{"id":"https://openalex.org/W4412405005","doi":"https://doi.org/10.1109/cniot65435.2025.11070971","title":"Abnormal Vehicle Event Detection Based on Deep Learning","display_name":"Abnormal Vehicle Event Detection Based on Deep Learning","publication_year":2025,"publication_date":"2025-05-23","ids":{"openalex":"https://openalex.org/W4412405005","doi":"https://doi.org/10.1109/cniot65435.2025.11070971"},"language":"en","primary_location":{"id":"doi:10.1109/cniot65435.2025.11070971","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cniot65435.2025.11070971","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 6th International Conference on Computing, Networks and Internet of Things (CNIOT)","raw_type":"proceedings-article"},"type":"article","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/A5100722265","display_name":"Kun Song","orcid":"https://orcid.org/0000-0002-1564-9916"},"institutions":[{"id":"https://openalex.org/I4210114105","display_name":"Tsinghua\u2013Berkeley Shenzhen Institute","ror":"https://ror.org/02hhwwz98","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210114105","https://openalex.org/I95457486","https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Song Kun","raw_affiliation_strings":["Tsinghua Shenzhen International Graduate School,Shenzhen,China,518055"],"affiliations":[{"raw_affiliation_string":"Tsinghua Shenzhen International Graduate School,Shenzhen,China,518055","institution_ids":["https://openalex.org/I4210114105"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5118979877","display_name":"Huang Xing-Wei","orcid":null},"institutions":[{"id":"https://openalex.org/I180662265","display_name":"China Mobile (China)","ror":"https://ror.org/05gftfe97","country_code":"CN","type":"company","lineage":["https://openalex.org/I180662265"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Huang Xing-Wei","raw_affiliation_strings":["China Mobile Guangdong,Guangzhou,China,510623"],"affiliations":[{"raw_affiliation_string":"China Mobile Guangdong,Guangzhou,China,510623","institution_ids":["https://openalex.org/I180662265"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5067351861","display_name":"Jining Liu","orcid":null},"institutions":[{"id":"https://openalex.org/I180662265","display_name":"China Mobile (China)","ror":"https://ror.org/05gftfe97","country_code":"CN","type":"company","lineage":["https://openalex.org/I180662265"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Liu Ji-Ning","raw_affiliation_strings":["China Mobile Guangdong,Guangzhou,China,510623"],"affiliations":[{"raw_affiliation_string":"China Mobile Guangdong,Guangzhou,China,510623","institution_ids":["https://openalex.org/I180662265"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5112108963","display_name":"Lin Qiao","orcid":null},"institutions":[{"id":"https://openalex.org/I180662265","display_name":"China Mobile (China)","ror":"https://ror.org/05gftfe97","country_code":"CN","type":"company","lineage":["https://openalex.org/I180662265"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lin Qiao","raw_affiliation_strings":["China Mobile GBA (Greater Bay Area) Innovation Institute,Guangzhou,China,510653"],"affiliations":[{"raw_affiliation_string":"China Mobile GBA (Greater Bay Area) Innovation Institute,Guangzhou,China,510653","institution_ids":["https://openalex.org/I180662265"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5118979878","display_name":"Chen Jin-Xuan","orcid":null},"institutions":[{"id":"https://openalex.org/I180662265","display_name":"China Mobile (China)","ror":"https://ror.org/05gftfe97","country_code":"CN","type":"company","lineage":["https://openalex.org/I180662265"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chen Jin-Xuan","raw_affiliation_strings":["China Mobile GBA (Greater Bay Area) Innovation Institute,Guangzhou,China,510653"],"affiliations":[{"raw_affiliation_string":"China Mobile GBA (Greater Bay Area) Innovation Institute,Guangzhou,China,510653","institution_ids":["https://openalex.org/I180662265"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5100722265"],"corresponding_institution_ids":["https://openalex.org/I4210114105"],"apc_list":null,"apc_paid":null,"fwci":2.8599,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.91827146,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"7"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9598000049591064,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10400","display_name":"Network