Barriers to the Sustainable Adoption of Autonomous Vehicles in Developing Countries: A Multi-Criteria Decision-Making Approach

The acceptance of AI-based intelligent transportation systems requires addressing the existing barriers and the adoption of macro-decisions and policies by policymakers and governments. This study evaluates the potential barriers to the adoption of Autonomous Vehicles (AVs) in developing countries by considering the sustainability dimensions. The barriers are identified by conducting a comprehensive literature review and studying the academic experts' opinions in related industries. By identifying the main barriers to the sustainable adoption of AVs, a synthesized approach of the Rough Best-Worst Method (RBWM) and Interval-Rough Multi-Attributive Border Approximation Area Comparison (IR-MABAC) is utilized for weighting and evaluating each barrier in this context. According to the results of this study, the “inflation rate”, “lack of internet connection quality”, and “learning challenges and difficulties to use the AVs” are the top challenges and barriers to the AV adoption which need to be considered by policymakers. As the main contribution of this research, we provide efficient insights on a macro policy scale for decision-makers with respect to the main barriers to the implementation of AVs technology. From the AVs literature and to the best of our knowledge, this is the first study of its kind that considers the barriers to the AV technology implementation through the sustainability concept.