Safe & Sound

Inspiration

The initial inspiration for 'Safe and Sound' came from watching a news broadcast about a fatal car crash. The crash occurred when the driver worked for odd hours and dozed off while driving. Drowsy driving is so dangerous because it mirrors so many symptoms of drunk driving: blurred vision, slowed reaction time, and poor decision making. Statistics show that 60% of all adult drivers have driven while being drowsy, while more than 37% percent have at one time fallen asleep while driving, which is a worrying situation. 100,000 police-reported crashes and over 1,500 deaths are the results of drowsy driving each year. The cost of drowsy-driving crashes at about 13% of the total $836 billion in societal costs of traffic crashes. (NHTSA) Even the most experienced drivers can be susceptible and miss the warning signs that it's no longer safe to be behind the wheel of a motor vehicle. So, an application which warns alerts the user(by ringing an alarm) when the initial signs of fatigue or drowsiness are observed, can save many lives. Although emergency alarms which recognize the initial signs of fatigue are available in some luxury cars, we propose to make it available on a wider scale through this ML model.

What it does

This application tackles the rising number of road accidents due to fatigue or drowsiness while driving. Drowsy driving has costly effects on the safety, health, and security of not only the person driving but also the pedestrians. Whether fatigue is caused by sleep restriction due to a new baby waking every couple of hours, a late or long shift at work, hanging out late with friends, or a long and monotonous drive for the holidays – the negative outcomes can be the same. These include impaired cognition and performance leading to motor vehicle crashes. This application alerts the users(by ringing an alarm) when they are on the verge of dozing off. It reminds the user to take a short break before continuing on his journey so as to combat accidents.

How we built it

I used Google Teachable Machine and Tensorflow to train and develop the ML model. Then, I used Google Cloud Services to deploy it. I built the user-friendly frontend of this application primarily using HTML and CSS. I used JavaScript to use the results of the ML model in this application and trigger the alarm when signs of drowsiness are observed(i.e., when the probability of the driver being drowsy crosses the 70% benchmark).

Challenges we ran into

I tried using YOLO and Pytorch to train the ML model but I ran into errors. So, I decided to use Google Teachable Machine to train the model instead.

What's next for Safe and Sound

Further plans include working on making "Safe and Sound" available on Google Play Store and App Store as well for a greater reach.

Built With

• css3
• github
• google-teachable-machine
• html5
• javascript
• tensorflow