Students from the Federal University of Uberlândia (UFU) developed an application called 'Achei,' designed to identify unoccupied parking spots on public streets. This project uses artificial intelligence and cameras to monitor the spaces and present drivers with the location of free spots via a map.
The creation of this tool was carried out by students from the Electronic Engineering, Telecommunications, and Biotechnology courses, belonging to the Patos de Minas campus. The development took place during the hackathon named 'Smart Cities,' a competition focused on developing technological solutions for urban mobility.
The system's operation is based on images collected by cameras positioned in strategic locations on the streets. These images are transmitted to a server, where a computer vision and artificial intelligence system determines which spots are occupied and which remain available.
The team won first place in the contest, which was organized by the University Center of Patos de Minas (Unipam), and as a result, the students received mentorships and practical opportunities to improve the project.
Although it has undergone preliminary tests, 'Achei' is still in the development phase and is not available for general public use. The students have already conducted simulations using computer vision models and carried out tests with a real vehicle.
Possibility of Partnership
The team responsible for the 'Achei' project is analyzing the feasibility of establishing a collaboration with the City Hall of Patos de Minas to advance the work. One of the suggestions presented is to utilize the cameras already integrated into the municipal infrastructure, which would reduce the need for installing new equipment.
For the application to operate fully, it is still necessary to define the required infrastructure for data processing and mapping technologies, in addition to evaluating aspects such as cost, maintenance, and system accuracy.
Additionally, the students plan to expand the tests by capturing images at various times of the day and under different weather conditions, aiming to increase the reliability and identification capacity of the spots.