This project is formed by a group of students and professors at the UPC-ETSEIB with the aim of developing technological systems that convert a conventional electric scooter to an intelligent one.
The challenge was to overcome the challenges of the Smart Scooter Challenge. However there has been more developements. You can find more information about them in the official Smart Scooter Etseib website.
In here you will find the code to create a Traffic Lights with and without color detector as well as a cones detector. It all has been tested on Linux and Raspberry Pi 4.
In here you will find the code to create a scooter riding zone classifier, as well as all the backend code (Go) to create the endpoint and do the inference.
Here you'll find the necessary code (Arduino) to create the communication between the dashboard and the ESP32, through MQTT, and the code to light up the LED's of the Matrix.
To find more info about this solution, read this paper.
Using computer vision algorithms, detect driving lane lines, as well as, according to the captured inclination of the lines, define whether the scooter is in or out of the lane.
To display the information of every solution, there is a Node-RED flow (.json) for it, with a complete dashboard.
This flow uses the Teachable Machine tool (together with the TM node-red node) and a Telegramm Bot, to classify wether the rider wears or not a safety helmet.
Examples:
