Mutual Benefit: The Case for Sharing Autonomous Vehicle Data with the Public

TL;DR

The paper argues that data collected from on-road autonomous-vehicle testing, which currently primarily benefits private developers, should be shared with the public through a trusted intermediary as compensation and risk-mitigation. It grounds the argument in ethical frameworks (Belmont Report and the Common Rule) and shows that sharing can reduce redundant testing and enable broader societal benefits. The authors categorize AV data into sensory, modeled, logged, and aggregate forms, and review current sharing practices in Singapore, Sweden, California, and Massachusetts, highlighting governance gaps. They propose a governance blueprint—proportional disclosure, open standards (ASAM, ISO), third-party stewardship, and regulatory oversight—and discuss privacy, trade secrets, and GDPR-related considerations to guide policy.

Abstract

Autonomous driving is a widely researched technology that is frequently tested on public roads. The data generated from these tests represent an essential competitive element for the respective companies moving this technology forward. In this paper, we argue for the normative idea that a part of this data should more explicitly benefit the general public by sharing it through a trusted entity as a form of compensation and control for the communities that are being experimented upon. To support this argument, we highlight what data is available to be shared, make the ethical case for sharing autonomous vehicle data, present case studies in how AV data is currently shared, draw from existing data-sharing platforms from similar transportation industries to make recommendations on how data should be shared and conclude with arguments as to why such data-sharing should be encouraged.