DOI QR코드

DOI QR Code

자율주행과 공간정보의 빅데이터 기반 연계성 분석을 통한 동향 및 예측에 관한 연구

A study on trends and predictions through analysis of linkage analysis based on big data between autonomous driving and spatial information

  • 조국 (한국국토정보공사 공간정보연구원) ;
  • 이종민 (한국국토정보공사 공간정보기획부) ;
  • 김종서 (서울경제신문사 편집국) ;
  • 민규식 (전주대학교 부동산학과)
  • 투고 : 2020.10.03
  • 심사 : 2020.11.25
  • 발행 : 2020.12.30

초록

자율주행 분야 글로벌 동향 파악 및 공간정보 서비스 활성화 방안 도출을 위해 빅데이터 분석방법을 활용하였다. 사용된 빅데이터는 뉴스기사와 특허문헌을 상호 연계하여 활용하고, 뉴스 기사를 통한 동향 분석, 특허문헌 정보를 활용한 기술 분석이 진행 되었다. 본 논문에서는 자율주행에 대한 주요 뉴스에서 토픽모델을 기반으로 한 LDA(Latent Dirichlet Allocation)를 활용하여 빅데이터화 하고 주요 단어를 추출하였다. 특허정보의 주요 단어를 기반으로 적용된 워드넷(WordNet)을 활용하여 공간정보와 연계성 분석, 글로벌 기술 동향 분석을 실시하고 공간정보 분야의 동향 분석 및 예측을 실시하였다. 본 논문에서는 주요뉴스와 특허문헌 정보를 기반으로 한 빅데이터 분석방법으로 자율주행 분야와 공간정보와의 연계성 분석을 통하여 최신 동향과 미래를 예측하는 방법을 제시한다. 빅데이터 분석으로 도출된 자율주행 분야 공간정보의 글로벌 동향은 플랫폼 얼라이언스, 비지니스 파트너쉽, 기업 인수합병, 합작회사 설립, 표준화 및 기술개발로 도출되었다.

In this paper, big data analysis method was used to find out global trends in autonomous driving and to derive activate spatial information services. The applied big data was used in conjunction with news articles and patent document in order to analysis trend in news article and patents document data in spatial information. In this paper, big data was created and key words were extracted by using LDA (Latent Dirichlet Allocation) based on the topic model in major news on autonomous driving. In addition, Analysis of spatial information and connectivity, global technology trend analysis, and trend analysis and prediction in the spatial information field were conducted by using WordNet applied based on key words of patent information. This paper was proposed a big data analysis method for predicting a trend and future through the analysis of the connection between the autonomous driving field and spatial information. In future, as a global trend of spatial information in autonomous driving, platform alliances, business partnerships, mergers and acquisitions, joint venture establishment, standardization and technology development were derived through big data analysis.

키워드

과제정보

본 논문은 2018년 한국국토정보공사의 "자율주행분야 공간정보 지원 및 대응전략 실행계획 마련" 사업 수행 내용과 산업부 자동차산업핵심기술개발사업의 "지능형자동차 인식기술 개발 지원을 위한 공개용 표준 DB 구축 및 평가시스템 개발" 과제(No. 10052941)를 통해 수행되었습니다.

참고문헌

  1. Kang SH, Park JM. 2015. Semantic Similarity Measures Between Words within a Document using WordNet. Journal of the Korea Academia-Industrial cooperation Society. 16(11):7718-7728. https://doi.org/10.5762/KAIS.2015.16.11.7718
  2. Kim KR, Song HJ, Moon NM. 2017. Topic modeling for automatic classification of learner question and answer in teaching-learning support system. Journal of Digital Contents Society. 18(2): 339-346. https://doi.org/10.9728/dcs.2017.18.2.339
  3. Kim DW, Lee SW. 2017. News Topic Extraction based on Word Similarity. Journal of KIISE. 44(11): 1138-1148. https://doi.org/10.5626/JOK.2017.44.11.1138
  4. Kim SK, Jang SY. 2016. A Study on the Research Trends in Domestic Industrial Engineering using Topic Modeling. Journal of the Korea Management Engineers Society. 21(3):71-95.
  5. Kim TK, Choi HR, Lee HC. 2016. A Study on the Research Trends in Fintech using Topic Modeling. Journal of the Korea Academia-Industrial cooperation Society. 17(11):670-681. https://doi.org/10.5762/KAIS.2016.17.11.670
  6. Kim HK, Ahn JW. 2019. The Analysis of Research Trends in Technology to the Fourth Industrial Revolution using SNA. Journal of Cadastre & Land InformatiX. 49(1):113-121. https://doi.org/10.22640/LXSIRI.2019.49.1.113
  7. Park GK. 2017. A Study on the Prediction of Autonomous Driving Technology Development in R & D Field from Patent Citation Information : Focus on Automotive Ethernet network technology in Electricity field[Thesis]. Korea University. p. 36-41.
  8. Park JS, Hong SG, Kim JW. 2017. (A Study on Science Technology Trend and Prediction Using Topic Modeling. Journal of the Korea Industrial Information Systems Research. 22(4): 19-28. https://doi.org/10.9723/jksiis.2017.22.4.019
  9. Yi CH. 2015. A Spatial Analysis on the Formation and Dissolution of Start-up Firms in the Seoul Metropolitan Region. Journal of Cadastre & Land InformatiX. 45(1): 241-256. https://doi.org/10.22640/LXSIRI.2015.45.1.241
  10. Lim SY. 2019. Trends in Core Technologies of AIoT: Big Data Analysis[Thesis]. Yonsei University. pp. 1-13.
  11. Jang CH, Jang JY, Song JM. 2018. Analysis of Impacts of Autonomous Vehicles and Carsharing on Spatial Configuration in Urban Areas: Focusing on Parking Demand. The Korea Spatial Planning Review. 99. 151-170. https://doi.org/10.15793/kspr.2018.99..009
  12. Jung BK, Kim JW, Yoon JH. 2015. Patent-based competitive intelligence analysis of augmented reality technology: Application of toic modeling. Journal of the Korean Institute of Indurstrial Engineers. 2265-2270.
  13. Junh YH, Seo MS, Yoo HH. 2017. Significance Analysis of Yellow Dust Related Disease Using Tweet Data. Journal of Cadastre & Land InformatiX. 47(1):267-276. https://doi.org/10.22640/lxsiri.2017.47.1.267
  14. Wu Zhibiao, Martha Palmer. 1994. Verb semantics and lexical selection. Proceedings of the 32nd Annual Meeting of the Association for Computational Linguistics. Las Cruces, New Mexico. 133-138.

피인용 문헌

  1. 다양한 정밀도로지도의 자율주행 적용을 위한 데이터 모델 변환 방안 연구 vol.51, pp.1, 2021, https://doi.org/10.22640/lxsiri.2021.51.1.39