{"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\/nl\/using-convolutional-neural-networks-to-improve-road-safety-and-save-lives\/","title":{"rendered":"Het gebruik van convolutionele neurale netwerken om de verkeersveiligheid te verbeteren en levens te redden."},"content":{"rendered":"<p>Lois Anne Leal en Chaitree Sham Baradkar namen deel aan het innovatieve project. <a href=\"https:\/\/irap.org\/nl\/2020\/11\/can-your-ai-skills-help-irap-prevent-road-death-and-injury\/\">iRAP Omdena 8 weken Challenge<\/a> Eerder dit jaar zochten ze naar AI-gebaseerde oplossingen om het risico op ongevallen in kaart te brengen en attribuutgegevens voor een iRAP-sterrenclassificatie te genereren. Ze waren slechts twee van de vijftig AI- en machine learning-ingenieurs die via crowdsourcing wereldwijd waren geselecteerd om een oplossing te vinden voor het versneld en intelligent verzamelen en coderen van gegevens over wegkenmerken.<a href=\"https:\/\/irap.org\/nl\/project\/ai-rap\/\">AiRAP<\/a>) om levens te redden.<\/p>\n<p>Lois is een specialist in wetenschappelijk onderzoek, machine learning-ingenieur en gecertificeerd TensorFlow-ontwikkelaar uit de Filipijnen, en Chaitree is data scientist bij PharmaACE en machine learning-ingenieur bij Omdena uit Pune, India.<\/p>\n<p>De iRAP Omdena Challenge had 3 hoofddoelen:<\/p>\n<ol class=\"postList\">\n<li class=\"graf graf--li\">Verzamel geolocatiegegevens van ongevallen en produceer iRAP-risicokaarten van het aantal historische ongevallen per kilometer en het aantal ongevallen per afgelegde kilometer voor elke weggebruiker.<\/li>\n<li class=\"graf graf--li\">Verzamel gegevens over wegkenmerken, verkeersstromen en snelheden volgens de wereldwijde iRAP-standaard en breng de veiligheidsprestaties en sterrenclassificatie in kaart van meer dan 100 miljoen kilometer aan wegen wereldwijd.<\/li>\n<li class=\"graf graf--li\">Ontwikkel herhaalbare prestatie-indicatoren voor de wegeninfrastructuur die als basis kunnen dienen voor jaarlijkse prestatiemonitoring.<\/li>\n<\/ol>\n<p>Lois en Chaitree droegen bij aan het tweede doel door met behulp van kunstmatige intelligentie automatisch het cruciale onderdeel &#039;aantal voertuigen&#039; te achterhalen onder het attribuut &#039;verkeer&#039; of &#039;voertuigstroom&#039;, door satellietbeelden te gebruiken.<\/p>\n<p>Lees hun gepubliceerde blog: \u201c<a href=\"https:\/\/omdena.com\/blog\/ai-road-safety\/\">Het gebruik van convolutionele neurale netwerken om de verkeersveiligheid te verbeteren en levens te redden.<\/a>\u201c.<\/p>\n<p>Onze dank gaat uit naar Lois en Chaitree, en alle vrijwilligers van de Challenge voor hun waardevolle bijdrage!<\/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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