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Identifying Emerging Trends and Temporal Patterns About Self-driving Cars in Scientific Literature

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Advances in Computer Vision (CVC 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 944))

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Abstract

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.

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Notes

  1. 1.

    https://www.rstudio.com/products/rstudio/download/

  2. 2.

    http://cluster.ischool.drexel.edu/~cchen/citespace/download/

  3. 3.

    https://www.r-project.org

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Author information

Authors and Affiliations

  1. Department of Computer and Systems Science, Stockholm University, Stockholm, Sweden

    Workneh Y. Ayele & Imran Akram

Authors
  1. Workneh Y. Ayele
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  2. Imran Akram
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Corresponding author

Correspondence to Workneh Y. Ayele .

Editor information

Editors and Affiliations

  1. Saga University, Saga, Saga, Japan

    Kohei Arai

  2. The Science and Information (SAI) Organization, Bradford, West Yorkshire, UK

    Supriya Kapoor

Appendix 1: Top 87 Terms with the Strongest Citation Bursts

Appendix 1: Top 87 Terms with the Strongest Citation Bursts

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Ayele, W.Y., Akram, I. (2020). Identifying Emerging Trends and Temporal Patterns About Self-driving Cars in Scientific Literature. In: Arai, K., Kapoor, S. (eds) Advances in Computer Vision. CVC 2019. Advances in Intelligent Systems and Computing, vol 944. Springer, Cham. https://doi.org/10.1007/978-3-030-17798-0_29

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