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Systematic Review

Deep Learning and Autonomous Vehicles: Strategic Themes, Applications, and Research Agenda Using SciMAT and Content-Centric Analysis, a Systematic Review

by
Fábio Eid Morooka
1,
Adalberto Manoel Junior
1,
Tiago F. A. C. Sigahi
2,*,
Jefferson de Souza Pinto
1,3,
Izabela Simon Rampasso
4 and
Rosley Anholon
1
1
School of Mechanical Engineering, State University of Campinas, Campinas 13083-860, Brazil
2
Institute of Science and Technology, Federal University of Alfenas, Poços de Caldas 37715-400, Brazil
3
Federal Institute of Education, Science and Technology of São Paulo, Bragança Paulista 12903-000, Brazil
4
Departamento de Ingeniería Industrial, Universidad Católica del Norte, Antofagasta 0610, Chile
*
Author to whom correspondence should be addressed.
Mach. Learn. Knowl. Extr. 2023, 5(3), 763-781; https://doi.org/10.3390/make5030041
Submission received: 9 June 2023 / Revised: 26 June 2023 / Accepted: 11 July 2023 / Published: 13 July 2023
(This article belongs to the Section Thematic Reviews)
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Abstract

Applications of deep learning (DL) in autonomous vehicle (AV) projects have gained increasing interest from both researchers and companies. This has caused a rapid expansion of scientific production on DL-AV in recent years, encouraging researchers to conduct systematic literature reviews (SLRs) to organize knowledge on the topic. However, a critical analysis of the existing SLRs on DL-AV reveals some methodological gaps, particularly regarding the use of bibliometric software, which are powerful tools for analyzing large amounts of data and for providing a holistic understanding on the structure of knowledge of a particular field. This study aims to identify the strategic themes and trends in DL-AV research using the Science Mapping Analysis Tool (SciMAT) and content analysis. Strategic diagrams and cluster networks were developed using SciMAT, allowing the identification of motor themes and research opportunities. The content analysis allowed categorization of the contribution of the academic literature on DL applications in AV project design; neural networks and AI models used in AVs; and transdisciplinary themes in DL-AV research, including energy, legislation, ethics, and cybersecurity. Potential research avenues are discussed for each of these categories. The findings presented in this study can benefit both experienced scholars who can gain access to condensed information about the literature on DL-AV and new researchers who may be attracted to topics related to technological development and other issues with social and environmental impacts.
Keywords: artificial intelligence; deep learning; autonomous vehicles; autonomous driving; systematic review; research agenda artificial intelligence; deep learning; autonomous vehicles; autonomous driving; systematic review; research agenda

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MDPI and ACS Style

Morooka, F.E.; Junior, A.M.; Sigahi, T.F.A.C.; Pinto, J.d.S.; Rampasso, I.S.; Anholon, R. Deep Learning and Autonomous Vehicles: Strategic Themes, Applications, and Research Agenda Using SciMAT and Content-Centric Analysis, a Systematic Review. Mach. Learn. Knowl. Extr. 2023, 5, 763-781. https://doi.org/10.3390/make5030041

AMA Style

Morooka FE, Junior AM, Sigahi TFAC, Pinto JdS, Rampasso IS, Anholon R. Deep Learning and Autonomous Vehicles: Strategic Themes, Applications, and Research Agenda Using SciMAT and Content-Centric Analysis, a Systematic Review. Machine Learning and Knowledge Extraction. 2023; 5(3):763-781. https://doi.org/10.3390/make5030041

Chicago/Turabian Style

Morooka, Fábio Eid, Adalberto Manoel Junior, Tiago F. A. C. Sigahi, Jefferson de Souza Pinto, Izabela Simon Rampasso, and Rosley Anholon. 2023. "Deep Learning and Autonomous Vehicles: Strategic Themes, Applications, and Research Agenda Using SciMAT and Content-Centric Analysis, a Systematic Review" Machine Learning and Knowledge Extraction 5, no. 3: 763-781. https://doi.org/10.3390/make5030041

APA Style

Morooka, F. E., Junior, A. M., Sigahi, T. F. A. C., Pinto, J. d. S., Rampasso, I. S., & Anholon, R. (2023). Deep Learning and Autonomous Vehicles: Strategic Themes, Applications, and Research Agenda Using SciMAT and Content-Centric Analysis, a Systematic Review. Machine Learning and Knowledge Extraction, 5(3), 763-781. https://doi.org/10.3390/make5030041

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