The distracted drives ppt, which is the summary of the project we made- the methods, results, conclusion, bigger picture.

1. Distracted Drivers
Weekday Summer 1
Andrew, Angel, David, Karthik, Sanaa and Minsung
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2. The Bigger Picture
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3. ● 1.6 million crashes happen every year because of
someone using a phone while driving.Around 390,000
injuries happen yearly from accidents caused by
distracted driving.
● Computer vision is used to improve road safety by
detecting distracted drivers, it is being applied in several
areas for security and traffic monitoring.It improves safety
and accuracy and plummets the risk of accidents.
Different distractions
while driving
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4. ● Can you think of any other ways in
which we can use AI to prevent
distracted driving?
● Could you implement these solutions to
prevent distracted driving in different
parts of the world?
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5. Distracted Driving
and Our Mission
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6. First let’s briefly talk about AI in general. Artificial
intelligence known as AI is a wide range of a
computer science with machine buildings capable
of performing the tasks that are typically require
human intelligence. For our topic, we focused on
how to detect whether the driver is driving safely
or not. In United States, there are so many cases
of drowsy driving and unrealized accidents, such
caused due to smartphones and etc. This was the
time where the AI came in. By training the
computers and using the Ai technology, we can
detect further number of distracted drivers and
therefore guarantee more safety along with less
percentages car accidents. Furthermore,
development of AI not just protects people from
many accidents, but it also improved every
human’s quality of life.
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7. What We Tried:
The Good, the
Bad, and the Meh
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8. Basic Models:
● Our task was to build a model
capable of differentiating between
different driver behaviors based on
images (computer vision)
● Nearest Neighbor Model (KNN
Model)
○ Testing Accuracy: about 50%
○ Overpredicted that driver was using
radio
● Logistic Regression Model
○ Accuracy: about 35%
○ Overpredicted that driver was using
mirrors
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KNN
Predictions
Logistic Regression
Predictions

9. Neural Networks and CNNs
● Next, we used Neural Networks to see if they
would be any better
● Our basic neural network (top) trained 10
times on the training set
● Unfortunately, it only reached a 40% testing
accuracy
● The Convolutional Neural Network (bottom)
didn’t do much better
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10. Transfer Learning Neural Networks
● The next attempt was to take pre-
established machine learning algorithms
such as VGG16, VGG19, ResNet50, and
DenseNet121
● These algorithms have already been
extensively trained, and are very adept at
computer vision tasks.
● When applied to our task of identifying driver
behaviors, these networks performed much
better than our initial attempts.
● In particular, VGG 19 was highly effective
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VGG 19, 15 training epochs
DenseNet 121, 10 training Epochs

11. Transfer Learning with Some Customization
● The final thing we tried was combining
specific layers of the VGG neural network
with some new layers of our own creation
● While we were unable to improve the model
any further, we still maintained about an
80% validation accuracy, which is pretty
good.
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VGG 19 with customized dense layers,
15 epochs of training
DenseNet121 with customized layers,
50 epochs of training

12. Conclusion
We did a lot of trial and error to make our
data as accurate as possible. We want to
focus on the bigger picture. There are
many different ways to prevent
distracted driving using AI.
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13. Acknowledgements
Special thanks to our instructor, Yoichi, for teaching us this week!
We would also like to thank the entire Inspirit AI team for helping us through this
project. Thank you all for teaching us, for all the wonderful talks and it was a great
learning experience.
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