Policy Learning and the Diffusion of Autonomous Vehicle Policy in the American States

Abstract

This study examines the multifaceted mechanisms driving the adoption of autonomous vehicle (AV) policy in the United States. Drawing from diffusion and network governance theories, it explores the intricate interplay between state governments, private industry, and shared mobility services in shaping AV policies. Several states like California, Nevada, and Texas have emerged as frontrunners in AV testing and innovation. However, policymakers grapple with unique challenges related to AV technology, including safety, liability, and infrastructure enhancement, necessitating innovative policy solutions. As AVs signify a transformative shift in transportation, state governments assume a pivotal role in policy innovation and experimentation. Using a blend of dyadic quantitative analysis and qualitative interviews, this study examines to what extent policy learning predominantly drives AV policy adoption. In doing so, we explore specific diffusion mechanisms, including policy learning and competitive dynamics, to understand how they shape the early adoption of AV policies across states. We find substantial evidence for interstate learning facilitated by intergovernmental organizations and the private sector.