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

  • Workneh Y. AyeleEmail author
  • Gustaf Juell-Skielse
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1129)


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.


Dynamic Topic Modeling Topic modeling NLP Self-driving cars Topic evolution Topic trends Forecasting in topics 



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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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Computer and Systems SciencesStockholm UniversityStockholmSweden

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