HELMET DETECTOR
Before riding can begin, it is essential that the driver is wearing a safety helmet. So a user interface was created in which a selfie image of the driver is sent and a response is obtained that determines whether or not the helmet is worn. The solution was developed using a Telegramm bot that received the image and automatically responded with a predicted class and a subsequent message.
Visit the Smart Scooter Etseib repository for more technical information.
DIRECTION AND VEHICLE STATE SIGNALING
In order to give more visibility to the driver of these personal mobility vehicles, it was decided to open a place to develop a safety helmet (Smart Helmet) with a matrix of LEDs on the back of the helmet, with the aim of indicating directions and vehicle status to the different urban agents. A Dashboard was also created to run the developed program.
Visit the Smart Scooter Etseib repository for more technical information.
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ZONE CLASSIFIER
One of the biggest problems during the circulation of electric scooters is the non-compliance with the maximum speed allowed by zones. For this reason, a traffic zone classifier has been developed based on images captured by the Py Camera. To subsequently limit the speed to the speed allowed by that zone.
A Dashboard was also created to run the inference to the model and show the results.
Visit the Smart Scooter Etseib repository for more technical information.
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