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My Precious Crash Data: Barriers and Opportunities in Encouraging Autonomous Driving Companies to Share Safety-Critical Data

Hauke Sandhaus, Angel Hsing-Chi Hwang, Wendy Ju, Qian Yang

TL;DR

This paper investigates why safety-critical autonomous-vehicle data are rarely shared and how incentives shape sharing practices. Through twelve insider interviews, it identifies two fundamental barriers: data inherently encode proprietary AV design knowledge and are treated as private competitive assets rather than public goods. It reframes data sharing as an incentives problem and proposes strategies including clarifying public vs private knowledge, designing data tools that decouple data from embedded know-how, and leveraging academic intermediaries and policy frameworks. The work outlines practical pathways to promote data sharing that preserve competitive edges while advancing public safety in autonomous driving.

Abstract

Safety-critical data, such as crash and near-crash records, are crucial to improving autonomous vehicle (AV) design and development. Sharing such data across AV companies, academic researchers, regulators, and the public can help make all AVs safer. However, AV companies rarely share safety-critical data externally. This paper aims to pinpoint why AV companies are reluctant to share safety-critical data, with an eye on how these barriers can inform new approaches to promote sharing. We interviewed twelve AV company employees who actively work with such data in their day-to-day work. Findings suggest two key, previously unknown barriers to data sharing: (1) Datasets inherently embed salient knowledge that is key to improving AV safety and are resource-intensive. Therefore, data sharing, even within a company, is fraught with politics. (2) Interviewees believed AV safety knowledge is private knowledge that brings competitive edges to their companies, rather than public knowledge for social good. We discuss the implications of these findings for incentivizing and enabling safety-critical AV data sharing, specifically, implications for new approaches to (1) debating and stratifying public and private AV safety knowledge, (2) innovating data tools and data sharing pipelines that enable easier sharing of public AV safety data and knowledge; (3) offsetting costs of curating safety-critical data and incentivizing data sharing.

My Precious Crash Data: Barriers and Opportunities in Encouraging Autonomous Driving Companies to Share Safety-Critical Data

TL;DR

This paper investigates why safety-critical autonomous-vehicle data are rarely shared and how incentives shape sharing practices. Through twelve insider interviews, it identifies two fundamental barriers: data inherently encode proprietary AV design knowledge and are treated as private competitive assets rather than public goods. It reframes data sharing as an incentives problem and proposes strategies including clarifying public vs private knowledge, designing data tools that decouple data from embedded know-how, and leveraging academic intermediaries and policy frameworks. The work outlines practical pathways to promote data sharing that preserve competitive edges while advancing public safety in autonomous driving.

Abstract

Safety-critical data, such as crash and near-crash records, are crucial to improving autonomous vehicle (AV) design and development. Sharing such data across AV companies, academic researchers, regulators, and the public can help make all AVs safer. However, AV companies rarely share safety-critical data externally. This paper aims to pinpoint why AV companies are reluctant to share safety-critical data, with an eye on how these barriers can inform new approaches to promote sharing. We interviewed twelve AV company employees who actively work with such data in their day-to-day work. Findings suggest two key, previously unknown barriers to data sharing: (1) Datasets inherently embed salient knowledge that is key to improving AV safety and are resource-intensive. Therefore, data sharing, even within a company, is fraught with politics. (2) Interviewees believed AV safety knowledge is private knowledge that brings competitive edges to their companies, rather than public knowledge for social good. We discuss the implications of these findings for incentivizing and enabling safety-critical AV data sharing, specifically, implications for new approaches to (1) debating and stratifying public and private AV safety knowledge, (2) innovating data tools and data sharing pipelines that enable easier sharing of public AV safety data and knowledge; (3) offsetting costs of curating safety-critical data and incentivizing data sharing.

Paper Structure

This paper contains 30 sections, 2 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Benefits of Sharing Safety-Critical Data
  4. The Lack of Safety-Critical Data Sharing
  5. Barriers to Sharing Safety-Critical Data
  6. Facilitating Safety-Critical Data Sharing
  7. Method
  8. Participants
  9. Interviews
  10. Data Analysis
  11. Findings
  12. What are the participants' AV safety design work practices?
  13. What are surmountable barriers to AV data sharing?
  14. Too Entwined to Share: Risks of Publicizing AV Design Knowledge Are Key Barriers to Data-Sharing
  15. Stratification of data reveals diverse safety paradigms.
  16. ...and 15 more sections

Figures (2)

  • Figure 1: Left: OL 316, DMV report form for AV collisions (page 1/3). Right: DMV form for the general public to report AV incidents.
  • Figure 2: AV developers are reluctant to share data, that encodes precious knowledge, across the industrialized AV data pipeline.