{"id":31861,"date":"2025-11-24T17:04:34","date_gmt":"2025-11-24T17:04:34","guid":{"rendered":"https:\/\/www.tun.com\/home\/?p=31861"},"modified":"2025-11-24T17:04:35","modified_gmt":"2025-11-24T17:04:35","slug":"new-ai-system-enhances-traffic-safety-using-citywide-camera-footage","status":"publish","type":"post","link":"https:\/\/www.tun.com\/home\/new-ai-system-enhances-traffic-safety-using-citywide-camera-footage\/","title":{"rendered":"New AI System Enhances Traffic Safety Using Citywide Camera Footage"},"content":{"rendered":"\n<div class=\"wp-block-group\"><div class=\"wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained\">\n<div class=\"wp-block-uagb-blockquote uagb-block-e7eb3fc3 uagb-blockquote__skin-border uagb-blockquote__stack-img-none\"><blockquote class=\"uagb-blockquote\"><div class=\"uagb-blockquote__content\">Researchers at NYU Tandon School of Engineering have developed an AI system, SeeUnsafe, to improve road safety by analyzing traffic camera footage for collisions and near-misses.<\/div><footer><div class=\"uagb-blockquote__author-wrap uagb-blockquote__author-at-left\"><\/div><\/footer><\/blockquote><\/div>\n\n\n\n<div class=\"wp-block-group is-content-justification-space-between is-nowrap is-layout-flex wp-container-core-group-is-layout-0dfbf163 wp-block-group-is-layout-flex\"><div style=\"font-size:16px;\" class=\"has-text-align-left wp-block-post-author\"><div class=\"wp-block-post-author__content\"><p class=\"wp-block-post-author__name\">The University Network<\/p><\/div><\/div>\n\n\n<div class=\"wp-block-uagb-social-share uagb-social-share__outer-wrap uagb-social-share__layout-horizontal uagb-block-ee584a31\">\n<div class=\"wp-block-uagb-social-share-child uagb-ss-repeater uagb-ss__wrapper uagb-block-ec619ce7\"><span class=\"uagb-ss__link\" data-href=\"https:\/\/www.facebook.com\/sharer.php?u=\" tabindex=\"0\" role=\"button\" aria-label=\"facebook\"><span class=\"uagb-ss__source-wrap\"><span class=\"uagb-ss__source-icon\"><svg xmlns=\"https:\/\/www.w3.org\/2000\/svg\" viewBox=\"0 0 512 512\"><path d=\"M504 256C504 119 393 8 256 8S8 119 8 256c0 123.8 90.69 226.4 209.3 245V327.7h-63V256h63v-54.64c0-62.15 37-96.48 93.67-96.48 27.14 0 55.52 4.84 55.52 4.84v61h-31.28c-30.8 0-40.41 19.12-40.41 38.73V256h68.78l-11 71.69h-57.78V501C413.3 482.4 504 379.8 504 256z\"><\/path><\/svg><\/span><\/span><\/span><\/div>\n\n\n\n<div class=\"wp-block-uagb-social-share-child uagb-ss-repeater uagb-ss__wrapper uagb-block-32d99934\"><span class=\"uagb-ss__link\" data-href=\"https:\/\/twitter.com\/share?url=\" tabindex=\"0\" role=\"button\" aria-label=\"twitter\"><span class=\"uagb-ss__source-wrap\"><span class=\"uagb-ss__source-icon\"><svg xmlns=\"https:\/\/www.w3.org\/2000\/svg\" viewBox=\"0 0 512 512\"><path d=\"M389.2 48h70.6L305.6 224.2 487 464H345L233.7 318.6 106.5 464H35.8L200.7 275.5 26.8 48H172.4L272.9 180.9 389.2 48zM364.4 421.8h39.1L151.1 88h-42L364.4 421.8z\"><\/path><\/svg><\/span><\/span><\/span><\/div>\n\n\n\n<div class=\"wp-block-uagb-social-share-child uagb-ss-repeater uagb-ss__wrapper uagb-block-1d136f14\"><span class=\"uagb-ss__link\" data-href=\"https:\/\/www.linkedin.com\/shareArticle?url=\" tabindex=\"0\" role=\"button\" aria-label=\"linkedin\"><span class=\"uagb-ss__source-wrap\"><span class=\"uagb-ss__source-icon\"><svg xmlns=\"https:\/\/www.w3.org\/2000\/svg\" viewBox=\"0 0 448 512\"><path d=\"M416 32H31.9C14.3 32 0 46.5 0 64.3v383.4C0 465.5 14.3 480 31.9 480H416c17.6 0 32-14.5 32-32.3V64.3c0-17.8-14.4-32.3-32-32.3zM135.4 416H69V202.2h66.5V416zm-33.2-243c-21.3 0-38.5-17.3-38.5-38.5S80.9 96 102.2 96c21.2 0 38.5 17.3 38.5 38.5 0 21.3-17.2 38.5-38.5 38.5zm282.1 243h-66.4V312c0-24.8-.5-56.7-34.5-56.7-34.6 0-39.9 27-39.9 54.9V416h-66.4V202.2h63.7v29.2h.9c8.9-16.8 30.6-34.5 62.9-34.5 67.2 0 79.7 44.3 79.7 101.9V416z\"><\/path><\/svg><\/span><\/span><\/span><\/div>\n<\/div>\n<\/div>\n<\/div><\/div>\n\n\n\n<p>New York City\u2019s vast network of traffic cameras captures countless hours of video every day, creating a treasure trove of data that, until now, has been challenging to fully utilize. That\u2019s set to change with a groundbreaking development from researchers at New York University (NYU) Tandon School of Engineering. Their new artificial intelligence system, SeeUnsafe, aims to enhance road safety by automatically identifying collisions and near-misses in extensive traffic footage.<\/p>\n\n\n\n<p><a href=\"https:\/\/www.sciencedirect.com\/science\/article\/abs\/pii\/S0001457525001630\" target=\"_blank\" rel=\"noopener\" title=\"\">Published<\/a> in the journal Accident Analysis and Prevention, this innovative research has already earned the New York City&#8217;s Vision Zero Research Award, aligning with the city\u2019s road safety priorities. <\/p>\n\n\n\n<p>Senior author Kaan Ozbay, a professor in the Department of Civil and Urban Engineering and director of NYU Tandon&#8217;s\u00a0C2SMART center, presented the study at this year&#8217;s <a href=\"https:\/\/www.nyc.gov\/content\/visionzero\/pages\/events\" target=\"_blank\" rel=\"noopener\" title=\"\">Research on the Road symposium<\/a> on Nov. 19.