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A Cloud-based Quantum Artificial Intelligence-supported Truck Platooning Strategy for Safety and Operational Performance

项目名称：	A Cloud-based Quantum Artificial Intelligence-supported Truck Platooning Strategy for Safety and Operational Performance
摘要：	Description: This research aims to develop a new Q-AI driven technology that will address the existing knowledge gap identified by existing literature of fully quantifying the impact of freight operation by integrating freight pathways, powertrain technologies, the total cost of ownership, infrastructure, and platooning technology. To achieve this goal, the research objectives are to, (1) develop cloud-assisted truck platooning models considering different truck powertrain technologies (Obj.-1), (2) develop cloud computing strategies to satisfy real-time computing requirements of structured and unstructured data from heterogeneous data sources (Obj.-2); 3) study the impacts of cloud-supported truck platoons, in terms of safety, operational efficiency and energy consumption, along freight corridors in a simulation tool (Obj.-3); (4) validate the improved predictive analytics including Q-AI strategies in actual fleet trials for safety and operational impacts(Obj.-4), and (5) evaluate the cost-effectiveness and other impacts of proposed technology with the baseline technology with roadside units-supported cloud servers. Intellectual Merit: To maximize the expected safety and operational benefits of truck platooning, this proposal intends to develop a cloud infrastructure supported platooning algorithm to assist truck platooning to minimize delay, reduce energy consumption and improve safety and demonstrate the application in the real world. Cloud infrastructure will provide a seamless, on-demand data storing, application hosting, and execution platform for truck platooning application. Broader Impacts: Reliance on in-vehicle computational devices for truck platooning, as considered in the existing studies, will increase the computational burden for each vehicle. To reduce the overreliance on the in-vehicle computing nodes and enable a predictive analytics-based truck platooning for a corridor to ensure safety and operational improvement, a cloud-based truck platooning framework will be developed in this research. Both connected trucks and automated trucks will be considered in this study. This research focuses on predictive analytics using quantum artificial intelligence (Q-AI). An earlier study discussed using Q-AI to enhance learning efficiency, learning capacity, and run-time improvements. The focus of this study is to develop predictive Q-AI algorithms for cloud-based, safe, and efficient truck platooning using high volume and heterogeneous data from multiple diverse sources with the capability of scaling up. This study will also evaluate the efficacy of platooning for safety and operational performance in the real world in a test track in Greenville, South Carolina.
状态：	Active
资金：	296262
资助组织：	Office of the Assistant Secretary for Research and Technology；Center for Connected Multimodal Mobility；Clemson University；Benedict College；The Citadel；South Carolina State University；University of South Carolina, Columbia
管理组织：	Clemson University
项目负责人：	Chowdhury, Mashrur
执行机构：	Clemson University<==>Benedict College<==>The Citadel<==>South Carolina State University<==>University of South Carolina, Columbia
主要研究人员：	Comert, Gurcan；Michalaka, Dimitra；Mwakalonge, Judith；Huynh, Nathan；Davis, William J；Brown, Kweku；Chowdhury, Mashrur
开始时间：	20220930
预计完成日期：	20221003
主题领域：	Data and Information Technology；Freight Transportation；Highways；Motor Carriers；Planning and Forecasting；Vehicles and Equipment