The National Academies

NCHRP 20-102(24) [Anticipated]

Infrastructure Modifications to Improve the Operational Domain of Automated Vehicles
[ NCHRP 20-102 (Impacts of Connected Vehicles and Automated Vehicles on State and Local Transportation Agencies--Task-Order Support) ]

  Project Data
Funds: $1,000,000
Staff Responsibility: Christopher McKenney
Comments: In development
Fiscal Year: 2019

This project has been tentatively selected and a project statement (request for proposals) is expected in . The project statement will be available on this site. The problem statement below will be the starting point for a panel of experts to develop the project statement.


Connected and automated vehicle (CAV) technologies are quickly advancing, resulting for a growing need for roadways and infrastructure to begin considering the impacts of CAVs as well as the AV deployments that will be limited to operational domains (OD) where vehicles can readily demonstrate safe operation. Limitations in ODs may stem from factors that challenge an automated vehicle’s ability to accurately perceive the surrounding environment and effectively make decisions, such as adverse weather and degraded lane markings.

While city and state agencies may not be able to control some of these OD limitations, they can take steps to modify infrastructure to improve benefits of the OD, such as economic opportunities and accessibility, connect strategic locations, and simply be an attractive place for AV testing and deployment. Infrastructure modifications may include infrastructure-to-vehicle (I2V) communication systems, signage, and civil infrastructure such as curbs and barriers to provide different levels of segregation between the CAVs and other road users. Uniform and well-maintained traffic control devices, such as lane markings and traffic signs, may improve the extent of AV OD.

AV functionality depends on perception algorithms to accurately detect and respond to infrastructure based on sensor information. Just as humans learn to drive through experience, many perception algorithms use machine learning that is trained to detect and classify objects and events based on past experience. Atypical conditions are more challenging for perception systems. Segregation can create a less complex environment by eliminating mixed road users that can be unpredictable, or can even take advantage of an AV’s conservative behavior. Infrastructure owners and operators want to understand how the OD of near term deployments may benefit from infrastructure modifications.

In order to achieve a smoother transition to CAV transportation, state and local agencies must understand how and when traditional highway and street infrastructure may be affected and the impacts this could have on design, operations, maintenance, and policy.


This research will review and identify potential infrastructure modifications that could improve the OD of AVs. The analysis will:

• Impact with modifications to Design Standards and Guidelines with regards to procurement and regulation requirements requiring flexibility to fast-changing needs, functional requirements, and product availability;

• CAVs physical and digital interaction with Digital Infrastructure/Connectivity in ways that impact their standards and practices. This could include the impact on maintaining roadway markings and signage in a visible/retroreflective way, and supplementing these by newer technologies as more vehicles are equipped with automated driving system (ADS) technology;

• Reviewing types of changes with Variable Roadway Features with consideration for alternative methods/technologies. This could include Urban Design and the needed changes in curb space necessary to accommodate alternative pick-up/drop-off scenarios;

• Investigate aspects of technology and operation that influence OD, such as vehicle connectivity, dedicated lanes, AV sensors, perception algorithms, operating speeds, and pickup/drop-off locations;

• Review lessons learned from AV testing and deployment activities;

• Identify and characterize aspects of physical and digital infrastructure elements that may limit OD, such as V2I, curbs, barriers, reflectivity, geometry, and quality of data; and

• Develop implementation guidance for infrastructure modifications, including potential improvements to OD, costs, and impacts to other road users.


Outputs of this research will update and expand the guidance for state and local transportation agencies in evaluating and—if necessary—adapting their standards, practices, and institutional frameworks for roadway and infrastructure, urban design, and related maintenance and operations—to reflect the deployment of connected and automated vehicle technologies.

This research will also provide state and local transportation agencies with guidance on how to modify infrastructure to improve the OD of AVs. The assessment will provide insights based on AV technology and operations. It will provide a catalogue of potential infrastructure modifications, and describe how these modifications will impact ODs. Key considerations for prioritizing potential modifications will be provided to infrastructure owners and operators to enable decision making and investments.

The study should include work zones and dockless shared personal mobility devices. In addition to highly automated vehicles, the study should include automated driving system (ADS) technologies that are available now. The scope should include the digital infrastructure, data management, and work zones.

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