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Working of the computer imaging algorithm.
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Working on the hardwares
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Arduino code
Inspiration
The rapid increase in the production of cars has led to a significant rise in traffic in many parts of the world. The traffic signals currently in place have no means of optimizing the traffic to reduce wait time. unnecessary wait times lead to excessive fuel consumption and inefficient distribution of the road space.
What it does
Our solution analyzes the traffic on each lane of the intersection and modifies the wait time for each particular branch depending on the intensity of traffic. The design also accounts for high-priority vehicles like ambulance, fire trucks, etc to ensure that they do not suffer.
How we built it
We used Jupyter notebook as our primary IDE for coding. We also utilized the OpenCV library of python for computer imaging and image analysis. The hardware was built using Arduino, nRF24L01 and L293D.
Challenges we ran into
The biggest software challenge we faced was to efficiently identify roads and traffic using the correct range of HSV values. The thought process of developing our own algorithm was also quite challenging. On the hardware side, the disturbance caused by external noise signals was the biggest challenge. Due to this, it was hard to read the signals that we were transmitting.
Accomplishments that we're proud of
Our first major accomplishment was to be able to implement our idea and design a successful prototype. The successful integration of hardware and software was our largest milestone in the hackathon.
What we learned
We learned the successful integration of hardware and software. We learned to work with nRF24L01 and L293D. We improved our knowledge in computer imaging by working extensively with the OpenCV module
What's next for SmartSignal
It also sends real-time data to cloud servers which can then be paired with software like google maps to help suggest the best and least congested routes for travel.
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