{"id":3290,"date":"2024-10-10T10:58:24","date_gmt":"2024-10-10T08:58:24","guid":{"rendered":"https:\/\/www.mosaicfactor.com\/project\/synergies\/"},"modified":"2025-01-02T15:48:39","modified_gmt":"2025-01-02T14:48:39","slug":"synergies","status":"publish","type":"proyecto","link":"https:\/\/www.mosaicfactor.com\/es\/projects\/synergies\/","title":{"rendered":"SYNERGIES"},"content":{"rendered":"

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Forecasting model and maintenance<\/p>\n

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Cliente<\/h2>\n

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Partners<\/h2>\n

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global_colors_info=\u00bb{}\u00bb theme_builder_area=\u00bbpost_content\u00bb][\/et_pb_image][et_pb_image src=\u00bbhttps:\/\/www.mosaicfactor.com\/wp-content\/uploads\/2024\/11\/project-synergies-swiss-re.webp\u00bb title_text=\u00bbproject-synergies-swiss-re\u00bb url=\u00bbhttp:\/\/www.swissre.com\/\u00bb url_new_window=\u00bbon\u00bb _builder_version=\u00bb4.27.2″ _module_preset=\u00bbdefault\u00bb width=\u00bb180px\u00bb max_width=\u00bb180px\u00bb custom_css_main_element=\u00bbmax-width:fit-content;\u00bb global_colors_info=\u00bb{}\u00bb theme_builder_area=\u00bbpost_content\u00bb][\/et_pb_image][et_pb_image src=\u00bbhttps:\/\/www.mosaicfactor.com\/wp-content\/uploads\/2024\/11\/project-synergies-fev.webp\u00bb title_text=\u00bbproject-synergies-fev\u00bb url=\u00bbhttps:\/\/fev.io\/\u00bb url_new_window=\u00bbon\u00bb _builder_version=\u00bb4.27.2″ _module_preset=\u00bbdefault\u00bb width=\u00bb160px\u00bb max_width=\u00bb180px\u00bb custom_css_main_element=\u00bbmax-width:fit-content;\u00bb global_colors_info=\u00bb{}\u00bb theme_builder_area=\u00bbpost_content\u00bb][\/et_pb_image][\/et_pb_column][\/et_pb_row][et_pb_row admin_label=\u00bbrow\u00bb _builder_version=\u00bb4.27.2″ background_size=\u00bbinitial\u00bb background_position=\u00bbtop_left\u00bb background_repeat=\u00bbrepeat\u00bb width=\u00bb100%\u00bb global_colors_info=\u00bb{}\u00bb theme_builder_area=\u00bbpost_content\u00bb][et_pb_column type=\u00bb4_4″ _builder_version=\u00bb4.16″ custom_padding=\u00bb|||\u00bb global_colors_info=\u00bb{}\u00bb custom_padding__hover=\u00bb|||\u00bb theme_builder_area=\u00bbpost_content\u00bb][et_pb_text _builder_version=\u00bb4.27.2″ _module_preset=\u00bbdefault\u00bb text_font_size=\u00bb18px\u00bb text_line_height=\u00bb1.6em\u00bb custom_margin=\u00bb||1em||false|false\u00bb global_colors_info=\u00bb{}\u00bb theme_builder_area=\u00bbpost_content\u00bb]<\/p>\n

Problema<\/h2>\n

[\/et_pb_text][et_pb_text _builder_version=\u00bb4.27.2″ _module_preset=\u00bbdefault\u00bb text_font_size=\u00bb18px\u00bb text_line_height=\u00bb1.6em\u00bb global_colors_info=\u00bb{}\u00bb theme_builder_area=\u00bbpost_content\u00bb]<\/p>\n

Es necesario garantizar la seguridad de los sistemas CCAM (Movilidad Conectada, Cooperativa y Automatizada).<\/p>\n

El principal reto para la validaci\u00f3n de la seguridad es que, en funci\u00f3n del ODD (Operational Design Domain), es necesario probar y validar muchas situaciones de conducci\u00f3n diferentes y escenarios complejos. Adem\u00e1s, el sector automovil\u00edstico europeo se gu\u00eda por estrictas normas de prueba y validaci\u00f3n que imponen obligatoriamente una evaluaci\u00f3n exhaustiva de las posibles situaciones a las que se enfrentar\u00e1 un sistema CCAM (incluidos escenarios complejos de m\u00faltiples actores, peligros, situaciones inusuales y condiciones desafiantes).<\/p>\n

Sin embargo, la complejidad e incertidumbre del entorno de conducci\u00f3n, y la complejidad de la propia tarea de conducci\u00f3n, implican que el n\u00famero de escenarios posibles que puede encontrar un Sistema de Conducci\u00f3n Automatizada (ADS) es pr\u00e1cticamente infinito.<\/p>\n

