Design of a low-cost AI system for the modernization of conventional cars

• Autores: Rojas, Karin, Auccahuasi, Wilver, Urbano, Kitty, Meza, Sandra, Romero-Echevarria, Luis, Portillo-Allende, Arlich, Figueroa-Revilla, Jorge, Sanchez-Atuncar, Giancarlo, Arroyo, Sergio, Castro-Mejia, Percy Junior
• Formato: artículo
• Fecha de Publicación: 2024
• Institución: Universidad Tecnológica del Perú
• Repositorio: UTP-Institucional
• Lenguaje: inglés
• OAI Identifier: oai:repositorio.utp.edu.pe:20.500.12867/14519
• Enlace del recurso: https://hdl.handle.net/20.500.12867/14519; https://doi.org/10.3390/wevj15100455
• Nivel de acceso: acceso abierto
• Materia: Recognition; Cars; Modernization; Artificial intelligence; https://purl.org/pe-repo/ocde/ford#2.02.04

Artificial intelligence techniques are beginning to be implemented in most areas. In the particular case of automobiles, new cars include integrated applications, such as cameras in different configurations, including in the rear of the car to provide assistance while reversing, as well as front and side cameras; these applications also include different configurations of sensors that provide information to the driver, such as objects approaching from different directions, such as from the front and sides. In this paper, we propose a practical and low-cost methodology to provide solutions using artificial intelligence techniques, as is the purpose of YOLO architecture, version 3, using hardware based on Nvidia’s Jetson TK1 architecture, and configurations in conventional cars. The results that we present demonstrate that these technologies can be applied in conventional cars, working with independent power to avoid causing problems in these cars, and we evaluate their application in the detection of people and cars in different situations, which allows information to be provided to the driver while performing maneuvers. The methodology that we provide can be replicated and scaled according to needs.