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Diseño De Un Prototipo Electrónico Para El Control Automático De La Luz Alta De Un Vehículo Mediante Detección Inteligente De Otros Automóviles.”

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Diseño De Un Prototipo Electrónico Para El Control Automático De La Luz Alta De Un Vehículo Mediante Detección Inteligente De Otros Automóviles.” Tecnológico de Costa Rica Escuela Ingeniería Electrónica Proyecto de Graduación “Diseño de un prototipo electrónico para el control automático de la luz alta de un vehículo mediante detección inteligente de otros automóviles.” Informe de Proyecto de Graduación para optar por el título de Ingeniero en Electrónica con el grado académico de Licenciatura Ronald Miranda Arce San Carlos, 2018 2 Declaración de autenticidad Yo, Ronald Miranda Arce, en calidad de estudiante de la carrera de ingeniería Electrónica, declaro que los contenidos de este informe de proyecto de graduación son absolutamente originales, auténticos y de exclusiva responsabilidad legal y académica del autor. Ronald Fernando Miranda Arce Santa Clara, San Carlos, Costa Rica Cédula: 207320032 3 Resumen Se presenta el diseño y prueba de un prototipo electrónico para el control automático de la luz alta de un vehículo mediante la detección inteligente de otros automóviles, de esta forma mejorar la experiencia de manejo y seguridad al conducir en la noche, disminuyendo el riesgo de accidentes por deslumbramiento en las carreteras. Se expone el uso de machine learning (máquina de aprendizaje automático) para el entrenamiento de una red neuronal, que permita el reconocimiento en profundidad de patrones, en este caso identificar el patrón que describe un vehículo cuando se acerca durante la noche. Se entrenó exitosamente una red neuronal capaz de identificar correctamente hasta un 89% de los casos ocurridos en las pruebas realizadas, generando las señales para controlar de forma automática los cambios de luz. 4 Summary The design and testing of an electronic prototype for the automatic control of the vehicle's high light through the intelligent detection of other automobiles is presented. This improves driving experience and driving safety at night, reducing the risk of accidents due to glare on the roads. It exposes the use of machine learning for the training of a neural network, that allows deep recognition of patterns, in this case identify the pattern that describes a vehicle when it approaches. A neural network capable of correctly identifying up to 89% of the cases occurred in the tests performed was successfully trained, generating the signals to automatically control the high beam and low beam. 5 Índice General Resumen .............................................................................................................................................. 4 Summary .............................................................................................................................................. 5 Capítulo 1: Introducción ........................................................................................................... 10 1.1 Problema existente e importancia de su solución ..................................................... 11 1.2 Solución seleccionada ..................................................................................................... 12 Capítulo 2: Meta y objetivos ......................................................................................................... 14 2.1 Meta ..................................................................................................................................... 14 2.2 Objetivo general ................................................................................................................ 14 2.3 Objetivos específicos ...................................................................................................... 14 Capítulo 3: Marco teórico ............................................................................................................ 15 3.1.1 Aprendizaje automático .............................................................................................. 15 3.1.2 Redes neuronales ......................................................................................................... 16 3.1.3 Aprendizaje por transferencia ................................................................................... 16 3.1.4 Vector de características de imagen o “Bottleneck” ........................................... 17 3.1.5 Reentrenamiento de una red neuronal .................................................................... 17 3.1.6 Microprocesador Raspberry Pi 3 .............................................................................. 19 3.1.7 Placa de desarrollo Nvidea Drive AGX .................................................................... 20 3.1.8 Asus Tinker S ................................................................................................................ 21 3.1.9 Cámara ............................................................................................................................ 22 3.1.10 Módulos transmisores ESP286 ................................................................................. 23 3.1.11 Arduino Micro ............................................................................................................... 23 3.1.12 Relé ................................................................................................................................... 24 3.1.13 Encapsulado LM350 ..................................................................................................... 24 3.2 Antecedentes y estado de la cuestión ........................................................................ 25 3.2.1 Controlador inteligente de la luz alta IHC ............................................................... 25 3.2.2 Asistente de iluminación SmartBeam ..................................................................... 26 3.2.3 Focos LED adaptables ................................................................................................ 26 3.2.4 Sensor de seguridad AHB .......................................................................................... 26 Capítulo 4: Procedimiento Metodológico .................................................................................. 27 4.1 Reconocimiento y definición del problema .............................................................. 27 6 4.2 Obtención y análisis de información ........................................................................... 28 4.3 Evaluación de las alternativas y síntesis de una solución .................................... 29 4.4 Implementación de la solución .................................................................................... 31 4.4.2 Etapa 2 ....................................................................................................................... 32 4.4.3 Etapa 3 .............................................................................................................................. 33 4.4.4 Etapa 4 ........................................................................................................................... 34 4.4.5 Etapa 5 ........................................................................................................................... 34 4.5 Reevaluación y rediseño ................................................................................................ 35 Capítulo 5: Explicación detallada de la solución ..................................................................... 36 5.1 Análisis de las soluciones y selección final .............................................................. 36 5.1.1 Solución I .................................................................................................................... 37 5.1.2 Solución II ................................................................................................................... 38 5.1.3 Solución III .................................................................................................................. 39 5.2 Descripción del hardware................................................................................................... 40 5.2.1 Unidad de Alimentación ............................................................................................ 41 5.2.2 Unidad de reconocimiento de vehículos ............................................................ 43 5.3 Descripción del software ................................................................................................ 47 Capítulo 6: Análisis de Resultados ............................................................................................. 55 6.1 Resultados del entrenamiento ........................................................................................ 55 6.2 Análisis de imágenes ........................................................................................................ 58 6.3 Análisis de video .............................................................................................................. 61 6.4 Pruebas de filtrado ........................................................................................................... 70 6.5 Pruebas de suavizado ...................................................................................................... 72 6.6 Pruebas regulador de voltaje ......................................................................................... 73 6.7 Implementación en un computador de bajo costo.................................................... 74 Capítulo 7: Conclusiones y recomendaciones ........................................................................ 76 7.1 Conclusiones.....................................................................................................................
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