Guk-Jin Son, Junkwang Kim, Chan-Ho Song, Hee-Earn Kim, Youngduk Kim
{"title":"A Method to Distinguish between Congestion and Vehicle Accident Using Traffic Radar","authors":"Guk-Jin Son, Junkwang Kim, Chan-Ho Song, Hee-Earn Kim, Youngduk Kim","doi":"10.1109/IIAI-AAI.2018.00211","DOIUrl":null,"url":null,"abstract":"Individual radars measure range, radial speed, angle, reflectivity, and other parameters of multiple stationary and moving reflectors simultaneously. The measured signal is excellent for acquiring vehicle information on the road. Several studies have proposed identifying accident vehicles on the road by processing radar information. However, it is difficult to identify accident vehicles using a radar information in congested traffic since traffic radar information for accidents are almost the same as those for traffic jams. This study proposes a method to identifying accident vehicles based on an accident decision algorithm that distinguishes between congested and accident vehicles. Experimental results verified that the proposed method's detection accuracy exceeded 95%. We believe the proposed method will enhance future intelligent transportation systems.","PeriodicalId":309975,"journal":{"name":"2018 7th International Congress on Advanced Applied Informatics (IIAI-AAI)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 7th International Congress on Advanced Applied Informatics (IIAI-AAI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IIAI-AAI.2018.00211","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
Individual radars measure range, radial speed, angle, reflectivity, and other parameters of multiple stationary and moving reflectors simultaneously. The measured signal is excellent for acquiring vehicle information on the road. Several studies have proposed identifying accident vehicles on the road by processing radar information. However, it is difficult to identify accident vehicles using a radar information in congested traffic since traffic radar information for accidents are almost the same as those for traffic jams. This study proposes a method to identifying accident vehicles based on an accident decision algorithm that distinguishes between congested and accident vehicles. Experimental results verified that the proposed method's detection accuracy exceeded 95%. We believe the proposed method will enhance future intelligent transportation systems.