Large network multi-level control for CAV and Smart Infrastructure: AI-based fog-cloud collaboration

The vast expanse of prospective CAV traffic networks is expected to exponentially increase the information availability and complexity of inter-agent interactions. In such an environment, a single system is inadequate to make decisions for all the agents individually, and therefore, multilevel system decomposition is needed. Further, due to the large amount of generated information that is redundant and therefore irrelevant to the specific decisions, the overall effectiveness and efficiency of the decision processors may be compromised. Thus, it is essential to design the subsystems that are capable of automatically identifying relevant data to make operational decisions based on the tasks and goals. To address this issue, the proposed research proposes a framework to decompose large transportation networks using a Fog-Cloud collaboration up to larger transportation networks with minimal compromises being made in real-time decision making. In effect, the multi-scale architecture of Fog-Cloud collaboration causes separation of the tasks based on their respective scales and decision levels, decomposes the large network, and decentralizes the computation. This research will address regional decision tasks (which require low latency) and network decision tasks (which require high computational capacity). By assigning regional decision tasks to the fog layer and network decision tasks to the cloud layer, we anticipate that systemic efficiency can be improved.

• Record URL:
  ccat.umtri.umich.edu

Language

• English

Project

• Status: Completed
• Funding: $300,000($150KCCAT, $150K Cost Share)
• Contract Numbers:

  69A3551747105

• 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 and Automated Transportation

  University of Michigan Transportation Research Institute
  Ann Arbor, MI  United States  48109
• Managing Organizations:

  Center for Connected and Automated Transportation

  University of Michigan Transportation Research Institute
  Ann Arbor, MI  United States  48109
• Project Managers:

  Tucker-Thomas, Dawn

  Bezzina, Debra

• Performing Organizations:

  Purdue University, Lyles School of Civil Engineering

  550 Stadium Mall Drive
  West Lafayette, IN  United States  47907
• Principal Investigators:

  Chen, Sikai

  Labi, Samuel

  Sinha, Kumares

• Start Date: 20210101
• Expected Completion Date: 20220930
• Actual Completion Date: 20230307
• USDOT Program: University Transportation Centers Program
• Subprogram: Research

Subject/Index Terms

• TRT Terms: Automated highway systems; Connected vehicles; Networks; Traffic; Transportation
• Subject Areas: Vehicles and Equipment;

Filing Info

• Accession Number: 01768296
• Record Type: Research project
• Source Agency: Center for Connected and Automated Transportation
• Contract Numbers: 69A3551747105
• Files: UTC, RIP
• Created Date: Mar 26 2021 2:58PM