SmartShuttle: Model Based Design and Evaluation of Automated On-Demand Shuttles for Solving the First-Mile and Last-Mile Problem in a Smart City

A major component of mobility in a smart city is the use of fully electric driverless vehicles that will be used for solving the first-mile and last-mile problem, for reducing traffic congestion in downtown areas and for improving safety and helping in the overall reduction of mobility related undesired emissions. Currently available Smart Shuttle solutions have serious interoperability problems due to the low volumes of production and due to the fact that they are developed and manufactured by small startup companies in contrast to OEMs with their series production capability and large R&D departments. Current Smart Shuttle sensing and automation architectures are, therefore, also not easily scalable and replicable. Success of Smart Shuttles in Smart Cities requires an interoperable, scalable and replicable approach which is what this project addresses through model based design techniques. The model based design approach uses a unified software, hardware, control and decision making architecture for low speed smart shuttles that is scalable and replicable. Robust parameter space based design will be used for easily scalable low level control systems. Model based design will use model-in-the-loop and hardware-in-the-loop simulations before road testing. The proposed method will be demonstrated using a proof-of-concept deployment in an outdoor shopping area in Columbus. Year 1 of the project will involve the preparation of the unified scalable and replicable architecture and the hardware-in-the-loop simulator for automated driving. Extensive model-in-the-loop and hardware-in-the-loop simulations will be used for testing the automated driving system in the lab setting. Testing will include communication with other vehicles and instrumented traffic lights using two DSRC modems that will be added to the hardware-in-the-loop simulator. Year 2 of the project will involve applying the results from the first year to the target deployment vehicle (the Dash EV) and demonstrating scalability and replicability by application to a Ford Fusion Hybrid automated vehicle. The research team will use a Ford Fusion Hybrid automated vehicle to collect perception sensor data from the Easton Town Center outdoor shopping area and identify a short segment for a possible proof-of-concept demo. The team intends to provide a proof-of-concept deployment demonstration with the target vehicle in the Easton Town Center outdoor shopping area at the end of year 2.

Language

  • English

Project

  • Status: Active
  • Funding: $126911
  • Contract Numbers:

    69A3551747111

  • Sponsor Organizations:

    Carnegie Mellon University

    Mobility 21 National UTDOT for Mobility of Goods and People
    ,    

    Office of the Assistant Secretary for Research and Technology

    University Transportation Program
    ,    
  • Managing Organizations:

    Carnegie Mellon Univeristy

    Mobility 21 National UTDOT for Mobility of Goods and People
    ,    
  • Project Managers:

    Schweyer, Lisa Kay

  • Performing Organizations:

    Ohio State University

    Columbus, OH  United States 
  • Principal Investigators:

    Ozguner, Umit

  • Start Date: 20161130
  • Expected Completion Date: 20180930
  • Actual Completion Date: 0
  • USDOT Program: University Transportation Centers

Subject/Index Terms

Filing Info

  • Accession Number: 01677494
  • Record Type: Research project
  • Source Agency: Technologies for Safe and Efficient Transportation University Transportation Center
  • Contract Numbers: 69A3551747111
  • Files: UTC, RiP
  • Created Date: Aug 7 2018 12:04PM