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Identifying Emerging Trends and Temporal Patterns About Self-driving Cars in Scientific Literature
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
Stockholm University, Faculty of Social Sciences, Department of Computer and Systems Sciences.
2020 (English)In: Advances in Computer Vision: Proceedings of the 2019 Computer Vision Conference (CVC) / [ed] Kohei Arai, Supriya Kapoor, Springer, 2020, Vol. 2, p. 355-372Conference paper, Published paper (Refereed)
Abstract [en]

Self-driving is an emerging technology which has several benefits such as improved quality of life, crash reductions, and fuel efficiency. There are however concerns regarding the utilization of self-driving technology such as affordability, safety, control, and liabilities. There is an increased effort in research centers, academia, and the industry to advance every sphere of science and technology yet it is getting harder to find innovative ideas. However, there is untapped potential to analyze the increasing research results using visual analytics, scientometrics, and machine learning. In this paper, we used scientific literature database, Scopus to collect relevant dataset and applied a visual analytics tool, CiteSpace, to conduct co-citation clustering, term burst detection, time series analysis to identify emerging trends, and analysis of global impacts and collaboration. Also, we applied unsupervised topic modeling, Latent Dirichlet Allocation (LDA) to identify hidden topics for gaining more insight about topics regarding self-driving technology. The results show emerging trends relevant to self-driving technology and global and regional collaboration between countries. Moreover, the result form the LDA shows that standard topic modeling reveals hidden topics without trend information. We believe that the result of this study indicates key technological areas and research domains which are the hot spots of the technology. For the future, we plan to include dynamic topic modeling to identify trends.

Place, publisher, year, edition, pages
Springer, 2020. Vol. 2, p. 355-372
Series
Advances in Intelligent Systems and Computing, ISSN 2194-5357, E-ISSN 2194-5365 ; 944
Keywords [en]
Self-driving car, Clustering, Term burst detection, Time series analysis, Visual analytics
National Category
Computer Vision and Robotics (Autonomous Systems)
Research subject
Computer and Systems Sciences
Identifiers
URN: urn:nbn:se:su:diva-179861DOI: 10.1007/978-3-030-17798-0_29ISBN: 978-3-030-17797-3 (print)ISBN: 978-3-030-17798-0 (electronic)OAI: oai:DiVA.org:su-179861DiVA, id: diva2:1413966
Conference
Computer Vision Conference (CVC), Las Vegas, NV, USA, 25-26 April, 2019
Available from: 2020-03-11 Created: 2020-03-11 Last updated: 2020-03-13Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
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