Bajracharya, Vijay (Rochester Institute of Technology), Ahmad, Junaid (Rochester Institute of Technology), Ahmad, Fawad (Rochester Institute of Technology)

ParkAssist: Towards Automated, Real-Time Parking Analytics

Scheduled for presentation during the Regular Session "S40a-Cooperative and Connected Autonomous Systems" (FR-LM-T40), Friday, November 21, 2025, 11:10−11:30, Cooleangata 4

2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC), November 18-21, 2025, Gold Coast, Australia

This information is tentative and subject to change. Compiled on October 18, 2025

Abstract

One of the major reasons the transportation system is inefficient is due to the lack of real-time, fine-grained traffic analytics. An average motorist in the United States spends a significant amount of time looking for vacant parking spots during their daily commute. Existing systems for parking spot detection are either not scalable to large metropolitan areas or require a human-in-the-loop. This paper focuses on automating the detection of fine-grained parking analytics. Leveraging on-board stereo cameras, ParkAssist utilizes a crowd-sourced strategy to take time-windowed snapshots of the road containing 3D map points of the environment. These snapshots are used to extract changes in the environment which are uploaded to a central cloud server responsible for inferring vacant parking spots. Evaluations show that ParkAssist correctly predicts a vacant or an occupied parking spot 89% of the time in various parking scenarios.