{"id":"https://openalex.org/W4413010317","doi":"https://doi.org/10.1109/iv64158.2025.11097800","title":"Intersection Safety Modeling Using Semantic Scene Graph and Graph Neural Network","display_name":"Intersection Safety Modeling Using Semantic Scene Graph and Graph Neural Network","publication_year":2025,"publication_date":"2025-06-22","ids":{"openalex":"https://openalex.org/W4413010317","doi":"https://doi.org/10.1109/iv64158.2025.11097800"},"language":"en","primary_location":{"id":"doi:10.1109/iv64158.2025.11097800","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iv64158.2025.11097800","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE Intelligent Vehicles Symposium (IV)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5039625631","display_name":"Akash Sonth","orcid":null},"institutions":[{"id":"https://openalex.org/I859038795","display_name":"Virginia Tech","ror":"https://ror.org/02smfhw86","country_code":"US","type":"education","lineage":["https://openalex.org/I859038795"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Akash Sonth","raw_affiliation_strings":["Virginia Tech,Bradley Department of Electrical and Computer Engineering,Blacksburg,VA,24061"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Virginia Tech,Bradley Department of Electrical and Computer Engineering,Blacksburg,VA,24061","institution_ids":["https://openalex.org/I859038795"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5031293008","display_name":"A. Lynn Abbott","orcid":null},"institutions":[{"id":"https://openalex.org/I859038795","display_name":"Virginia Tech","ror":"https://ror.org/02smfhw86","country_code":"US","type":"education","lineage":["https://openalex.org/I859038795"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"A. Lynn Abbott","raw_affiliation_strings":["Virginia Tech,Bradley Department of Electrical and Computer Engineering,Blacksburg,VA,24061"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Virginia Tech,Bradley Department of Electrical and Computer Engineering,Blacksburg,VA,24061","institution_ids":["https://openalex.org/I859038795"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5003946047","display_name":"Abhijit Sarkar","orcid":"https://orcid.org/0000-0002-7699-9741"},"institutions":[{"id":"https://openalex.org/I859038795","display_name":"Virginia Tech","ror":"https://ror.org/02smfhw86","country_code":"US","type":"education","lineage":["https://openalex.org/I859038795"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Abhijit Sarkar","raw_affiliation_strings":["Virginia Tech,Bradley Department of Electrical and Computer Engineering,Blacksburg,VA,24061"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Virginia Tech,Bradley Department of Electrical and Computer Engineering,Blacksburg,VA,24061","institution_ids":["https://openalex.org/I859038795"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I859038795"],"apc_list":null,"apc_paid":null,"fwci":3.6912,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.90952273,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"569","last_page":"576"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13295","display_name":"Safety Systems Engineering in Autonomy","score":0.902400016784668,"subfield":{"id":"https://openalex.org/subfields/2213","display_name":"Safety, Risk, Reliability and Quality"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T13295","display_name":"Safety Systems Engineering in Autonomy","score":0.902400016784668,"subfield":{"id":"https://openalex.org/subfields/2213","display_name":"Safety, Risk, Reliability and Quality"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7446539402008057},{"id":"https://openalex.org/keywords/intersection-graph","display_name":"Intersection graph","score":0.6291519999504089},{"id":"https://openalex.org/keywords/intersection","display_name":"Intersection (aeronautics)","score":0.517435610294342},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5007171630859375},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.43665581941604614},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.41508615016937256},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.28047144412994385},{"id":"https://openalex.org/keywords/line-graph","display_name":"Line graph","score":0.07738626003265381},{"id":"https://openalex.org/keywords/transport-engineering","display_name":"Transport engineering","score":0.0761619508266449},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.07609769701957703}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7446539402008057},{"id":"https://openalex.org/C54540088","wikidata":"https://www.wikidata.org/wiki/Q3498041","display_name":"Intersection