Intelligent Parking Guidance System Using Computer Vision, IoT, and Edge Computing
With the increasing population, the surge in vehicle owners has made parking challenges a pressing issue. Navigating a crowded parking lot in urban centers, commercial areas, and public places is often time-consuming, leading to wasted fuel and increased greenhouse gas emissions. Another concern in parking lots is the occurrence of traffic accidents, frequently caused by distracted drivers searching for parking spaces or navigating through congested lots. Many parking lots worldwide have implemented smart parking systems that provide information about the number of available parking spaces at each instant but do not guide drivers about where these parking spaces are in the parking lot. This paper introduces an intelligent parking guidance system that monitors the parking lot using an infrastructure monocular camera to identify the available parking spaces using computer vision and edge computing to process the data and avoid latency. The processed video from the camera is streamed on a website interface for remote monitoring or viewing by drivers accessing the parking lot. The website also provides the driver with navigation route guidance to the nearest available parking spot among all the vacant parking spaces from the drivers location at each instance. An experimental evaluation was performed to assess the robustness of the developed prototype system. Additionally, multi-camera-based vacant parking space detection was performed to upgrade the current system. This can be seamlessly integrated with the developed system to mitigate the problems of occlusions and false positives/negatives. This innovative, intelligent parking guidance solution significantly simplifies the parking process for human drivers, making it safer by reducing the risk of accidents and making it more convenient and environmentally friendly. The system’s convenience will make the audience feel more comfortable, knowing that finding a parking spot will be a hassle-free experience and that they are less likely to be involved in a parking lot accident.
Authors
- P. Jai Rajesh
- Dheeraj Kallakuri
- Junfeng Zhao (ORCID: https://orcid.org/0000-0002-8728-2885)
- Jingxiong Meng
- Sudeeksha Chagarlamudi
- Sai Manoj Vissavajhula
Institutions
- Arizona State University (US)
Publication Details
- Journal
- IEEE Open Journal of Intelligent Transportation Systems
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1109/ojits.2025.3605318
- Citations
- 2
- Primary Topic
- Smart Parking Systems Research
- Type
- article
- Field-Weighted Citation Impact
- 2.01