Automated Driving Systems (ADS) Operational Behavior and Traffic Regulations Information – Concept of Use

Garrett, Kyle; Ma, Jiaqi; Morgan, Abigail · 2020 · ROSA P / United States. Federal Highway Administration

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Summary

This report, produced by the Federal Highway Administration (FHWA), addresses the critical challenge of integrating Automated Driving Systems (ADS) into the U.S. transportation network amidst inconsistent and fragmented traffic regulations. The research is motivated by the fact that traffic laws are enacted at the State and local levels, resulting in significant variability in format, structure, and implementation across jurisdictions. This inconsistency creates substantial barriers for ADS developers, who require precise, standardized regulatory data to program vehicle behavior safely and efficiently. Without a unified data framework, ADS software cannot reliably interpret varying rules—such as specific lane usage or signal timing interpretations—leading to potential operational inefficiencies or safety risks. The study aims to define the requirements for an ADS-ready traffic laws and regulations database and establish the necessary access and exchange protocols to support the ADS ecosystem. The methodology involves a comprehensive analysis of existing traffic laws, regulatory databases, and ADS operational behaviors. The authors examine the current landscape from compilation, State, and local perspectives, identifying gaps in existing resources like the Uniform Vehicle Code (UVC) and various digital compilations. The report outlines stakeholder needs from legislative bodies, infrastructure owner-operators, enforcement agencies, and ADS developers. It proposes a conceptual framework for a traffic regulation data system, defining specific data elements, logical structures, and interfaces. The study utilizes use cases to illustrate how this framework would function in practice, covering scenarios such as complying with speed limits, interpreting non-standard pedestrian crosswalks, and managing school bus interactions. Additionally, the report considers the integration of this data with cooperative driving automation standards and existing initiatives like the Work Zone Data Exchange. Key findings highlight that while most national traffic regulations are broadly consistent, the lack of a complete, structured digital database for these rules hinders ADS development. The report identifies that ADS behavior is directly influenced by regulatory nuances; for instance, differing interpretations of yellow signal timing or left-lane usage can drastically alter vehicle trajectory planning. The proposed solution is a comprehensive, structured database framework that provides consistent indications of traffic regulations. This framework is designed to support the development, testing, and operation of ADS by ensuring compliance with jurisdictional laws. The study also emphasizes the need for collaboration among State and local stakeholders and ADS subject matter experts to maintain the database’s accuracy and relevance. The significance of this work lies in its contribution to the safe and effective deployment of automated vehicles. By establishing a standardized approach to traffic regulation data, the framework facilitates interoperability and reduces the complexity for ADS developers. It supports the broader goal of creating robust digital transportation systems that can accommodate ADS integration. The report concludes by discussing implementation implications, including design, deployment, testing, security, and privacy considerations. It underscores that a unified regulations data framework is essential for realizing the vision of safe ADS operations and for enabling infrastructure owner-operators to prepare their systems for the advent of automated driving.

Key finding

The report proposes a concept of use for an ADS regulations data framework to address jurisdictional inconsistencies in traffic laws that hinder automated vehicle integration.

Methodology

review

Provenance

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Topics

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• odd communication
• driverless ads
• situational awareness

Information type

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• Methodological Resource: dataset resource
• Theoretical Contribution: conceptual framework, computational model