Safety Considerations for Automated Vehicles

Research Team: Junshan Zhang (lead), Mollie D’Agostino (co-lead), Cooper Michael, Marilia Ramos, and Camila Correa-Jullian

UC Campus(es): UC Davis, UCLA

Problem Statement: In the coming decades, advancements in automated driving systems (ADS) vehicles have the potential to transform communities and mobility. To ensure communities can achieve traffic safety goals as these technologies progress, policymakers and practitioners will need tools and information to proactively design policies and practices that facilitate ADS market growth in a responsible manner. The ADS sector may require new foundational definitions of safety and liability frameworks, and may develop new methods for data collection and analysis. ADS will also influence the workforce, and bring new traffic assistive personnel, such as safety drivers and remote operators. While academic literature, legal and regulatory doctrine have made piecemeal efforts to address these topics, many stakeholders are still working within sectoral silos. Policy makers need multidisciplinary perspectives so they can carefully consider how to advance safety, and address emerging ADS issues.

Project Description: This project aims to foster a multidisciplinary dialogue on ADS safety, primarily through the development of a blueprint for ADS safety policy covering three primary areas: i) general ADS safety, ii) data collection and privacy, and iii) human-machine interaction issues. The development of the blueprint was informed by a synthesis of best practices within three domains i) safety and risk analysis found in engineering doctrine, including consensus-based standards, ii) legal literature and case law, and iii) federal and state regulatory precedent. This project was inspired by and builds upon topics raised in the White House’s Blueprint for an AI Bill of Rights.

Status: In Progress

Budget: $50,000