TRAFFIC SIGN DETECTOR

Vividly crafted by A.I. Camp students, 2020

Our Story

A traffic sign detector for the development of autonomous vehicles

As technological advancement continues, self-driving vehicles have become increasingly prevalent in the modern society. However, in order to ensure safety on the roads, autonomous vehicles must be able to sense the traffic conditions and react correctly based on the detected data. We created this project in the hopes that it can serve as the eyes of a self-driving car, recognizing the most common traffic signs and allowing the car to make the best judgements. This model enables us to feel secure in our transportation, and has the potential to save many lives. 

Features

Here are some types of signs our model can detect!

Stop Sign

Stop Signs are found at intersections, and they notify a driver to stop and look at traffic to determine when it is their turn to go. 

Red Light

Red lights are the universal signal to stop at a busy intersection. When the red light is on, cars must wait until the light turns green before they can proceed.

Yellow Light

Yellow lights are a warning sign between a green light and a red light. This signal turns on to indicate the onset of a red light, and drivers must slow down to avoid running a red light.

Green Light

Green lights signal the driver to go ahead and cross the intersection. When this signal is on, that means the driver has the right of way. 

Crosswalk Sign

Those signs are present at places where there is a crosswalk but no stop sign. Drivers should be cautious around this sign and be ready to stop if a person is going to use the crosswalk. 

Team Jarvis

Meet the creators & contributors of this project!

Alexander Zhou

Student at Alsion Montessori

Alex enjoys playing the piano and getting things to work. He wrote and configured scripts to retrieve data from Bing, prepare labeled data, and evaluate the model.

Samantha Solomon

Student at Castilleja High School

Sam enjoys math and playing water polo. She worked on labeling the data, training the model, and the backend of the website.

Siddhant Hullur

Student at Saint Francis High School

Sid enjoys software development and playing basketball. He worked on the backend, setting up the model in python, and deploying it to the website.

Cathy Tran

Student at Valley Christian High School

Cathy enjoys learning about the astonishing wonders of A.I. She contributed to our data collection as well as the front end of our website.

Riley Chou

Student at Amador Valley High School

Riley enjoys working with computers and watching all types of sports. He labeled data, trained the AI model using Darknet, and built the front end of our website.

Shane Berger

Team Instructor

Shane studies Artificial Intelligence and Human-Computer Interaction at Stanford University, and he enjoys teaching computer science. He hopes to one day be on the TV show, Survivor!

Michael Ke Zhang

Lead Instructor

Michael ran data teams at Blend Labs and Grab. He enjoys fishing and inspiring the next generation of students.

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