{"id":2483,"date":"2021-03-29T03:02:40","date_gmt":"2021-03-28T16:02:40","guid":{"rendered":"https:\/\/wordpress.temp.irap-dev.org\/using-convolutional-neural-networks-to-improve-road-safety-and-save-lives\/"},"modified":"2026-04-30T12:38:05","modified_gmt":"2026-04-30T02:38:05","slug":"using-convolutional-neural-networks-to-improve-road-safety-and-save-lives","status":"publish","type":"post","link":"https:\/\/irap.org\/es\/using-convolutional-neural-networks-to-improve-road-safety-and-save-lives\/","title":{"rendered":"Utilizaci\u00f3n de redes neuronales convolucionales para mejorar la seguridad vial y salvar vidas."},"content":{"rendered":"

Lois Anne Leal y Chaitree Sham Baradkar participaron en la innovadora Desaf\u00edo de 8 semanas iRAP Omdena<\/a> A principios de este a\u00f1o, se reunieron para buscar soluciones basadas en IA para mapear el riesgo de accidentes y generar datos de atributos de calificaci\u00f3n de estrellas iRAP. Eran solo 2 de 50 ingenieros de IA y aprendizaje autom\u00e1tico de todo el mundo que se centraron en encontrar una soluci\u00f3n para la recopilaci\u00f3n y codificaci\u00f3n acelerada e inteligente de datos de atributos de carreteras (AiRAP<\/a>) para salvar vidas.<\/p>\n

Lois es especialista en investigaci\u00f3n cient\u00edfica, ingeniera de aprendizaje autom\u00e1tico y desarrolladora certificada de TensorFlow, originaria de Filipinas, y Chaitree es cient\u00edfica de datos en PharmaACE e ingeniera de aprendizaje autom\u00e1tico en Omdena, originaria de Pune, India.<\/p>\n

El desaf\u00edo iRAP Omdena ten\u00eda 3 objetivos principales:<\/p>\n

    \n
  1. Obtener datos de accidentes geolocalizados y generar mapas de riesgo iRAP de los accidentes hist\u00f3ricos por kil\u00f3metro y los accidentes por kil\u00f3metro recorrido para cada usuario de la carretera.<\/li>\n
  2. Obtener datos sobre atributos de carreteras, flujo de tr\u00e1fico y velocidad seg\u00fan el est\u00e1ndar global iRAP y mapear el rendimiento de seguridad y la calificaci\u00f3n por estrellas de m\u00e1s de 100 millones de km de carreteras en todo el mundo.<\/li>\n
  3. Elaborar indicadores clave de rendimiento repetibles para la infraestructura vial que puedan servir de base para el seguimiento anual del rendimiento.<\/li>\n<\/ol>\n

    Lois y Chaitree contribuyeron al segundo objetivo al obtener autom\u00e1ticamente el componente crucial del recuento de veh\u00edculos dentro del atributo de tr\u00e1fico o flujo vehicular utilizando im\u00e1genes satelitales con la ayuda de la Inteligencia Artificial.<\/p>\n

    Lee su blog publicado: \u201cUtilizaci\u00f3n de redes neuronales convolucionales para mejorar la seguridad vial y salvar vidas.<\/a>\u201c.<\/p>\n

    \u00a1Nuestro agradecimiento a Lois y Chaitree, y a todos los voluntarios del Desaf\u00edo por su valiosa contribuci\u00f3n!<\/p>","protected":false},"excerpt":{"rendered":"

    Lois Anne Leal and Chaitree Sham Baradkar participated in the innovative iRAP Omdena 8 week Challenge earlier this year to find AI-based solutions to map crash risk and generate iRAP star rating attribute data. 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