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Project

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


Principal Investigator
Levent Guvenc
Status
Completed
Start Date
June 19, 2017
End Date
Sept. 30, 2018
Project Type
Research Applied
Grant Program
FAST Act - Mobility National (2016 - 2022)
Grant Cycle
Mobility21 - The Ohio State University
Visibility
Public

Abstract

Project Lead: Prof. Levent Guvenc

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 our Ford Fusion Hybrid automated vehicle. We will use our 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. We intend 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.    
Description
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. 
Timeline
CMU Subcontract with OSU Signed: 6/27/2017

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 our Ford Fusion Hybrid automated vehicle. We will use our 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. We intend 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.
Strategic Description / RD&T

    
Deployment Plan

    
Expected Outcomes/Impacts

    
Expected Outputs

    
TRID


    

Individuals Involved

Email Name Affiliation Role Position
aksunguvenc.1@osu.edu Aksun-Guvenc, Bilin The Ohio State University Co-PI Faculty - Research/Systems
guvenc.1@osu.edu Guvenc, Levent The Ohio State University PI Faculty - Tenured
hillstrom.7@osu.edu Hillstrom, Stacy The Ohio State University Other Other
ozguner.1@osu.edu Ozguner, Umit The Ohio State University Co-PI Faculty - Tenured
redmill.1@osu.edu Redmill, Keith The Ohio State University Co-PI Other

Budget

Amount of UTC Funds Awarded
$126572.00
Total Project Budget (from all funding sources)
$249415.00

Documents

Type Name Uploaded
Presentation 01_-_Guvenc_Levent.pdf March 29, 2018, 3:12 p.m.
Progress Report 77_Progress_Report_2018-03-30 March 29, 2018, 4:17 p.m.
Presentation CDC18_postReview_v2.pdf Sept. 30, 2018, 7:50 p.m.
Presentation 2018-01-0608.pdf Sept. 30, 2018, 7:55 p.m.
Publication PaperFinal_pdfA.pdf Sept. 30, 2018, 8:18 p.m.
Presentation 2018-01-1086.pdf Sept. 30, 2018, 8:18 p.m.
Presentation 2018-01-1182_1.pdf Sept. 30, 2018, 8:18 p.m.
Progress Report 77_Progress_Report_2018-09-30 Sept. 30, 2018, 8:24 p.m.
Final Report 77-SmartShuttleFinalReport_N8JqGdH_37RSgR1.pdf March 6, 2019, 12:31 p.m.
Publication Smooth: improved short-distance mobility for a smarter city. Dec. 2, 2020, 10:34 a.m.
Publication Distributed MPC for cooperative highway driving and energy-economy validation via microscopic simulations Dec. 27, 2020, 10:55 p.m.
Publication Smooth: improved short-distance mobility for a smarter city Dec. 27, 2020, 10:56 p.m.
Publication Non-iterative distributed model predictive control for flexible vehicle platooning of connected vehicles Dec. 27, 2020, 11 p.m.
Publication MPC Based Automated Steering of a Low Speed Shuttle for Socially Acceptable Accident Avoidance April 19, 2021, 7:34 a.m.
Publication SmartShuttle: A unified, scalable and replicable approach to connected and automated driving in a smart city April 19, 2021, 7:35 a.m.
Publication A unified architecture for scalable and replicable autonomous shuttles in a smart city April 19, 2021, 7:36 a.m.
Publication Real time implementation of socially acceptable collision avoidance of a low speed autonomous shuttle using the elastic band method April 19, 2021, 7:37 a.m.
Publication A unified, scalable and replicable approach to development, implementation and HIL evaluation of autonomous shuttles for use in a smart city April 19, 2021, 7:38 a.m.
Publication Collision Avoidance of Low Speed Autonomous Shuttles with Pedestrians April 19, 2021, 7:39 a.m.
Publication Localization and Perception for Control and Decision-Making of a Low-Speed Autonomous Shuttle in a Campus Pilot Deployment April 19, 2021, 7:40 a.m.
Publication SmartShuttle: Model Based Design and Evaluation of Automated On-Demand Shuttles for Solving the First-Mile and Last-Mile Problem in a Smart City April 19, 2021, 7:41 a.m.
Publication Development and Evaluation of Path and Speed Profile Planning and Tracking Control for an Autonomous Shuttle Using a Realistic, Virtual Simulation Environment April 19, 2021, 7:44 a.m.
Publication Autonomous Road Vehicle Path Planning and Tracking Control May 2, 2022, 9:37 a.m.
Publication Autonomous Road Vehicle Emergency Obstacle Avoidance Maneuver Framework at Highway Speeds May 2, 2022, 9:37 a.m.
Publication Simulating the effect of autonomous vehicles on roadway mobility in a microscopic traffic simulator May 2, 2022, 9:38 a.m.
Publication Connected UAV and CAV Coordination for Improved Road Network Safety and Mobility May 2, 2022, 9:40 a.m.
Publication Development and evaluation of path and speed profile planning and tracking control for an autonomous shuttle using a realistic, virtual simulation environment May 2, 2022, 9:40 a.m.
Publication SmartShuttle: Model Based Design and Evaluation of Automated On-Demand Shuttles for Solving the First-Mile and Last-Mile Problem in a Smart City May 2, 2022, 9:41 a.m.
Publication Cooperative ecological cruising using hierarchical control strategy with optimal sustainable performance for connected automated vehicles on varying road conditions May 2, 2022, 9:42 a.m.

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