Artificial Intelligence for ADAS and Autonomous Vehicles: A Systematic Review
Authors
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Jose Andres Murillo Velez
Facultad de Ciencias Informáticas, Universidad Técnica de Manabí, Portoviejo, Manabí, Ecuador
https://orcid.org/0009-0001-5634-4713
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Roberth Abel Alcívar Cevallos
Facultad de Ciencias Informáticas, Universidad Técnica de Manabí, Portoviejo, Manabí, Ecuador
https://orcid.org/0000-0001-6282-8493
DOI:
https://doi.org/10.33936/isrtic.v10i1.8063Keywords:
Artificial Intelligence, Autonomous vehicles, Advanced driver assistance systems (ADAS), Cybersecurity, Deep learningAbstract
This study examines the impact of Artificial Intelligence (AI) within the automotive industry, with a special focus on Advanced Driver Assistance Systems (ADAS) and the development of autonomous vehicles. To address the safety limitations, present in real-world scenarios, a systematic literature review (SLR) was conducted following the PRISMA protocol, analyzing 50 articles from databases such as IEEE Xplore, Scopus, and SciELO. The results show that deep learning algorithms, particularly the YOLO variants (v2 to v11), achieve accuracy levels exceeding 90% with critical response times of less than 35 ms. However, the research also reveals a significant scientific gap: reliability decreases considerably (30-35%) in pedestrian and traffic sign detection under adverse conditions, reflecting a lack of robustness against potential cybersecurity attacks on VANETs. This work organizes and classifies these technical and organizational challenges, providing quantitative evidence that serves as a basis for outlining future lines of research in cybersecurity and in the diversification of datasets, with a view to ensuring safer autonomous mobility.
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Copyright (c) 2026 Jose Andres Murillo Velez, Roberth Abel Alcívar Cevallos
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