Policy formulation for highly automated vehicles: Emerging importance, research frontiers and insights

Abstract

Highly automated vehicles (HAVs) generate the optimistic prospect of future smart mobility together with the disruptive influence of traditional policies. Formulating appropriate policies based on applicable methods are necessary to cope with the potential uncertainties of HAVs. By reviewing the literature in a structural manner, this paper analyzes the emerging importance and research frontiers in formulating HAV policies and presents insights gained from three major methods of dealing with uncertain, dynamic, and evolving transport problems. First, the formulation of HAV policy is important for at least three reasons: it may accelerate the development and control of potential uncertainties of HAV, balance technology innovations with traffic security, and provide a steady and efficient migration from human drivers to automated driving systems. Second, current research focuses mainly on the role of government, licensing and testing standards, certification, reliability, policy interventions, public health, legal challenges, and restrictive or supportive policies. A common research framework and methodology of HAV systems has not yet been established to deal with the uncertainties of technology security. Finally, three potential methods of formulating HAV policy are identified herein, namely (1) the backcasting method, which could determine the future of HAV objectives and pathways; (2) the dynamic adaptive method, which makes the policy transition to HAV systems more organic and dynamic; and (3) the policy transfer and migration method, which provides a clear vision of the adaptation procedure. These methods are used in different circumstances to formulate HAV policies. Each method has its pros and cons. The present review provides insights into formulating future HAV policies using these methods.