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- Citation Author(s):
-
ZhichangZhang
China Medical University - Submitted by:
- Wu Yuhan
- Last updated:
- Tue, 07/02/2024 - 03:07
- DOI:
- 10.21227/p7gv-c486
- License:
117 Views- Categories:
- Keywords:
Abstract
The objective of this study is to conduct a systematic examination of research trends and hotspots in the domain of autonomous vehicles leveraging deep learning, through a bibliometric analysis. By scrutinizing research publications from various countries spanning 2017 to 2023, this paper aims to summarize effective research methodologies and identify potential innovative pathways to foster further advancements in AVs research. A total of 1,239 publications from the core collection of scientific networks were retrieved and utilized to construct a clustering network. Employing Cite Space V and VOSviewer tools, we have identified the countries, institutions, journals, co-cited documents, keywords, and research hotspots associated with self-driving cars. Our findings indicate a rapid increase in publications related to AVs methods, involving 73 countries, 1,376 institutions, and 247 journals. This paper provides a summary of the current research status of self-driving cars and analyzes the related research hotspots and future trends, with the aim of offering a resource for future research endeavors and encouraging more researchers to engage in the study of autonomous vehicles.
The data for our analysis was extracted on January 7, 2024, from the Web of Science Core Collection (WoSCC), a highly respected and widely utilized repository for scientific information. Our analysis confirmed the citation counts and search results from the WoSCC database through independent verification by two authors. From a corpus of 1,239 scholarly manuscripts, we generated a clustering network. Our literature review spanned from 2017 to 2023, utilizing the following search query: (TS=("Deep Learning" OR "Convolutional Neural Network*" OR "Recurrent Neural Network*" OR "Fully Convolutional Network*" OR "Generative Adversarial Network" OR "Reinforcement Learning" OR "Back Propagation" OR "Fully Neural Network" OR "Recursive Neural Network" OR "Autoencoder" OR "Deep Belief Network" OR "Restricted Boltzmann machine" OR "Transformers" OR "Graph Convolution Networks") AND TS=("Autonomous Vehicles")