<\/p>\n\n\n\n<p>SeeUnsafe leverages pre-trained AI models to understand both visual data and text, making it one of the first applications of multimodal large language models for analyzing long-form traffic videos. <\/p>\n\n\n\n<p>&#8220;You have a thousand cameras running 24\/7 in New York City. Having people examine and analyze all that footage manually is untenable,&#8221; Ozbay said in a news release. &#8220;SeeUnsafe gives city officials a highly effective way to take full advantage of that existing investment.&#8221;<\/p>\n\n\n\n<p>The AI system addresses a critical gap in traffic safety management \u2014 resource limitations in analyzing vast amounts of video footage. By identifying where and when incidents occur, SeeUnsafe allows transportation agencies to pinpoint hazardous intersections and conditions needing intervention before severe accidents happen. <\/p>\n\n\n\n<p>&#8220;Agencies don&#8217;t need to be computer vision experts. They can use this technology without the need to collect and label their own data to train an AI-based video analysis model,&#8221; added co-author Chen Feng, an associate professor at NYU Tandon and a co-founding director of the Center for Robotics and Embodied Intelligence.<\/p>\n\n\n\n<p>Tested on the Toyota Woven Traffic Safety dataset, SeeUnsafe outperformed other models, correctly classifying traffic incidents 76.71% of the time and identifying involved road users with success rates as high as 87.5%. <\/p>\n\n\n\n<p>This significant accuracy means the system can provide actionable insights into traffic safety, potentially preventing accidents by informing timely interventions like improved signage, better signal timings and redesigned road layouts based on near-misses and collision patterns rather than waiting for accidents to happen.<\/p>\n\n\n\n<p>The system\u2019s ability to generate road safety reports with natural language explanations allows it to describe factors like weather conditions, traffic volume and specific movements leading to near-misses or collisions. <\/p>\n\n\n\n<p>Despite some limitations, such as sensitivity to object tracking accuracy and challenges under low-light conditions, the researchers believe SeeUnsafe lays a crucial foundation for further AI advancements in road safety.<\/p>\n\n\n\n<div style=\"height:14px\" aria-hidden=\"true\" class=\"wp-block-spacer\"><\/div>\n\n\n\n<p><strong>Source:<\/strong> <a href=\"https:\/\/engineering.nyu.edu\/news\/new-ai-language-vision-models-transform-traffic-video-analysis-improve-road-safety\" target=\"_blank\" rel=\"noopener\" title=\"\">NYU Tandon School of Engineering<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>New York City\u2019s vast network of traffic cameras captures countless hours of video every day, creating a treasure trove of data that, until now, has been challenging to fully utilize. That\u2019s set to change with a groundbreaking development from researchers at New York University (NYU) Tandon School of Engineering. Their new artificial intelligence system, SeeUnsafe, [&hellip;]<\/p>\n","protected":false},"author":3,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"single-no-separators","format":"standard","meta":{"_acf_changed":false,"_uag_custom_page_level_css":"","_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"footnotes":""},"categories":[8],"tags":[59],"class_list":["post-31861","post","type-post","status-publish","format-standard","hentry","category-ai","tag-nyu"],"acf":[],"aioseo_notices":[],"uagb_featured_image_src":{"full":false,"thumbnail":false,"medium":false,"medium_large":false,"large":false,"1536x1536":false,"2048x2048":false},"uagb_author_info":{"display_name":"The University Network","author_link":"https:\/\/www.tun.com\/home\/author\/funky_junkie\/"},"uagb_comment_info":0,"uagb_excerpt":"New York City\u2019s vast network of traffic cameras captures countless hours of video every day, creating a treasure trove of data that, until now, has been challenging to fully utilize. That\u2019s set to change with a groundbreaking development from researchers at New York University (NYU) Tandon School of Engineering. Their new artificial intelligence system, SeeUnsafe,&hellip;","_links":{"self":[{"href":"https:\/\/www.tun.com\/home\/wp-json\/wp\/v2\/posts\/31861","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.tun.com\/home\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.tun.com\/home\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.tun.com\/home\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/www.tun.com\/home\/wp-json\/wp\/v2\/comments?post=31861"}],"version-history":[{"count":8,"href":"https:\/\/www.tun.com\/home\/wp-json\/wp\/v2\/posts\/31861\/revisions"}],"predecessor-version":[{"id":31873,"href":"https:\/\/www.tun.com\/home\/wp-json\/wp\/v2\/posts\/31861\/revisions\/31873"}],"wp:attachment":[{"href":"https:\/\/www.tun.com\/home\/wp-json\/wp\/v2\/media?parent=31861"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.tun.com\/home\/wp-json\/wp\/v2\/categories?post=31861"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.tun.com\/home\/wp-json\/wp\/v2\/tags?post=31861"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}