## Inspiration

Have you ever turned on your car to a dashboard full of alarming, unspecific, flashing symbols ⚠️❗️❌? All of a sudden, you’re not sure if you just forgot to fill that windshield washer fluid💧 or if there’s a bigger problem that’s going to leave you stranded in the middle of nowhere. Modern dashboard symbols are nearly impossible to decipher and can put you in a panic🤯. DashBuddy is an app that can identify warning symbols and their potential causes, potentially saving you from a costly trip to the auto repair shop🚗⚙️!

## What it does

The DashBuddy app lets you take a picture 📸 of your dashboard and identifies the warning symbol that is displayed. It then provides you with information on what your vehicle's issue could be and proposes a solution💡.

## How we built it

We created our custom dataset by annotating 200+ images of dashboard warning symbols using Roboflow💻. We generated a training set of 400+ images using data augmentation for our custom object detection training model with a precision of 95%📈. The detected result is added to the prompt for the Cohere API, which uses language AI and is built on the GPT-3 model🤖 to determine what the issue is and generate a solution for the symbol🛠️. The Frontend of the mobile application was implemented using React-Native after designing and prototyping in Figma📱.

## Challenges we ran into

A significant challenge we faced was the scarcity of datasets containing car dashboard images to train our image-detection neural network. We decided to instead create our own dataset using Roboflow, by taking and annotating 200+ images of dashboard symbols. Another one of our challenges was integrating the camera📸 functionality in React-Native with the object detection API.

## What's next for DashBuddy

In the future, we hope to implement a vehicle damage assessment feature that suggests the appropriate action to take for external damage🚗💥.
## Inspiration

As with many drivers, when a major incident occurs while driving, we are often left afraid, anxious, and overwhelmed. Just as many of our peers, we had little experience behind the wheel and barely understood how insurance claims worked and what steps we should be taking if an accident occurs. We decided to innovate the process of filing insurance claims for people of all ages and diverse backgrounds to allow for a quicker, accessible, and user-friendly experience through SWIFT DETECT.

## What it does

SWIFT DETECT is an app that utilizes machine learning to extract information from user fed pictures and environmental context to auto-fill an insurance claims form. The automated process gives the user an informed and step-by-step guide on steps to take collision. The machine learning software can also make informed decisions on whether to contact emergency services, towing services, or whether the user will need a temporary vehicle based off of the picture evidence the user submits. This automated process allows the user to gain control over the situation and get back on track with their day-to-day activities faster than the traditional methods practiced.

## How we built it

SWIFT DETECT was made using Node.JS and CARSXE ML API.

## Challenges we ran into

Initially, we had tried creating our own ML model however we faced issues gathering datasets to train our model with. We thus utilized the pre-existing CARSXE ML API. However, this API proved to be very challenging to use.

## Accomplishments that we're proud of

We are proud to have utilized our knowledge of tech to engineer a meaningful product that impacts our society in a positive way. We are proud to have engineered a product that caters to a diverse group of end-users and ultimately puts the user first.

## What we learned

Through the process of planning and executing our hack, we have learned a lot about the insurance industry and ML models.

## What's next for SWIFT DETECT

SWIFT DETECT hopes to take a preventative approach when it comes to vehicle collisions. We will do so by becoming the primary source of information when it comes to your vehicle's health and longevity.
We aim to reduce the amount of collisions by analyzing a car’s mechanical parts and alerting the user when it is time for replacement or repair. Through the use of smart car features, we want to deliver rapid and accurate results of the current status of your vehicle.
## Inspiration

We get the inspiration from the idea provided by Stanley Black&Decker, which is to show users how would the product like in real place and in real size using AR technic. We choose to solve this problem because we also encounter same problem in our daily life. When we are browsing website for buying furnitures or other space-taking product, the first wonders that we come up with is always these two: How much room would it take and would it suit the overall arrangement.

## What it does

It provides customer with 3D models of products which they might be interested in and enable the customers to place, arrange (move and rotate) and interact with these models in their exact size in reality space to help they make decision on whether to buy it or not.

## How we built it

We use iOS AR kit.

## Challenges we ran into

Plane detection; How to open and close the drawer; how to build 3D model by ourselves from nothing

## Accomplishments that we're proud of

We are able to open and close drawer

## What we learned

How to make AR animation

## What's next for Y.Cabinet

We want to enable the change of size and color of a series/set of products directly in AR view, without the need to go back to choose. We also want to make the products look more realistic by finding a way to add light and shadow to it.