HELMET DETECTOR
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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.
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Visit the Smart Scooter Etseib repository for more technical information.
DIRECTION AND VEHICLE STATE SIGNALING
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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.
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Visit the Smart Scooter Etseib repository for more technical information.
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ZONE CLASSIFIER
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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.
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Visit the Smart Scooter Etseib repository for more technical information.
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