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

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.

Language

• English

Project

• Status: Active
• Funding: $296262
• Contract Numbers:

  69A3551747117

• Sponsor Organizations:

  Office of the Assistant Secretary for Research and Technology

  University Transportation Centers Program
  Department of Transportation
  Washington, DC  United States  20590

  Center for Connected Multimodal Mobility

  Clemson University
  Clemson, SC  United States  29634

  Clemson University

  110 Lowry Hall
  Box 340911
  Clemson, SC  United States  29634-0911

  Benedict College

  1600 Harden Street
  Columbia, South Carolina  United States  29204

  The Citadel

  Department of Civil and Environmental Engineering
  171 Moultrie Street
  Charleston, SC  United States  29409

  South Carolina State University

  300 College Street NE
  Orangeburg, South Carolina  United States  29117

  University of South Carolina, Columbia

  502 Byrnes Building
  Columbia, SC  United States  29208
• Managing Organizations:

  Clemson University

  110 Lowry Hall
  Box 340911
  Clemson, SC  United States  29634-0911
• Project Managers:

  Chowdhury, Mashrur

• Performing Organizations:

  Clemson University

  110 Lowry Hall
  Box 340911
  Clemson, SC  United States  29634-0911

  Benedict College

  1600 Harden Street
  Columbia, South Carolina  United States  29204

  The Citadel

  Department of Civil and Environmental Engineering
  171 Moultrie Street
  Charleston, SC  United States  29409

  South Carolina State University

  300 College Street NE
  Orangeburg, South Carolina  United States  29117

  University of South Carolina, Columbia

  502 Byrnes Building
  Columbia, SC  United States  29208
• Principal Investigators:

  Comert, Gurcan

  Michalaka, Dimitra

  Mwakalonge, Judith

  Huynh, Nathan

  Davis, William

  Brown, Kweku

  Chowdhury, Mashrur

• Start Date: 20220930
• Expected Completion Date: 20221003
• Actual Completion Date: 0
• USDOT Program: University Transportation Centers

Subject/Index Terms

• TRT Terms: Artificial intelligence; Autonomous vehicles; Cloud computing; Connected vehicles; Traffic platooning; Truck traffic
• Subject Areas: Data and Information Technology; Freight Transportation; Highways; Motor Carriers; Planning and Forecasting; Vehicles and Equipment;

Filing Info

• Accession Number: 01838177
• Record Type: Research project
• Source Agency: Center for Connected Multimodal Mobility
• Contract Numbers: 69A3551747117
• Files: UTC, RIP
• Created Date: Mar 6 2022 3:24PM