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DC Field Value Language Son, Gukjin - Kim, Junkwang - Song, Chanho - Kim, Young-Duk - Kim, HeeKang - 2019-05-16T02:46:04Z - 2019-05-16T02:46:04Z - 2018-11-22 - 2018-07-10 -
dc.identifier.isbn 9781538674475 -
dc.identifier.uri -
dc.description.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. © 2018 IEEE. -
dc.language English -
dc.publisher Institute of Electrical and Electronics Engineers Inc. -
dc.title A Method to Distinguish between Congestion and Vehicle Accident Using Traffic Radar -
dc.type Conference Paper -
dc.identifier.doi 10.1109/IIAI-AAI.2018.00211 -
dc.identifier.scopusid 2-s2.0-85065187854 -
dc.identifier.bibliographicCitation 7th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2018, pp.982 - 983 -
dc.identifier.url -
dc.citation.conferencePlace JA -
dc.citation.conferencePlace Yonago -
dc.citation.endPage 983 -
dc.citation.startPage 982 -
dc.citation.title 7th IIAI International Congress on Advanced Applied Informatics, IIAI-AAI 2018 -
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Appears in Collections:
Division of Automotive Technology 2. Conference Papers
Convergence Research Center for Future Automotive Technology 2. Conference Papers


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