Machine learning and vision neural networks in autonomous vehicles for the aging population: A scoping review protocol
DOI:
https://doi.org/10.33448/rsd-v13i10.47019Keywords:
Autonomous Vehicle, Machine Learning, Vision Neural Networks, Human-Computer Interaction, Artificial Intelligence.Abstract
This scoping review aims to systematically map the current body of literature on the role of Machine Learning (ML) and Vision Neural Networks (VNN) in enhancing the usability and accessibility of Autonomous Vehicles (AVs) for elderly and disabled users. While AV technology has advanced significantly in recent years, the solutions of how these technologies can address the unique challenges faced by these vulnerable populations still in an undeveloped or underdeveloped stage. For example, cognitive decline, physical limitations, and lower trust in automated systems. The review will investigate how ML and VNN contribute to improving safety, usability, accessibility, and trust in AVs, focusing on studies published between 2020 and 2024. A comprehensive search will be conducted across four major databases, which are PubMed, IEEE Xplore, Scopus, and Google Scholar. The language of targeting peer-reviewed empirical studies and reviews must be written in English. Data will be extracted using a standardized form and synthesized through a descriptive analytical framework to identify key themes, trends, and gaps in the literature. The findings will offer valuable insights into how AV technologies can be further optimized for elderly and disabled users. It will guide future research and informing the development of more inclusive, safe, and trustworthy AV systems. Therefore, they can promote greater mobility and independence for these populations.
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