  {"id":190367,"date":"2024-01-18T10:22:24","date_gmt":"2024-01-18T20:22:24","guid":{"rendered":"https:\/\/www.hawaii.edu\/news\/?p=190367"},"modified":"2024-01-18T10:22:24","modified_gmt":"2024-01-18T20:22:24","slug":"enhancing-road-safety-dot-award","status":"publish","type":"post","link":"https:\/\/www.hawaii.edu\/news\/2024\/01\/18\/enhancing-road-safety-dot-award\/","title":{"rendered":"Enhancing road safety: Engineering professor wins $100<abbr>K<\/abbr> for <abbr>AI<\/abbr> innovation"},"content":{"rendered":"<span class=\"span-reading-time rt-reading-time\" style=\"display: block;\"><span class=\"rt-label rt-prefix\">Reading time: <\/span> <span class=\"rt-time\"> 2<\/span> <span class=\"rt-label rt-postfix\">minutes<\/span><\/span><p><img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/www.hawaii.edu\/news\/wp-content\/uploads\/2021\/04\/manoa-uhero-traffic-hawaii.jpg\" alt=\"several cars on a roadway\" width=\"676\" height=\"381\" class=\"alignleft size-full wp-image-140365\" srcset=\"https:\/\/www.hawaii.edu\/news\/wp-content\/uploads\/2021\/04\/manoa-uhero-traffic-hawaii.jpg 676w, https:\/\/www.hawaii.edu\/news\/wp-content\/uploads\/2021\/04\/manoa-uhero-traffic-hawaii-300x169.jpg 300w, https:\/\/www.hawaii.edu\/news\/wp-content\/uploads\/2021\/04\/manoa-uhero-traffic-hawaii-130x73.jpg 130w\" sizes=\"auto, (max-width: 676px) 100vw, 676px\" \/><\/p>\n<p>Our roads may become safer for motorists and pedestrians through a new, innovative artificial intelligence (<abbr>AI<\/abbr>) tech project by the University of <span aria-label=\"Hawaii\">Âé¶¹´«Ã½<\/span> at M\u0101noa <a href=\"https:\/\/www.eng.hawaii.edu\/\">College of Engineering<\/a>.<\/p>\n<p>The project, led by <a href=\"http:\/\/www.cee.hawaii.edu\/\">Department of Civil, Environmental and Construction Engineering<\/a> Professor Guohui Zhang, earned a $100,000 award from the U.S. Department of Transportation (<abbr>DOT<\/abbr>). Zhang\u2019s proposed design concept, &ldquo;Toward Vision Zero: Sensing, Predicting, and Preventing Intersection Collisions,&rdquo; was one of 15 winners among 120 entries in the <a href=\"https:\/\/its.dot.gov\/isc\/\">U.S. <abbr>DOT<\/abbr> Intersection Safety Challenge<\/a>.<\/p>\n<h2>Creating an <abbr>AI<\/abbr> traffic safety net<\/h2>\n<p>The challenge encourages new technologies to improve intersection safety by addressing risks for vehicles, pedestrians, bicyclists and other road users.<\/p>\n<p>Zhang\u2019s project aims to create a safety system for intersections using advanced technologies such as sensors, edge computing, machine learning and wireless communication. The system will focus on preventing crashes between vehicles and pedestrians. Artificial intelligence will be used to identify and track objects such as pedestrians and vehicles in real-time. By predicting collisions and issuing warnings, the system aims to improve intersection safety and reduce costs. Additionally, the technology may have positive effects on traffic systems, including reducing congestion and emissions.<\/p>\n<p>&ldquo;We are truly honored to receive this exceptionally competitive award, resulting from the hard work and collaboration between <abbr title=\"University of Hawaii\">UH<\/abbr>, <span aria-label=\"Hawaii\">Âé¶¹´«Ã½<\/span> Department of Transportation (<abbr>HDOT<\/abbr>), the Pacific International Center for High Technology Research, and our industry partner, <abbr>NEC<\/abbr> Corporation of America,&rdquo; Zhang said. &ldquo;We also appreciate all the support and leadership from Edwin Sniffen and Robin Shishido at <abbr>HDOT<\/abbr> and Brennon Morioka at the <abbr>UH<\/abbr> College of Engineering for their guidance and contributions. This kind of project will go a long way in helping <span aria-label=\"Hawaii\">Âé¶¹´«Ã½<\/span> in its efforts towards reducing accidents and saving lives on <span aria-label=\"Hawaii\">Âé¶¹´«Ã½<\/span>\u2019s roads by utilizing today&#8217;s cutting-edge innovation and technology.&rdquo;<\/p>\n<p>Zhang\u2019s project will now advance into the next phase of the challenge, where teams are expected to develop, train and improve algorithms for the detection, localization and classification of vulnerable road users and vehicles using U.S. <abbr>DOT<\/abbr>-supplied sensor data collected at a controlled test roadway intersection.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>The project will advance into the second phase of the U.S. Department of Transportation 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