Otros estudios sugieren que para probar un sistema CCAM para evaluar su seguridad y demostrar que es un 20% mejor que un veh\u00edculo conducido por humanos, es necesario conducirlo durante m\u00e1s de 11 mil millones de millas. Adem\u00e1s, las pruebas basadas en peligros (HBT, Hazard Based Testing) y los sistemas sociot\u00e9cnicos defienden que el n\u00famero de millas recorridas por s\u00ed solo no es suficiente para juzgar la confianza en los sistemas CCAM.<\/p>\n

En cambio, el aspecto crucial es el rango y la variedad de escenarios encontrados durante los tests. El objetivo es comprender e identificar \u00abc\u00f3mo un sistema puede fallar o comportarse mal\u00bb y, posteriormente, asegurarse de que lo hace de manera segura y confiable. La naturaleza de los escenarios es fundamental para una evaluaci\u00f3n de la seguridad.<\/p>\n

Los desaf\u00edos clave que abordaremos a partir de la identificaci\u00f3n de escenarios son:<\/p>\n

    \n
  1. \n
      \n
    1. Identificaci\u00f3n de los escenarios \u00abcorrectos\u00bb y \u00abrepresentativos\u00bb para que el ODD sea relevante.<\/li>\n
    2. Comprender si se han identificado \u00absuficientes\u00bb escenarios para garantizar la seguridad del sistema.<\/li>\n
    3. Considerar la probabilidad de ocurrencia de los escenarios.<\/li>\n
    4. Definici\u00f3n del riesgo de seguridad asociado con el escenario y la respuesta del veh\u00edculo al escenario. Un aspecto clave de esto es la definici\u00f3n de \u00abbuen comportamiento\u00bb o criterios de aprobaci\u00f3n\/reprobaci\u00f3n del sistema para un escenario determinado.<\/li>\n<\/ol>\n<\/li>\n<\/ol>\n

      [\/et_pb_text][et_pb_text _builder_version=\u00bb4.27.2″ _module_preset=\u00bbdefault\u00bb text_font_size=\u00bb18px\u00bb text_line_height=\u00bb1.6em\u00bb custom_margin=\u00bb||1em||false|false\u00bb global_colors_info=\u00bb{}\u00bb theme_builder_area=\u00bbpost_content\u00bb]<\/p>\n

      Soluci\u00f3n<\/h2>\n

      [\/et_pb_text][et_pb_text _builder_version=\u00bb4.27.2″ _module_preset=\u00bbdefault\u00bb text_font_size=\u00bb18px\u00bb text_line_height=\u00bb1.6em\u00bb global_colors_info=\u00bb{}\u00bb theme_builder_area=\u00bbpost_content\u00bb]<\/p>\n

      Mediante el uso de big data, junto con m\u00e1s de 30 socios y aprovechando los conocimientos de los an\u00e1lisis de riesgos y las pruebas operativas de campo anteriores (FOT, Field Operational Tests), permitiremos una r\u00e1pida identificaci\u00f3n e implementaci\u00f3n en el mercado para mejorar la seguridad de los sistemas CCAM.<\/p>\n

      Adem\u00e1s, nuestro uso de herramientas basadas en inteligencia artificial (IA) ayudar\u00e1 a ampliar el espacio de requisitos del sistema, haciendo que el proceso sea m\u00e1s eficaz y eficiente. Esto permitir\u00e1 que los sistemas CCAM sean seguros desde el testeo hasta la implementaci\u00f3n. <\/p>\n

      Nuestra plataforma SYNERGIES proporciona a las partes interesadas europeas:<\/p>\n

        \n
      1. \n
          \n
        1. los escenarios y herramientas necesarios para el desarrollo acelerado y aceptado,<\/li>\n
        2. la formaci\u00f3n,<\/li>\n
        3. las pruebas virtuales<\/li>\n
        4. y la validaci\u00f3n de los sistemas CCAM, reduciendo el tiempo y los costes de desarrollo, aumentando la seguridad y la fiabilidad, y apoyando una adopci\u00f3n m\u00e1s amplia.<\/strong><\/li>\n<\/ol>\n<\/li>\n<\/ol>\n

          Nuestra contribuci\u00f3n se centra en los requisitos de fiabilidad de los datos y en la definici\u00f3n de las m\u00e9tricas de confianza de los datos de los escenarios. Tambi\u00e9n investigamos para detectar y pronosticar las nuevas tecnolog\u00edas, nuevos escenarios, nuevos actores, tendencias del mercado, etc.<\/p>\n

          Definiremos hojas de ruta de alto nivel por caso y analizaremos c\u00f3mo evolucionan los requisitos a lo largo del tiempo por hoja de ruta (incluido el an\u00e1lisis de impacto y probabilidad para priorizar los cambios en los requisitos). A continuaci\u00f3n, evaluaremos la aplicabilidad de los requisitos futuros teniendo en cuenta las nuevas tecnolog\u00edas, las innovaciones automotrices, las capacidades de datos y hardware.\n<\/p>\n

