Research Team: PI: Itzhak Rubin Team:
About this project:
The research team proposes to translate its models and techniques to the design of autonomous transportation systems when aided by interconnected roadside unit (RSU) stations that form a backbone network infrastructure. The research team’s methods will be used to determine the best configuration of joint traffic management and data networking mechanisms, described by the locations and interconnection features of the RSU stations and the backbone network infrastructure that they form.
What problem does this research aim to address?
Researchers have been developing innovative methods for integrated traffic management and communications networking systems for autonomous transportation systems. These models provide for optimal on-ramp merging and adaptive formation of vehicular flows across highway lanes, with the goal of achieving high vehicular flow rates while reducing queueing delays. To effectively control vehicular flows and formations across the highway, researchers have developed new data communications protocols and algorithms.
What are the expected impacts and benefits of the research?
This study’s findings will be of interest to policymakers, planners, and technical experts interested in autonomous vehicle operations and safety. The proposed research translation project will result in the development of methods that will provide for the optimal synthesis of the RSU backbone and the RSU-aided infrastructure-to-vehicle and vehicle-to-infrastructure wireless access communications nets. The resulting network system will provide communications connectivity that assures rapid dissemination of safety and other key data flows.