{"id":"https://openalex.org/W4294672675","doi":"https://doi.org/10.1109/rcar54675.2022.9872218","title":"A Data Annotation and Recognition Method Based on Zero Statistical Hypothesis Test and Multi Variable Binary Classification Theory","display_name":"A Data Annotation and Recognition Method Based on Zero Statistical Hypothesis Test and Multi Variable Binary Classification Theory","publication_year":2022,"publication_date":"2022-07-17","ids":{"openalex":"https://openalex.org/W4294672675","doi":"https://doi.org/10.1109/rcar54675.2022.9872218"},"language":"en","primary_location":{"id":"doi:10.1109/rcar54675.2022.9872218","is_oa":false,"landing_page_url":"https://doi.org/10.1109/rcar54675.2022.9872218","pdf_url":null,"source":{"id":"https://openalex.org/S4363608312","display_name":"2022 IEEE International Conference on Real-time Computing and Robotics (RCAR)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE International Conference on Real-time Computing and Robotics (RCAR)","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/A5100386001","display_name":"Ying Zhang","orcid":"https://orcid.org/0000-0001-5246-2141"},"institutions":[{"id":"https://openalex.org/I4210094894","display_name":"China Automotive Technology and Research Center","ror":"https://ror.org/00r5r6807","country_code":"CN","type":"other","lineage":["https://openalex.org/I4210094894"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Zhang Ying","raw_affiliation_strings":["CATARC Intelligent Connected Technology (Tianjin) Co., Ltd"],"affiliations":[{"raw_affiliation_string":"CATARC Intelligent Connected Technology (Tianjin) Co., Ltd","institution_ids":["https://openalex.org/I4210094894"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5061826340","display_name":"Chen Chao","orcid":"https://orcid.org/0000-0002-6385-6282"},"institutions":[{"id":"https://openalex.org/I4210094894","display_name":"China Automotive Technology and Research Center","ror":"https://ror.org/00r5r6807","country_code":"CN","type":"other","lineage":["https://openalex.org/I4210094894"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chen Chao","raw_affiliation_strings":["Automotive Data of China (Tianjin) Co., Ltd"],"affiliations":[{"raw_affiliation_string":"Automotive Data of China (Tianjin) Co., Ltd","institution_ids":["https://openalex.org/I4210094894"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5065009375","display_name":"Lei Wang","orcid":"https://orcid.org/0000-0001-5614-4378"},"institutions":[{"id":"https://openalex.org/I4210094894","display_name":"China Automotive Technology and Research Center","ror":"https://ror.org/00r5r6807","country_code":"CN","type":"other","lineage":["https://openalex.org/I4210094894"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wang Lei","raw_affiliation_strings":["Automotive Data of China (Tianjin) Co., Ltd"],"affiliations":[{"raw_affiliation_string":"Automotive Data of China (Tianjin) Co., Ltd","institution_ids":["https://openalex.org/I4210094894"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Zhao Shuai","orcid":null},"institutions":[{"id":"https://openalex.org/I4210094894","display_name":"China Automotive Technology and Research Center","ror":"https://ror.org/00r5r6807","country_code":"CN","type":"other","lineage":["https://openalex.org/I4210094894"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhao Shuai","raw_affiliation_strings":["Tianjin university,Automotive Data of China (Tianjin) Co., Ltd","Automotive Data of China (Tianjin) Co., Ltd, Tianjin university"],"affiliations":[{"raw_affiliation_string":"Tianjin university,Automotive Data of China (Tianjin) Co., Ltd","institution_ids":["https://openalex.org/I4210094894"]},{"raw_affiliation_string":"Automotive Data of China (Tianjin) Co., Ltd, Tianjin university","institution_ids":["https://openalex.org/I4210094894"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5104061405","display_name":"Xianglei Zhu","orcid":null},"institutions":[{"id":"https://openalex.org/I4210094894","display_name":"China Automotive Technology and Research Center","ror":"https://ror.org/00r5r6807","country_code":"CN","type":"other","lineage":["https://openalex.org/I4210094894"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhu XiangLei","raw_affiliation_strings":["Tianjin university,China Automotive Technology and Research Center Co., Ltd","China Automotive Technology and Research Center Co., Ltd, Tianjin university"],"affiliations":[{"raw_affiliation_string":"Tianjin university,China Automotive Technology and Research Center Co., Ltd","institution_ids":["https://openalex.org/I4210094894"]},{"raw_affiliation_string":"China Automotive Technology and Research Center Co., Ltd, Tianjin university","institution_ids":["https://openalex.org/I4210094894"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5100386001"],"corresponding_institution_ids":["https://openalex.org/I4210094894"],"apc_list":null,"apc_paid":null,"fwci":0.3355,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.44390244,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"676","last_page":"680"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9968000054359436,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"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/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9968000054359436,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.9650999903678894,"subfield":{"id":"https://openalex.org/subfields/3313","display_name":"Transportation"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10761","display_name":"Vehicular Ad Hoc Networks (VANETs)","score":0.9427000284194946,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"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":0.6756690740585327},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6530028581619263},{"id":"https://openalex.org/keywords/binary-classification","display_name":"Binary classification","score":0.5937576293945312},{"id":"https://openalex.org/keywords/decision-tree","display_name":"Decision