Adaptive Learning for Raspberry Pi Controlled Smart Streetlights

For the project, a raspberry pi, and an Arduino control house lights in response to detecting cars with ultrasonic distance sensors. The project aims to demonstrate that street lights could save energy by adapting to traffic via neural networks in the raspberry pi, which would make the lights turn off when no cars are driving by at night, and flickering could be reduced. The project also investigates whether lights consume more energy when flickering, as compared to being powered constantly. In theory, neural networks reduce the energy consumed by smart-street lights, as well as flickering; ultrasonic distance sensors detect cars and send feedback to the neural networks, and the networks adapt the lights’ behavior based off of the learned traffic patterns. The project has successfully detected a car and turned a house lamp on and off with an ultrasonic distance sensor