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Felipe Santos vecchietti, Luiz
데이터 사이언스 그룹
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Sensing Accident-Prone Features in Urban Scenes for Proactive Driving and Accident Prevention

DC Field Value Language
dc.contributor.authorMishra, Sumit-
dc.contributor.authorRajendran, Praveen Kumar-
dc.contributor.authorLuiz Felipe Vecchietti-
dc.contributor.authorHar, Dongsoo-
dc.date.accessioned2023-07-31T22:00:28Z-
dc.date.available2023-07-31T22:00:28Z-
dc.date.created2023-05-30-
dc.date.issued2023-05-
dc.identifier.issn1524-9050-
dc.identifier.urihttps://pr.ibs.re.kr/handle/8788114/13673-
dc.description.abstractIn urban cities, visual information on and along roadways is likely to distract drivers and lead to missing traffic signs and other accident-prone (AP) features. To avoid accidents due to missing these visual cues, this paper proposes a visual notification of AP-features to drivers based on real-time images obtained via dashcam. For this purpose, Google Street View images around accident hotspots (areas of dense accident occurrence) identified by a real-accident dataset are used to train a novel attention module to classify a given urban scene into an accident hotspot or a non-hotspot (area of sparse accident occurrence). The proposed module leverages channel, point, and spatial-wise attention learning on top of different CNN backbones. This leads to better classification results and more certain AP-features with better contextual knowledge when compared with CNN backbones alone. Our proposed module achieves up to 92% classification accuracy. The capability of detecting AP-features by the proposed model were analyzed by a comparative study of three different class activation map (CAM) methods, which are used to inspect specific AP-features causing the classification decision. The outputs of the CAM methods were processed by an image processing pipeline to extract only the AP-features that are explainable to drivers and notified using a visual notification system. Range of experiments was performed to prove the efficacy and AP-features of the system. Ablation of the AP-features taking 9.61%, on average, of the total area in each image sample increased the chance of a given area to be classified as a non-hotspot by up to 21.8%.-
dc.language영어-
dc.publisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC-
dc.titleSensing Accident-Prone Features in Urban Scenes for Proactive Driving and Accident Prevention-
dc.typeArticle-
dc.type.rimsART-
dc.identifier.wosid000988381800001-
dc.identifier.scopusid2-s2.0-85159793289-
dc.identifier.rimsid80854-
dc.contributor.affiliatedAuthorLuiz Felipe Vecchietti-
dc.identifier.doi10.1109/TITS.2023.3271395-
dc.identifier.bibliographicCitationIEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, v.24, no.9, pp.9401 - 9414-
dc.relation.isPartOfIEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS-
dc.citation.titleIEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS-
dc.citation.volume24-
dc.citation.number9-
dc.citation.startPage9401-
dc.citation.endPage9414-
dc.description.journalClass1-
dc.description.journalClass1-
dc.description.isOpenAccessN-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.relation.journalWebOfScienceCategoryEngineering, Civil-
dc.relation.journalWebOfScienceCategoryEngineering, Electrical & Electronic-
dc.relation.journalWebOfScienceCategoryTransportation Science & Technology-
dc.subject.keywordPlusROAD-
dc.subject.keywordPlusDISTRACTION-
dc.subject.keywordPlusCOMPLEXITY-
dc.subject.keywordPlusDISPLAY-
dc.subject.keywordPlusSYSTEMS-
dc.subject.keywordPlusIMAGERY-
dc.subject.keywordAuthorConvolutional neural networks-
dc.subject.keywordAuthorAccident prevention-
dc.subject.keywordAuthorhead-up display-
dc.subject.keywordAuthorattentive driving system-
dc.subject.keywordAuthoraccident prone feature-
dc.subject.keywordAuthoraccident hotspot-
dc.subject.keywordAuthorstreet view images-
dc.subject.keywordAuthorAccidents-
dc.subject.keywordAuthorFeature extraction-
dc.subject.keywordAuthorVehicles-
dc.subject.keywordAuthorVisualization-
dc.subject.keywordAuthorRoads-
dc.subject.keywordAuthorReal-time systems-
Appears in Collections:
Pioneer Research Center for Mathematical and Computational Sciences(수리 및 계산과학 연구단) > Data Science Group(데이터 사이언스 그룹) > 1. Journal Papers (저널논문)
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