Software and Hardware Systems for Autonomous Smart Parking Accommodating both Traditional and Autonomous Vehicles

Current parking infrastructure suffers from congestion as the number of vehicles circulating in urban areas grow while expansion is not a cost-effective solution. In parallel, developments in autonomous vehicle technology mean that these driverless vehicles are predicted to being circulation by the 2020s and make up 40% of vehicle travel by the 2040s. Expected benefits of autonomous vehicle travel include reduced congestion through vehicle sharing and lower walking distance for passengers who can be dropped off chauffeur-style by autonomous vehicles. However, empty vehicle cruising, or the case in which autonomous vehicles cannot efficiently locate parking and circle instead, can potentially increase congestion. Given that this new technology has the potential to exacerbate existing congestion issues, it is thus necessary to develop a solution for parking congestion integrated with autonomous vehicles. Our project addresses this issue by providing a full stack solution including: sensors to monitor occupancy, Fog systems to perform local data pre-processing, radios to communicate with autonomous vehicles, and cloud-based software to predict occupancy. This solution is divided into 3 main subsystems which includes the PTFS (Parking Tracker Fog System), a wireless sensor network, and a Cloud-based server. The PTFS refers to the local IoT module and is equipped with DSRC (Dedicated Short-Range Communications) technology for V2V (Vehicle to Vehicle) and V2I (Vehicle to Infrastructure) communication. It is responsible for generating useful information about occupancy and vehicle classes based on data collected from the wireless sensor network or data directly received from autonomous vehicles over DSRC. For the wireless sensor network above, we will be using a tested system of MEMSIC IRIS sensor motes equipped with PIRs (Passive Infrared Sensor) because they have demonstrated compatibility with multi-hop networks that allow for sensor connections over a greater distance. To facilitate DSRC between the PTFS and autonomous vehicles, we will be customizing software defined radios, Ettus Research boards specifically. These boards were selected due to having successfully been used to implement IEEE 802.11p, the standard for V2X (Vehicle to Everything) communication. Our novel contribution to the ongoing issue of parking congestion will be this DSRC solution for integrating autonomous vehicles into Intelligent Transportation Systems (ITS). A Cloud-based server is the final subsystem and will be responsible for collecting data across multiple PTFSs to be inputted into a machine learning model trained to predict occupancy in parking structures. To validate the algorithms employed, we will simulate parking scenarios and evaluate the performance of the system in terms of response time and accuracy. We will also evaluate our DSRC solution based on criteria including latency and accuracy.

    Language

    • English

    Project

    • Status: Active
    • Funding: $65,000.00
    • Sponsor Organizations:

      California Department of Transportation

      1227 O Street
      Sacramento, CA  United States  95843
    • Managing Organizations:

      METRANS Transportation Center

      University of Southern California
      Los Angeles, CA  United States  90089-0626
    • Project Managers:

      Brinkerhoff, Cort

    • Performing Organizations:

      University of California, Irvine

      Institute of Transportation Studies
      4000 Anteater Instruction and Research Building
      Irvine, CA  United States  92697
    • Principal Investigators:

      Al Faruque, Mohammad

    • Start Date: 20200101
    • Expected Completion Date: 20201231
    • Actual Completion Date: 0
    • USDOT Program: University Transportation Centers

    Subject/Index Terms

    Filing Info

    • Accession Number: 01732420
    • Record Type: Research project
    • Source Agency: METRANS Transportation Center
    • Files: RiP
    • Created Date: Feb 28 2020 1:34PM