Modeling of Barriers to the Adoption of Autonomous Vehicles: DEMATEL Method †

Abstract

Autonomous vehicles (AVs) have the potential to increase safety while reducing energy use, pollution, and traffic congestion, to name a few positive effects. Industries, however, are having trouble implementing AVs. The goal of this study was to pinpoint and analyze the obstacles preventing the widespread use of AVs. To do this, a comprehensive review of the literature was conducted to identify the barriers, which were later confirmed by a panel of experts. There were five issues that needed to be addressed: a lack of infrastructure, funding limitations for manufacturing, low customer acceptance, security breach concerns, and potential employment effects. After these barriers were decided upon, the Decision-Making Trial and Evaluation Laboratory method was chosen to model them. The DEMATEL approach makes use of the expertise of groups and experts while relying on matrix tools and graph theory. It develops a visual framework that emphasizes the causal connections between various factors. A DEMATEL digraph is also presented which helps to identify which barrier is the most crucial barrier. Priority ranking was applied to the identified barriers and categorization of barriers was also performed in this study. Two categories were formed, namely, cause and effect barrier categories. Based on the results of DEMMATEL, the lack of funds for manufacturing AVs and the lack of infrastructure are the most crucial barriers to AV adoption. Industries should focus on the cause group barriers first as they run the system. By eliminating cause group barriers, the impact of effect barriers can be reduced. Implications and future directions were provided in the current study.