, p.6929.
  18. Park, S., So, J. J., Ko, H., Jeong, H. and Yun, I.(2019), "Development of Safety Evaluation Scenarios for Autonomous Vehicle Tests Using 5-Layer Format (Case of the Community Road)", The Journal of the Korea Institute of Intelligent Transport Systems, vol. 18, no. 2, pp.114-128. https://doi.org/10.12815/kits.2019.18.2.114
  19. Roder, M., Both, A. and Hinneburg, A.(2015), "Exploring the space of topic coherence measures", Proceedings of the Eighth ACM International Conference on Web Search and Data Mining, February, pp.399-408.
  20. Sievert, C. and Shirley, K.(2014), "LDAvis: A method for visualizing and interpreting topics", Proceedings of the Workshop on Interactive Language Learning, Visualization, and Interfaces, June, pp.63-70.
  21. Yang, M., Lee, S., Park, K., Choi, K. and Kim, T.(2021), "A Study on Analysis of national R&D research trends for Artificial Intelligence using LDA topic modeling", Journal of Internet Computing and Services, vol. 22, no. 5, pp.47-55. https://doi.org/10.7472/JKSII.2021.22.5.47
  22. Yu, Y. L.(2017), Analysis of media coverage on 2015 revised curriculum policy using big data analysis, Unpublished Doctoral Dissertation, Department of Education, Graduate School of Seoul National University.