Predicting the Public Adoption of Connected and Autonomous Vehicles

Connected and Autonomous Vehicles (CAV) are gaining increasing importance due to the current needs of modern society for better mobility and societal impact. CAV development and adoption will be driven by Artificial Intelligence (AI) and 5G/6G technologies which will offer increased speed, reduced latency and ubiquity. However, the public is concerned with the concept of handing total control of driving to vehicles. These concerns will inhibit the adoption of CAVs when they become available to the public. In this paper, the authors investigated user adoption of CAVs by collecting quantitative data from potential users based on their preference and inherent concerns towards adoption. They conducted a statistical analysis and applied machine learning techniques to predict the user adoption for CAVs. Their results show that several machine learning approaches were effective in forecasting user adoption for CAVs. The authors have employed Neural Networks, Random Forest, Naïve Bayes and Fuzzy Logic based models and achieved accuracies of 81.76%, 83.63%, 82.15% and 86.38, respectively, in forecasting the public adoption of CAV.

• Record URL:
  http://dx.doi.org/10.1109/TITS.2021.3109846
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/oclc/41297384
• Supplemental Notes:
  • Copyright © 2022, IEEE.
• Authors:
  • Ahmed, Muhammed Lawal
  • Iqbal, Rahat
  • Karyotis, Charalampos
  • Palade, Vasil
  • Amin, Saad Ali
• Publication Date: 2022-2

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: 1680-1688
• Serial:
  • IEEE Transactions on Intelligent Transportation Systems
  • Volume: 23
  • Issue Number: 2
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
  • ISSN: 1524-9050
  • Serial URL: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6979

Subject/Index Terms

• TRT Terms: Autonomous vehicles; Connected vehicles; Highway safety; Machine learning; Predictive models
• Subject Areas: Data and Information Technology; Highways; Safety and Human Factors;

Filing Info

• Accession Number: 01841013
• Record Type: Publication
• Files: TRIS
• Created Date: Mar 29 2022 10:20AM