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Eliciting Evolving Topics, Trends and Foresight about Self-driving Cars Using Dynamic Topic Modeling

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1129))

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Abstract

Self-driving technology is part of smart city ecosystems, and it touches a broader research domain. There are advantages associated with using this technology, such as improved quality of life, reduced pollution, and reduced fuel cost to name a few. However, there are emerging concerns, such as the impact of this technology on transportation systems, safety, trust, affordability, control, etc. Furthermore, self-driving cars depend on highly complex algorithms. The purpose of this research is to identify research agendas and innovative ideas using unsupervised machine learning, dynamic topic modeling, and to identify the evolution of topics and emerging trends. The identified trends can be used to guide academia, innovation intermediaries, R&D centers, and the auto industry in eliciting and evaluating ideas. The research agendas and innovative ideas identified are related to intelligent transportation, computer vision, control and safety, sensor design and use, machine learning and algorithms, navigation, and human-driver interaction. The result of this study shows that trending terms are safety, trust, transportation system (traffic, modeling traffic, parking, roads, power utilization, the buzzword smart, shared resources), design for the disabled, steering and control, requirement handling, machine learning, LIDAR (Light Detection And Ranging) sensor, real-time 3D image processing, navigation, and others.

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Notes

  1. 1.

    https://www.bbc.com/news/topics/c90ymkd8lglt/driverless-cars.

  2. 2.

    https://www.bbc.com/news/business-48965315.

  3. 3.

    https://radimrehurek.com/gensim/models/ldaseqmodel.html.

  4. 4.

    https://radimrehurek.com/gensim/models/coherencemodel.html.

  5. 5.

    https://www.ada.gov/pubs/adastatute08.pdf.

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Acknowledgment

The authors wish to thank Alexey Voronov (Ph.D.) and Mahedre DW Amanuel (M.Sc.) at RISE for their support in interpreting the results of the analysis. We would also like to thank Panagiotis Papapetrou (Professor) for offering constructive feedback. This study was conducted as part of the research project IQUAL (2018-04331) funded by Sweden’s Innovation Agency (Vinnova).

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Authors and Affiliations

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

    Workneh Y. Ayele & Gustaf Juell-Skielse

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  1. Workneh Y. Ayele
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  2. Gustaf Juell-Skielse
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Corresponding author

Correspondence to Workneh Y. Ayele .

Editor information

Editors and Affiliations

  1. Faculty of Science and Engineering, Saga University, Saga, Japan

    Kohei Arai

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

    Supriya Kapoor

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

    Rahul Bhatia

Appendix 1

Appendix 1

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Ayele, W.Y., Juell-Skielse, G. (2020). Eliciting Evolving Topics, Trends and Foresight about Self-driving Cars Using Dynamic Topic Modeling. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Advances in Information and Communication. FICC 2020. Advances in Intelligent Systems and Computing, vol 1129. Springer, Cham. https://doi.org/10.1007/978-3-030-39445-5_37

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