SlotSpot
A future-facing parking assistant combining AI with real-time data.
Quick view of some of SlotSpot's unique AI-powered capabilities.
The Problem
In crowded cities, parking is unpredictable, confusing, and time-consuming. Drivers often waste fuel, risk fines, or circle for 10+ minutes trying to find a legal space. Current apps fail to provide real-time, personalized guidance.
The Solution
SlotSpot gives drivers clarity. With predictive suggestions, legal signage scanning, and a simple, responsive interface, the app offers real-time assistance using data from traffic systems, connected vehicles, and user input. The SlotSpot concept imagines future partnerships with Apple Maps, Google Maps, Tesla, municipal traffic cameras/data, as well as popular parking partnerships like ParkMobile through API integrations, offering predictive parking assistance based on real-time data.
My Role
I worked solo as a product designer on this project, making good use of software such as FigJam, Figma, and Illustrator.
Duration
Dec '24 - May '25
(6 mos. total)
Parking Statistics in America
$345
Per driver annual cost searching for parking space
63%
Of drivers have avoided going somewhere due to parking
17hrs
Per year, per driver searching for parking
9/10
Drivers get stressed out searching for parking
Driving My Design
Designing a smarter parking assistant took a lot of research, and I explored both the problem space and competitive landscape, focusing on what makes parking in urban areas so stressful. I conducted user interviews, analyzed popular parking apps, and researched the logistical and emotional challenges of finding legal, available spaces. I translated these insights into user personas, journey maps, and key design decisionsāall aimed at simplifying the parking experience. You can view my full research and supporting visuals below.
The Opportunity
Based on the research, I saw a clear opportunity: simplify legal parking decisions with real-time, safe cues, layered context, and trustworthy design to locate the best parking with easy payment methods.
What Had toĀ Work
My research indicated that SlotSpot should provide drivers with a solution to the stress associated with the search for parking. To solve this problem, I felt that the app would need to utilize state-of-the-art technology to provide the most accurate real-time data in a safe and efficient format. For this to be achieved, I determined the following features would be necessary.
Smart SignĀ Scan
Predictive Spot
Availability
IntegratedĀ
Payments
Map Layer Clarity
Trusted Legal
Information
Parking Zone
Notifications
How SlotSpot Works
SlotSpot draws from multiple sourcesāTesla vehicle communication, parking APIs, traffic cameras, and user feedbackāto surface the most legal and likely spots nearby. Data flows through predictive AI to generate real-time suggestions.
Designing the Details
I refined the interface with thoughtful touches ā clean overlays, subtle motion, and components built to feel calm, clear, and quietly intelligent.
š Find Parking Now
- Clean, tap-and-go screens with an AIĀ assistant and clear CTA.
- Real-time analysis provides multiple parking sites to choose from based on preferences and traffic.
š Easy Voice Command
Drivers can access the AIĀ Assistant on the go to make changes or ask questions.
š Quickly Check Nearby Spaces
Predictive logic makes the best nearby spots surface, based on proximity, conveniences, and preferences.
Final Product
The current SlotSpot prototype demonstrates two complete user flows:
Onboarding and Tutorial ā Demonstration of how the app works and basic setup.
āFind Parking Now ā the core interaction that helps users quickly locate nearby, legal, available spots using AI-backed suggestions.
The experience is designed to be calm, intuitive, and responsiveāoptimized for mobile use in real-world environments. The UI overlays seamlessly on a map system, with soft motion, minimal friction, and actionable clarity.
š You can interact with the prototype below or open it full screen for the best experience!
Results
Grounded in user interviews and real-world behavior, Carcloud was designed to reduce the stress, uncertainty, and wasted time drivers face when parking in urban areas. Through research synthesis and competitive analysis, I identified clear UX opportunities: real-time feedback, legal clarity, and personalized filters.
The final prototype introduces an AI-powered assistant that uses multimodal dataāfrom Tesla vehicle movement and traffic cameras to parking APIs and user inputāto provide timely, legal, and personalized spot suggestions. The interface is clean, intuitive, and designed to calm rather than overwhelm.
While Carcloud remains in the prototyping phase, early feedback validated its value: users reported a dramatic increase in confidence and clarity during task simulations. The system currently supports two polished user flows, over 30 high-fidelity screens, and a flexible component library ready for scalable development.
72%
Estimated Decision Time Reduction
4/5
Of Users Completed Parking Tasks with 0 Problems
65%
Reported Increase in Parking Confidence
5/5
Found UI Intuitive and Streamlined
Key Learnings
Carcloud draws from multiple sourcesāTesla vehicle movement, parking APIs, traffic cameras, and user feedbackāto surface the most legal and likely spots nearby. Data flows through predictive AI to generate real-time suggestions.
Like what you see?
Please check out more of my case studies below, or reach out to collaborateāI'd love to hear from you!
