{"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\/fr\/using-convolutional-neural-networks-to-improve-road-safety-and-save-lives\/","title":{"rendered":"Utiliser les r\u00e9seaux neuronaux convolutifs pour am\u00e9liorer la s\u00e9curit\u00e9 routi\u00e8re et sauver des vies"},"content":{"rendered":"<p>Lois Anne Leal et Chaitree Sham Baradkar ont particip\u00e9 \u00e0 ce projet novateur <a href=\"https:\/\/irap.org\/fr\/2020\/11\/can-your-ai-skills-help-irap-prevent-road-death-and-injury\/\">D\u00e9fi iRAP Omdena de 8 semaines<\/a> Plus t\u00f4t cette ann\u00e9e, ils ont cherch\u00e9 des solutions bas\u00e9es sur l&#039;IA pour cartographier les risques d&#039;accidents et g\u00e9n\u00e9rer des donn\u00e9es d&#039;attributs pour la notation iRAP. Ils n&#039;\u00e9taient que 2 des 50 ing\u00e9nieurs en IA et en apprentissage automatique recrut\u00e9s dans le monde entier pour trouver une solution permettant la collecte et le codage acc\u00e9l\u00e9r\u00e9s et intelligents des donn\u00e9es d&#039;attributs routiers (<a href=\"https:\/\/irap.org\/fr\/project\/ai-rap\/\">AiRAP<\/a>) pour sauver des vies.<\/p>\n<p>Lois est sp\u00e9cialiste en recherche scientifique, ing\u00e9nieure en apprentissage automatique et d\u00e9veloppeuse certifi\u00e9e TensorFlow, originaire des Philippines\u00a0; Chaitree est data scientist chez PharmaACE et ing\u00e9nieure en apprentissage automatique chez Omdena, originaire de Pune, en Inde.<\/p>\n<p>Le d\u00e9fi iRAP Omdena avait 3 objectifs principaux\u00a0:<\/p>\n<ol class=\"postList\">\n<li class=\"graf graf--li\">Collecter des donn\u00e9es g\u00e9olocalis\u00e9es sur les accidents et produire des cartes de risque iRAP pr\u00e9sentant l&#039;historique des accidents par kilom\u00e8tre et le nombre d&#039;accidents par kilom\u00e8tre parcouru pour chaque usager de la route<\/li>\n<li class=\"graf graf--li\">Collecte de donn\u00e9es sur les caract\u00e9ristiques des routes, le flux de trafic et la vitesse selon la norme mondiale iRAP et cartographie des performances de s\u00e9curit\u00e9 et du classement par \u00e9toiles de plus de 100 millions de km de routes dans le monde.<\/li>\n<li class=\"graf graf--li\">\u00c9laborer des indicateurs cl\u00e9s de performance reproductibles pour les infrastructures routi\u00e8res, pouvant servir de base au suivi annuel des performances.<\/li>\n<\/ol>\n<p>Lois et Chaitree ont contribu\u00e9 au deuxi\u00e8me objectif en obtenant automatiquement la composante cruciale du nombre de v\u00e9hicules sous l&#039;attribut trafic ou flux de v\u00e9hicules \u00e0 l&#039;aide d&#039;images satellites avec l&#039;aide de l&#039;intelligence artificielle.<\/p>\n<p>Lisez leur blog publi\u00e9 : \u201c<a href=\"https:\/\/omdena.com\/blog\/ai-road-safety\/\">Utiliser les r\u00e9seaux neuronaux convolutifs pour am\u00e9liorer la s\u00e9curit\u00e9 routi\u00e8re et sauver des vies<\/a>\u201c&quot;.<\/p>\n<p>Nous remercions Lois et Chaitree, ainsi que tous les b\u00e9n\u00e9voles du Challenge, pour leur pr\u00e9cieuse contribution !<\/p>","protected":false},"excerpt":{"rendered":"<p>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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