          Definiremos hojas de ruta de alto nivel por caso y analizaremos c\u00f3mo evolucionan los requisitos a lo largo del tiempo por hoja de ruta (incluido el an\u00e1lisis de impacto y probabilidad para priorizar los cambios en los requisitos). A continuaci\u00f3n, evaluaremos la aplicabilidad de los requisitos futuros teniendo en cuenta las nuevas tecnolog\u00edas, las innovaciones automotrices, las capacidades de datos y hardware.\n<\/p>\n

          En la evaluaci\u00f3n de la calidad de los datos, construiremos una metodolog\u00eda para analizar tanto los datos disponibles como los datos generados sint\u00e9ticamente en t\u00e9rminos de calidad y relevancia para su potencial para generar escenarios, definiremos las m\u00e9tricas adecuadas para cada descriptor de calidad junto con umbrales aceptables, que se definir\u00e1n a trav\u00e9s de la investigaci\u00f3n estad\u00edstica y los expertos en el dominio, y examinaremos las brechas y limitaciones de los conjuntos de datos y proporcionaremos retroalimentaci\u00f3n al propietario\/proveedor de datos con el fin de para aumentar la utilidad\/precisi\u00f3n\/correcci\u00f3n\/integridad de los datos. Desarrollaremos un inspector de datos impulsado por IA para la calidad de los datos para evaluar la correcci\u00f3n, integridad, precisi\u00f3n y consistencia de los conjuntos de datos y la puntualidad, validar el an\u00e1lisis y evaluar la importancia de la caracter\u00edstica. MOSAIC tambi\u00e9n crear\u00e1 una metodolog\u00eda y\/o herramienta que eval\u00fae la representatividad de un escenario aplicado en otras \u00e1reas o pa\u00edses.<\/p>\n

          En cuanto a la plataforma, contribuiremos en la definici\u00f3n de la estrategia para integrar los componentes necesarios dentro de la plataforma, participaremos en las actividades de prueba de los componentes individuales de la plataforma y contribuiremos a la implementaci\u00f3n de mejoras despu\u00e9s de la fase de prueba.<\/p>\n

          [\/et_pb_text][et_pb_text _builder_version=\u00bb4.27.2″ _module_preset=\u00bbdefault\u00bb text_font_size=\u00bb18px\u00bb text_line_height=\u00bb1.6em\u00bb locked=\u00bboff\u00bb global_colors_info=\u00bb{}\u00bb theme_builder_area=\u00bbpost_content\u00bb]<\/p>\n

          Data<\/h2>\n

          [\/et_pb_text][et_pb_video src=\u00bbhttps:\/\/youtu.be\/h0NX5FoIiy8?si=0-yKftx22GYmwjoW\u00bb _builder_version=\u00bb4.27.2″ _module_preset=\u00bbdefault\u00bb custom_margin=\u00bb||||false|false\u00bb custom_padding=\u00bb200px||||false|false\u00bb custom_padding_tablet=\u00bb200px||||false|false\u00bb custom_padding_phone=\u00bb200px||||false|false\u00bb custom_padding_last_edited=\u00bbon|phone\u00bb global_colors_info=\u00bb{}\u00bb theme_builder_area=\u00bbpost_content\u00bb locked=\u00bboff\u00bb][\/et_pb_video][et_pb_text _builder_version=\u00bb4.27.2″ _module_preset=\u00bbdefault\u00bb text_font_size=\u00bb18px\u00bb text_line_height=\u00bb1.6em\u00bb locked=\u00bboff\u00bb global_colors_info=\u00bb{}\u00bb theme_builder_area=\u00bbpost_content\u00bb]<\/p>\n

          Resultados<\/h2>\n

          [\/et_pb_text][et_pb_text _builder_version=\u00bb4.27.2″ _module_preset=\u00bbdefault\u00bb text_font_size=\u00bb18px\u00bb text_line_height=\u00bb1.6em\u00bb locked=\u00bboff\u00bb global_colors_info=\u00bb{}\u00bb theme_builder_area=\u00bbpost_content\u00bb]<\/p>\n

          El proyecto dar\u00e1 lugar a una plataforma europea destinada a mejorar el desarrollo, la formaci\u00f3n, las pruebas virtuales y la validaci\u00f3n de los sistemas CCAM.<\/p>\n

          La Plataforma SYNERGIES consta de:<\/p>\n

            \n
          1. \n
              \n
            1. Un Scenario Dataspace<\/strong>, alineado con el enfoque europeo sobre compartici\u00f3n de datos y competitividad,<\/li>\n
            2. y un Marketplace<\/strong>, para garantizar la actualizaci\u00f3n continua y la escalabilidad del Dataspace.<\/li>\n<\/ol>\n<\/li>\n<\/ol>\n

              [\/et_pb_text][\/et_pb_column][\/et_pb_row][\/et_pb_section]<\/p>\n","protected":false},"excerpt":{"rendered":"

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