Security and Intrusion Detection","score":0.9448000192642212,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6777111291885376},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4996933937072754},{"id":"https://openalex.org/keywords/event","display_name":"Event (particle physics)","score":0.4961629807949066},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.48790833353996277}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6777111291885376},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4996933937072754},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.4961629807949066},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.48790833353996277},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/cniot65435.2025.11070971","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cniot65435.2025.11070971","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 6th International Conference on Computing, Networks and Internet of Things (CNIOT)","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":21,"referenced_works":["https://openalex.org/W2092284929","https://openalex.org/W2559927751","https://openalex.org/W2579718262","https://openalex.org/W2753526808","https://openalex.org/W2773534256","https://openalex.org/W2777342313","https://openalex.org/W2921491036","https://openalex.org/W2921906393","https://openalex.org/W2962791923","https://openalex.org/W2963240734","https://openalex.org/W2963610939","https://openalex.org/W2963795951","https://openalex.org/W2964032056","https://openalex.org/W2964232409","https://openalex.org/W2964331599","https://openalex.org/W2981741013","https://openalex.org/W3015832418","https://openalex.org/W3109715690","https://openalex.org/W3174468209","https://openalex.org/W3174628227","https://openalex.org/W6730548322"],"related_works":["https://openalex.org/W2731899572","https://openalex.org/W3215138031","https://openalex.org/W3009238340","https://openalex.org/W4360585206","https://openalex.org/W4321369474","https://openalex.org/W4285208911","https://openalex.org/W3082895349","https://openalex.org/W4213079790","https://openalex.org/W2248239756","https://openalex.org/W3086377361"],"abstract_inverted_index":{"The":[0],"detection":[1,34,96,120,147],"of":[2,76,115,122,127,152],"abnormal":[3],"vehicle":[4,32,173],"events":[5,105],"is":[6,70],"critical":[7,153],"for":[8,30,170],"enabling":[9,149],"intelligent":[10],"transportation":[11],"systems,":[12],"yet":[13],"traditional":[14,130],"methods":[15],"often":[16],"suffer":[17],"from":[18,85],"missed":[19],"or":[20,161],"false":[21],"detections.":[22],"This":[23,163],"paper":[24],"proposes":[25],"a":[26,73,119,166],"deep":[27],"learning-based":[28],"framework":[29],"highway":[31,78],"anomaly":[33,95],"using":[35],"fused":[36],"LiDAR":[37,44,99],"and":[38,48,62,100,124],"visual":[39],"camera":[40,101],"data.":[41],"By":[42],"leveraging":[43],"to":[45,81,103,144],"capture":[46],"target":[47],"lane-line":[49],"information,":[50],"we":[51],"construct":[52],"spatiotemporal":[53,87],"input":[54],"features":[55],"that":[56],"encode":[57],"dynamic":[58],"interactions":[59],"between":[60],"vehicles":[61],"road":[63],"structures.":[64],"A":[65],"fully":[66],"connected":[67],"neural":[68],"network":[69],"trained":[71],"on":[72],"curated":[74],"dataset":[75],"real-world":[77],"sensor":[79],"data":[80,102],"extract":[82],"multidimensional":[83],"patterns":[84],"these":[86],"sequences.":[88],"After":[89],"training,":[90],"the":[91,113],"model":[92],"fuses":[93],"temporal":[94],"results":[97,111],"with":[98],"validate":[104],"against":[106],"corresponding":[107],"video":[108],"frames.":[109],"Experimental":[110],"demonstrate":[112],"effectiveness":[114],"our":[116,136],"method,":[117],"achieving":[118],"rate":[121],"97.11%":[123],"an":[125],"accuracy":[126],"97.10%,":[128],"outperforming":[129],"algorithms":[131],"in":[132],"both":[133],"metrics.":[134],"Additionally,":[135],"approach":[137],"reduces":[138],"computational":[139],"time":[140],"by":[141],"30%":[142],"compared":[143],"conventional":[145],"event":[146],"methods,":[148],"faster":[150],"identification":[151],"traffic":[154],"anomalies":[155],"such":[156],"as":[157],"sudden":[158],"lane":[159],"deviations":[160],"collisions.":[162],"work":[164],"provides":[165],"robust,":[167],"data-driven":[168],"solution":[169],"enhancing":[171],"real-time":[172],"surveillance":[174],"systems.":[175]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}