Research Projects

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Research Projects

STATUS: In Progress YEAR: 2019 TOPIC AREA: Connected and autonomous systems Vehicles and infrastructure CENTER: PSR

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

Project Summary

Project number: PSR-19-30
Funding source: Caltrans
Contract number: 65A0674, TO-027
Funding amount: $65,000
Start and end dates: January 1, 2020 to December 31, 2020

Project description

Current parking infrastructure suffers from congestion as the number of vehicles circulating in urban areas is growing and expansion is not a cost-effective solution. In parallel, developments in autonomous vehicle technology mean that driverless vehicles are predicted to be in circulation by the 2020s and makeup 40% of vehicle travel by the 2040s. Expected benefits of autonomous vehicle travel include reduced congestion through vehicle sharing and reduced 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 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 include 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, 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 using Ettus B210 Software Defined Radios (SDRs) to communicate using the V2X standard: IEEE 802.11p. 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.


Mohammad Al Faruque
Associate Professor of Electrical Engineering & Computer Science
The Henry Samueli School of Engineering
3223 Engineering HallIrvine, CA 92697-2625
United States
[email protected]