Inspiration

We took a look at current problems with driving and realized that we could improve the driver's safety.

What it does

There are two versions of the app: one is for the inexperienced drivers and the other is for experienced drivers. for novices, the app lets the him/her know when they are over the speed limit, what the passing by signs mean, and assist the normal features of a GPS. The other version is for the experienced drivers, in which the app will also advise the speed based on weather and road conditions and explain the driving rules in other countries.

How we built it

We built our app in React Native, and the machine learning models for street detection was done in Python Anaconda, OpenCV, and Tesseract (text recognition). The front and back-end were connected with a live Firebase database.

Challenges we ran into

Integrating the Firebase between the Python machine learning models and the React Native app were challenging, but we did it!

Accomplishments that we're proud of

We successfully integrated a machine learning model for sign detection, similar to ML models of autonomous cars! Additionally, we successfully combined a model to detect and assist drivers of all ages.

What we learned

We learned how to connect complex Python machine learning models with a React Native Android application, which is something we had done previously seperately, but now we could build a full integrated and functional machine learning mobile app.

What's next for DriveAssist

What's next is augmented reality, in which real time data is being feed in a camera, and the app can give instant advice and information about the condition of the vehicle at that particular time. Specifically, the app would include AR features to help both more advanced drivers, the elderly, and new drivers.

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