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The National Academies

NCHRP 20-102(27) [Active]

Realistic Timing Estimates for Automated Vehicle Implementation
[ NCHRP 20-102 (Impacts of Connected Vehicles and Automated Vehicles on State and Local Transportation Agencies--Task-Order Support) ]

  Project Data
Funds: $149,874
Staff Responsibility: Camille Crichton-Sumners
Research Agency: WSP USA Inc.
Principal Investigator: Steve Kuciemba
Effective Date: 9/29/2022

NOTE

Research in progress

The integration of automated vehicle (AV) technology into surface transportation is expected to reduce environmental impacts and enhance the movement of people and goods in the United States. While there has been significant growth in the development of AV, however, issues related to cybersecurity, data privacy, connectivity, and operating system maintenance costs contribute to its complexity and there is great uncertainty about the rate of deployment. Transportation agencies planning for AV implementation need realistic implementation timelines to aid decision-making. They need to identify a range of estimates—from optimistic to conservative—of market introduction and growth profiles for a broad range of the representative automated driving services that account for limiting factors such as rate of advancement in technologies, level of effort needed to expand operational design domains for each AV application, and the time and conditions for users to adopt new technologies.

The objectives of this research are to (1) identify influencing factors and risks that affect the timing of the deployment of the Society of Automotive Engineers (SAE) Level 4 AV technologies, described in SAE J3016, in diverse scenarios; (2) define operational concept and input parameters for a decision-making model that will estimate deployment timeframes; and (3) develop recommendations for how and at what frequency a model and its parameters should be updated.
 
The research plan shall give consideration to the following concepts: (1) the level of technical effort and investments required by agencies to safely operate AVs within various operational design domains such as urban or rural applications, climate/weather, roadway classifications, interaction with vulnerable road users, topography, geometric design, and infrastructure/asset readiness; (2) the ability of private entities to safely deploy AV technologies while considering barriers related to costs, technological limitations, supply chain, and local regulatory environments; (3) the amount of time and the conditions needed for different sectors of the user population to adopt new AV technologies and services; (4)  historical data on the rate of growth of new features and enhancements (e.g., how these have gone from being optional features only available on expensive vehicles to being standard features on new vehicles and the turnover rate of the domestic vehicle fleet.); (5) passenger and goods movement applications in personal and fleet vehicle operation; (6) reasonable consideration for near term and long-range planning requirements; and (7) the need for decision makers to consider a range of optimistic to conservative assumptions of parameter values in a model. 

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