Bridging the Gap: Machine Learning and Vision Neural Networks in Autonomous Vehicles for the Aging Population

As autonomous vehicles (AVs) evolve recently, it is necessary to address the unique needs of the aging population group. They can get a significant benefit from this technology. This scoping review focus the role of machine learning and vision neural networks in autonomous vehicles. A focus on enhancing safety, usability, and trust for elderly users will be mentioned as well. It systematically reviews existing literature to identify how these technologies address the cognitive and physical challenges faced by older adults. The review highlights key advancements in AV technology, such as adaptive interfaces and assistive features. That can enhance the driving experience for the elderly. Additionally, it investigates factors influencing trust and acceptance of AVs among older adults, emphasizing the importance of transparent and user-friendly design. Although, the despite notable progress has been made, the significant gaps remain in understanding how to optimize these technologies to meet the diverse needs of elderly passengers. The review identifies areas for future research, including personalized AV systems and regulatory frameworks that support designs friendly to the elderly. By addressing these gaps, the study aims to contribute to developing autonomous vehicles that are inclusive and accessible. It will make the mobility and quality of life for the aging population increased. This review underscores the importance of integrating machine learning and vision neural networks in designing AVs that cater to the unique needs of older adults. It was also offering valuable insights for researchers, policymakers, and industry stakeholders advancing autonomous vehicle technology.

@article{Tan2024,
  title     = {Bridging the Gap: Machine Learning and Vision Neural Networks in Autonomous Vehicles for the Aging Population},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {10},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Shengsheng Tan},
  doi       = {10.14569/IJACSA.2024.0151030},
  url       = {https://doi.org/10.14569/IJACSA.2024.0151030}
}