EAGER: Information Sharing for Auto Insurance and Its Impact on Road Safety in the Era of Connected Automated Driving

About This Grant

This EArly-Concept Grant for Exploratory Research (EAGER) will fund research that investigates the following question: can removing obstacles to data regulation practices for auto insurance improve road safety? Though it has long been known that the accident externality of driving, which refers to potential negative consequences from driving or having a vehicle on the road that affect other people, is significant and not adequately priced, remediating the issue has been challenging. Connectivity has provided both insurers and automakers with an opportunity to price driving risk more effectively by monitoring driver behavior. However, as presently constructed and regulated, auto insurance monitoring programs do not allow for a full use of individual driving data to price insurance. At the root of this missed opportunity to induce safer roads lie concerns related to data use and privacy. A structure that collects data on driver behavior and shares the data with specific automakers and insurers with drivers’ consent could potentially alleviate these privacy concerns while making roads safer. However, to which extent could such a structure contribute meaningfully to increasing road safety? What would the impact be of such a structure on the auto insurance industry? And to which extent would it affect the car manufacturing industry and reshape the conversation around automated driving? Through a series of theoretical and empirical analyses and by engaging the relevant stakeholders, this research will seek to provide answers to these questions. The research will lay the foundation to help inform transportation industry policy making and better road safety management. The research activities will be integrated into teaching and course design, as well as outreach activities for broadening participation in STEM. Research discoveries will be further disseminated through conferences and collaborations with practitioners. The research centers on two main tasks. First, a mathematical model that captures the impact of information availability on insurance pricing, driver behavior, and aggregate accident probability will be developed. This model will be used to study equilibria that emerge on a road or in a road network when more information about driving behavior is shared. Additionally, market structure, incentives for technology adoption, and the interactions between automakers and insurers will be considered. The outcome of these analyses will be a characterization of an upper bound on the benefits of a more holistic use of driving data in insurance pricing. Second, a series of interviews with different industry stakeholders will provide insights on attitudes towards removing obstacles to a wider use of driving behavior data in auto insurance. Together, the research will help inform policymaking and data regulations in auto insurance and road safety in the era of connected and automated vehicles. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.