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

Shunxi Li, Pang-Chieh Sui, Jinsheng Xiao and Richard Chahine

Transportation Research Part A: Policy and Practice, 2019, vol. 124, issue C, 573-586

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.

Keywords: Autonomous driving; Highly automated vehicle; Safety; Security; Policy (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (15)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0965856418300430
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:transa:v:124:y:2019:i:c:p:573-586

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/supportfaq.cws_home/regional
https://shop.elsevie ... _01_ooc_1&version=01

DOI: 10.1016/j.tra.2018.05.010

Access Statistics for this article

Transportation Research Part A: Policy and Practice is currently edited by John (J.M.) Rose

More articles in Transportation Research Part A: Policy and Practice from Elsevier
Bibliographic data for series maintained by Catherine Liu ().