ParkAI
Smart Parking, Simplified! A real-time parking management system that helps users find, reserve, and optimize parking spaces using live updates, AI-powered capacity detection, and seamless payments.
Created on 8th February 2025
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ParkAI
Smart Parking, Simplified! A real-time parking management system that helps users find, reserve, and optimize parking spaces using live updates, AI-powered capacity detection, and seamless payments.
The problem ParkAI solves
Finding parking in busy areas is frustrating, time-consuming, and inefficient. Drivers waste fuel searching for spots, while outdated systems cause congestion and poor space utilization. Many parking lots lack real-time availability updates, digital reservations, and optimized management.
Our Smart Parking Management System solves this by providing real-time parking availability, AI-powered space optimization, and seamless reservations with digital payments. Users can find, book, and navigate to parking spots effortlessly, while parking lot owners maximize space utilization. This reduces traffic, saves time, and creates a smarter, hassle-free parking experience. 🚗📍
Challenges I ran into
Building this project required seamless integration of multiple technologies. The frontend (React.js) communicates with the Flask backend to handle real-time updates, reservations, and ticketing while ensuring smooth UI interactions. Establishing real-time WebSocket communication for live parking availability updates was challenging, especially in managing high-frequency data exchange. Ensuring efficient collaboration between Python (AI processing) and JavaScript (frontend) required well-structured APIs and optimized asynchronous processing to prevent performance bottlenecks.
For vehicle detection, we leveraged Generative AI (GenAI) to classify car types (SUV, Sedan, Mini) based on camera feeds, ensuring accurate space allocation. In contrast, a custom-built AI model and algorithm analyze parking lot layouts to suggest the best spots for each vehicle type, optimizing space utilization. Fine-tuning these models for real-time accuracy while keeping computational overhead low was a key challenge. Integrating these diverse technologies into a single, scalable system required extensive optimization, debugging, and testing. 🚀