Beneficially Constraining Autonomous Vehicle Regulation in the US & EU

• In Event: Bayesian Case Studies in International and Comparative Politics

Abstract

Connected & Autonomous Vehicles (CAVs) are the application of cutting-edge machine learning and computer vision to vehicles on public roads. As such, they collide two regulatory regimes (highway safety & information technology), two industrial histories (automotive & software), and two communities (the transportation & tech sectors). These collisions bring to light two different imaginaries of the proper relationship between regulation and innovation: a transportation sector who works with powerful regulators in order to gain public acceptance of certified new technologies and a tech sector who disrupts regulators in order to win public buy-in through whizzbang new features. This chapter demonstrates that by uniting transportation and tech, CAVs force a reckoning between these different imaginaries leading entrepreneurs, innovators, and regulators to craft new ones based on beneficial constraints. In both the US and EU, these new imaginaries unite the need for guardrails against known risks in order to win public acceptance (constraints) with large space for innovative elaboration towards known and unknown potential benefits (beneficial). A variety of specific imaginaries are contending for dominance but all claim to enhance safety, whether by promoting or limiting CAVs. To characterize and classify these new imaginaries, this chapter draws upon Bayesian Type Validation (BayesTV) of archival evidence as well as elite interviews with stakeholders in the CAV sector.

Author

• Konrad Posch, University of San Francisco