Predicting the Public Adoption of Connected and Autonomous Vehicles

Mohammed Lawal Ahmed, Rahat Iqbal, Charalampos Karyotis, Vasile Palade, Saad Ali Amin

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

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, we investigated user adoption of CAVs by collecting quantitative data from potential users based on their preference and inherent concerns towards adoption. We conducted a statistical analysis and applied machine learning techniques to predict the user adoption for CAVs. Our results show that several machine learning approaches were effective in forecasting user adoption for CAVs. We 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.
Original languageEnglish
Pages (from-to)1680-1688
Number of pages9
JournalIEEE Transactions on Intelligent Transportation Systems
Volume23
Issue number2
Early online date11 Oct 2021
DOIs
Publication statusPublished - 1 Feb 2022

Bibliographical note

Publisher Copyright:
IEEE

Keywords

  • Autonomous automobiles
  • Autonomous vehicles
  • CAV adoption.
  • Connected and autonomous vehicles
  • Machine learning
  • Predictive models
  • Roads
  • Safety
  • Vehicles
  • fuzzy logic
  • machine learning

ASJC Scopus subject areas

  • Automotive Engineering
  • Mechanical Engineering
  • Computer Science Applications

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