CARMA

Inspiration

Modern navigation has a tendency to only consider the shortest route from point A to point B. Because of this, we decided to become more sophisticated with navigation and add more influencing factors that may affect the severity of accidents.

What it does

Our navigation allows the user to plan out much safer navigation compared to popular navigation apps such as Google Maps and Waze. Machine Learning was also implemented to predict accident severity and take into consideration of safety while driving.

How we built it

We built the frontend UI using HTML, CSS, and JavaScript as well as Flask for the backend. The machine learning model was built using Tensorflow and Keras

Challenges we ran into

One of the biggest challenges we ran into is being able to merge the frontend, backend, and machine learning models. We spent most of the time trying to amend everything to get a properly working full-stack application.

Accomplishments that we're proud of

We are very proud of being able to implement machine learning into our full-stack application, and we also did a great job working as a team of software engineers to ensure success.

What we learned

We learned to cooperate as a team, and we also learned new frameworks and API's to gather data and also work on the functionality of the website.

What's next for CARMA

The next step for CARMA is to turn it into a mobile application and being able to scale it better using AWS cloud.

Built With

• api
• css
• html
• javascript
• keras
• python
• tensorflow