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Project Details

Title:Evaluating the Impacts of Deploying Automated Roads for Infrastructure-Enabled Autonomous Vehicles
Principal Investigators:Ziqi Song
University:Utah State University
Grant #:69A3551747108 (FAST Act)
Project #:MPC-624
RiP #:01733134
RH Display ID:12743
Keywords:autonomous vehicles, equilibrium (systems), highways, impacts, implementation, infrastructure, network analysis (planning), route choice, transportation planning


Autonomous driving technology is expected to bring dramatic societal, environmental, and economic benefits due to its potential for improving traffic safety, vehicle fuel economy, road capacity, travel speed, and driver productivity. However, focusing on AV technology alone may potentially slow the penetration of AVs and consequently slow the realization of societal benefits from AVs. In order to safely drive itself in various road environments, an AV needs to be equipped with expensive sensor systems and additional hardware and software. The high cost of AVs can be a significant barrier to their broad adoption. Integrating transportation infrastructure enhancement into the realization of autonomous driving can potentially promote the development and adoption of AVs. This project proposes a modeling framework for the planning and evaluation of an infrastructure-enabled autonomous driving system. The proposed project will accomplish the following two objectives:1) Develop a new network equilibrium model to describe road users' vehicle type and route choice behaviors in a transportation network with automated roads; 2) Investigate the strategic planning of automated roads in a general transportation network.

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