The Road to Optimal Safety: Crash-Adaptive Regulation of Autonomous Vehicles at the National Highway Traffic Safety Administration

48 Pages Posted: 1 Dec 2023

Date Written: March 7, 2023

Abstract

Autonomous vehicles are now driving people around in cities from San Francisco to Phoenix. But how to regulate the safety risks from these autonomous driving systems (ADS) remains uncertain. While state tort law has traditionally played a fundamental role in controlling car crash risks, this Note argues that the development of novel data tracking and simulation tools by the ADS industry has led to a regulatory paradigm shift: By leveraging these tools for regulatory analysis, the federal National Highway Traffic Safety Administration (NHTSA) could iteratively adapt and improve its regulatory standards after each crash. While many scholars have advanced proposals for how state products liability can adapt to ADS crashes, this Note is the first to propose such a model of “crash-adaptive regulation” for NHTSA and to show that this model will prove superior to tort liability in controlling ADS crash risks. In presenting this new regulatory model, this Note engages with two rich theoretical debates. First, it compares the efficacy of tort liability and agency regulation in controlling ADS crash risks. Second, it evaluates whether ADS safety standards should be set at the federal level or at the state level. It concludes that ADS’ technical characteristics call for an agency regulatory scheme at the federal level and urges NHTSA to build the technological and operational expertise necessary to operate a crash-adaptive regulatory regime.

Keywords: AI, artificial intelligence, autonomous vehicles, self-driving cars, regulation, administrative law, tort

Suggested Citation

Kuate Fodouop, Kevin, The Road to Optimal Safety: Crash-Adaptive Regulation of Autonomous Vehicles at the National Highway Traffic Safety Administration (March 7, 2023). New York University Law Review, Vol. 98, No. 4, 2023, Available at SSRN: https://ssrn.com/abstract=4641963

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
69
Abstract Views
240
Rank
612,399
PlumX Metrics