Knowledge mapping of an artificial intelligence application scenario: A bibliometric analysis of the basic research of data-driven autonomous vehicles

Lei Huang, Miltos Ladikas, Jens Schippl, Guangxi He and Julia Hahn

Technology in Society, 2023, vol. 75, issue C

Abstract: With the rapid development and maturation of basic research in related fields such as deep learning and convolutional neural networks, artificial intelligence (AI) has become a policy hotspot of high interest in all major economies. Furthermore, the governance of application scenarios has become one of the important topics of AI governance policy research. The practice of AI governance policy depends on a change in the paradigm of relevant governance policy research theories, and there is an urgent need to empirically analyze the structural characteristics of the knowledge evolution of AI application scenarios. This study conducts a bibliometric analysis of the basic research trends in autonomous vehicles, which is one of the most important application scenarios of AI. The study empirically analyzes the relevant literature data from the Science Citation Index Expanded (SCI-Expanded) and Social Sciences Citation Index (SSCI) databases of Web of Science, based on both technical and social dimensions through the knowledge mapping analysis tools in Bibliometrix (in R environment). Based on the empirical analysis, the results show that the basic research on autonomous vehicles is characterized by strong data-driven innovation under the influence of AI. The fusion of AI and basic research on autonomous vehicles has become a major driver of knowledge innovation in this domain while there is a lack of the integration of social science research.

Keywords: Artificial intelligence; Autonomous vehicles; Application scenarios; Bibliometrics; Basic research; Knowledge mapping (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:teinso:v:75:y:2023:i:c:s0160791x23001653

DOI: 10.1016/j.techsoc.2023.102360

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