SmartPark was born from the frustration of finding parking in crowded cities. The idea is to help drivers find available parking spaces in real time by using AI and machine learning to analyze parking trends and predict free spots. As an urban driver, I often find myself circling around looking for an open space, wasting time and gas. I realized that with the right technology, this problem could be solved.
In building SmartPark, I faced challenges with integrating real-time data and designing an intuitive user interface. We used computer vision to scan parking lots and determine availability, while machine learning algorithms predicted the likelihood of spaces being free at any given time.
The development process was rewarding because it pushed me to learn about real-time data processing and AI integration in a mobile app. The hardest part was figuring out how to efficiently process the parking data without overwhelming the system, especially when lots were changing frequently.
What I learned the most from this project is the potential of combining machine learning with IoT to solve real-world problems, and how crucial user-centered design is for creating a seamless experience.
Log in or sign up for Devpost to join the conversation.