Parking Space Ground Truth Test Automation by Artificial Intelligence Using Convolutional Neural Networks

2025-09-15

AI Summary

To address the low efficiency, high cost, and poor scalability of manual ground-truth validation in urban roadside parking services, this paper proposes an automated testing framework leveraging vehicle-mounted crowdsourced ultrasonic and image data. It introduces convolutional neural networks (CNNs) for the first time to validate ground truth in parking detection, integrating multimodal sensor inputs to enable end-to-end automatic annotation and classification. The method drastically reduces reliance on manual analysis—shortening testing cycles by over 80% and decreasing human effort by up to 99.58%. It also improves parking-state recognition accuracy and accelerates system iteration, thereby enhancing the reliability of parking information services and enabling large-scale deployment. This work establishes a reusable technical paradigm for efficient, scalable validation of intelligent parking systems.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Computer Vision: Learning & Optimization for CV
• Intelligent Robots: State Estimation

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

This research is part of a study of a real-time, cloud-based on-street parking service using crowd-sourced in-vehicle fleet data. The service provides real-time information about available parking spots by classifying crowd-sourced detections observed via ultrasonic sensors. The goal of this research is to optimize the current parking service quality by analyzing the automation of the existing test process for ground truth tests. Therefore, methods from the field of machine learning, especially image pattern recognition, are applied to enrich the database and substitute human engineering work in major areas of the analysis process. After an introduction into the related areas of machine learning, this paper explains the methods and implementations made to achieve a high level of automation, applying convolutional neural networks. Finally, predefined metrics present the performance level achieved, showing a time reduction of human resources up to 99.58 %. The overall improvements are discussed, summarized, and followed by an outlook for future development and potential application of the analysis automation tool.

Problem

• Automating ground truth tests for parking space classification
• Reducing human effort in parking service quality analysis
• Applying convolutional neural networks to validate sensor data

Innovation

• Convolutional neural networks automate ground truth testing
• AI substitutes human analysis in parking data processing
• Machine learning reduces manual effort by 99.58%