graph","level":4,"score":0.6291519999504089},{"id":"https://openalex.org/C64543145","wikidata":"https://www.wikidata.org/wiki/Q162942","display_name":"Intersection (aeronautics)","level":2,"score":0.517435610294342},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5007171630859375},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.43665581941604614},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.41508615016937256},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.28047144412994385},{"id":"https://openalex.org/C203776342","wikidata":"https://www.wikidata.org/wiki/Q1378376","display_name":"Line graph","level":3,"score":0.07738626003265381},{"id":"https://openalex.org/C22212356","wikidata":"https://www.wikidata.org/wiki/Q775325","display_name":"Transport engineering","level":1,"score":0.0761619508266449},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.07609769701957703}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iv64158.2025.11097800","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iv64158.2025.11097800","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE Intelligent Vehicles Symposium (IV)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W1974475171","https://openalex.org/W1985757759","https://openalex.org/W1989750313","https://openalex.org/W2005560459","https://openalex.org/W2076246037","https://openalex.org/W2085261163","https://openalex.org/W2905509562","https://openalex.org/W2963150697","https://openalex.org/W2971323980","https://openalex.org/W2982310938","https://openalex.org/W3187966659","https://openalex.org/W3210909185","https://openalex.org/W3217070037","https://openalex.org/W4206485978","https://openalex.org/W4226095921","https://openalex.org/W4386076325","https://openalex.org/W6732059651","https://openalex.org/W6745308043","https://openalex.org/W6760045743","https://openalex.org/W6772452955","https://openalex.org/W6796539847","https://openalex.org/W6809893666","https://openalex.org/W6839744764"],"related_works":["https://openalex.org/W2348909947","https://openalex.org/W4292672442","https://openalex.org/W2362101859","https://openalex.org/W2791431590","https://openalex.org/W2941610985","https://openalex.org/W4235810826","https://openalex.org/W3000105423","https://openalex.org/W2350688482","https://openalex.org/W2953260685","https://openalex.org/W2362473619"],"abstract_inverted_index":{"Traffic":[0],"intersections":[1],"are":[2,85],"critical":[3],"zones":[4],"where":[5,91],"vehicle":[6],"and":[7,23,40,69,83,100,130],"pedestrian":[8],"interactions":[9,68,95],"significantly":[10],"impact":[11],"road":[12,41,70],"safety.":[13],"This":[14,140],"study":[15],"presents":[16],"a":[17,44,73,142],"novel":[18],"graph-based":[19],"approach":[20],"to":[21,65,87],"model":[22],"analyze":[24],"intersection":[25,57,107,147],"traffic":[26,38],"dynamics,":[27],"leveraging":[28],"Graph":[29],"Neural":[30],"Networks":[31],"(GNNs)":[32],"for":[33,145],"risk":[34,77],"assessment.":[35],"By":[36],"representing":[37],"participants":[39],"infrastructure":[42],"as":[43],"structured":[45,103],"graph,":[46],"we":[47,60],"capture":[48],"spatial-temporal":[49],"relationships":[50],"that":[51,112],"influence":[52],"crash":[53],"likelihood.":[54],"Using":[55],"real-world":[56],"video":[58],"data,":[59],"construct":[61],"semantic":[62],"scene":[63],"graphs":[64],"encode":[66],"actor":[67],"topology,":[71],"enabling":[72],"data-driven":[74],"understanding":[75],"of":[76,122],"factors.":[78],"Two":[79],"GNN":[80],"models,":[81],"TransformerConv":[82,92],"GINEConv,":[84],"employed":[86],"assess":[88],"safety":[89,135,148],"risks,":[90],"captures":[93],"dynamic":[94],"through":[96],"adaptive":[97],"attention":[98],"weighting,":[99],"GINEConv":[101],"models":[102],"dependencies":[104],"within":[105],"the":[106],"network.":[108],"Our":[109],"findings":[110],"demonstrate":[111],"this":[113],"framework":[114],"can":[115],"effectively":[116],"classify":[117],"high-risk":[118],"scenarios,":[119],"threat":[120],"assessment":[121],"each":[123],"actors":[124],"(node),":[125],"characterize":[126],"their":[127],"interaction":[128],"(edge),":[129],"provide":[131],"near":[132],"real":[133],"time":[134],"analysis":[136],"with":[137],"79.8%":[138],"accuracy.":[139],"provides":[141],"scalable":[143],"method":[144],"proactive":[146],"monitoring.":[149]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}