tree","score":0.5713049173355103},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5400770902633667},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.4829089641571045},{"id":"https://openalex.org/keywords/statistical-hypothesis-testing","display_name":"Statistical hypothesis testing","score":0.473762571811676},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.46849972009658813},{"id":"https://openalex.org/keywords/gradient-boosting","display_name":"Gradient boosting","score":0.451627641916275},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.42210400104522705},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.4212713837623596},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4020213484764099},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.3534236252307892},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.18946120142936707},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.14870685338974}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6756690740585327},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6530028581619263},{"id":"https://openalex.org/C66905080","wikidata":"https://www.wikidata.org/wiki/Q17005494","display_name":"Binary classification","level":3,"score":0.5937576293945312},{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.5713049173355103},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5400770902633667},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.4829089641571045},{"id":"https://openalex.org/C87007009","wikidata":"https://www.wikidata.org/wiki/Q210832","display_name":"Statistical hypothesis testing","level":2,"score":0.473762571811676},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.46849972009658813},{"id":"https://openalex.org/C70153297","wikidata":"https://www.wikidata.org/wiki/Q5591907","display_name":"Gradient boosting","level":3,"score":0.451627641916275},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.42210400104522705},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.4212713837623596},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4020213484764099},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.3534236252307892},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.18946120142936707},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.14870685338974}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/rcar54675.2022.9872218","is_oa":false,"landing_page_url":"https://doi.org/10.1109/rcar54675.2022.9872218","pdf_url":null,"source":{"id":"https://openalex.org/S4363608312","display_name":"2022 IEEE International Conference on Real-time Computing and Robotics (RCAR)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 IEEE International Conference on Real-time Computing and Robotics (RCAR)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10","score":0.6399999856948853}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":11,"referenced_works":["https://openalex.org/W1501399328","https://openalex.org/W1994554107","https://openalex.org/W2584223592","https://openalex.org/W2588394913","https://openalex.org/W2891268846","https://openalex.org/W2896417303","https://openalex.org/W2929446956","https://openalex.org/W3102287942","https://openalex.org/W4289305635","https://openalex.org/W6630052823","https://openalex.org/W6755844547"],"related_works":["https://openalex.org/W4288057626","https://openalex.org/W3200719183","https://openalex.org/W3201348321","https://openalex.org/W4293069612","https://openalex.org/W4292373754","https://openalex.org/W4249229055","https://openalex.org/W4200057378","https://openalex.org/W4308654587","https://openalex.org/W3080602699","https://openalex.org/W4280489286"],"abstract_inverted_index":{"Based":[0],"on":[1,96,142,153,163,200],"typical":[2,47],"Chinese":[3],"natural":[4,8],"driving":[5,9,243],"data,":[6],"from":[7],"scenario":[10,14,24,122],"data":[11,42,123],"collection":[12],"to":[13,180],"automatic":[15,25],"labeling":[16,26],"and":[17,27,34,54,56,60,133,157,223,229],"classification,":[18],"this":[19,230],"paper":[20],"proposed":[21],"a":[22],"specific":[23],"classification":[28,136,150,183,195,215,240],"method":[29,94,137,151,161],"by":[30,89,102],"using":[31,90,130],"statistical":[32,72,78,112],"tools":[33],"machine":[35,139],"learning":[36,140,149,160],"methods.":[37],"The":[38],"front":[39,83,119,187,219,225],"vehicle":[40,84,120,188,220,226],"cut-in":[41,85,189,221,227],"of":[43,74,81,99,107,117,138,185,217,241],"more":[44],"than":[45],"4000":[46],"road":[48],"scenarios":[49],"in":[50,237,245],"China":[51],"are":[52,62,177],"collected":[53],"extracted,":[55],"the":[57,66,71,75,77,91,97,104,111,115,118,127,131,147,158,173,182,186,193,197,201,205,209,214,238,246],"parametric":[58],"statistics":[59],"analysis":[61],"carried":[63],"out":[64],"for":[65],"relevant":[67],"6":[68],"variables.":[69],"Considering":[70],"uncertainty":[73],"variables,":[76],"exclusion":[79,113],"curve":[80,106],"\u201cnormal":[82,224],"scenario\u201d":[86,222],"is":[87,124],"calculated":[88],"hypothesis":[92],"test":[93,198],"based":[95,141,152,162],"principle":[98],"mathematical":[100],"statistics,":[101],"comparing":[103,192],"distribution":[105],"any":[108],"event":[109],"with":[110],"curve,":[114],"annotation":[116],"entry":[121],"realized.":[125],"At":[126],"same":[128],"time,":[129],"positive":[132],"negative":[134],"sample":[135],"bagging":[143],"decision":[144,155],"tree":[145],"classifier,":[146],"integrated":[148],"boosting":[154],"tree,":[156],"depth":[159],"improved":[164],"resnet-18":[165],"convolution":[166],"Network":[167],"+":[168],"LSTM":[169],"recurrent":[170],"neural":[171],"network,":[172],"multi-variable":[174],"binary":[175],"classifiers":[176],"trained":[178],"respectively":[179],"realize":[181],"task":[184],"scenario.":[190],"Furthermore,":[191],"three":[194],"methods,":[196],"results":[199],"verification":[202],"show":[203],"that":[204],"BDT":[206],"classifier":[207],"has":[208],"best":[210],"result,":[211],"effectively":[212],"realizes":[213],"tasks":[216],"\u201cdangerous":[218],"scenario\u201d,":[228],"technical":[231],"tool":[232],"chain":[233],"can":[234],"be":[235],"reused":[236],"fine-grained":[239],"other":[242],"scenes":[244],"future":[247]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2026-04-16T08:26:57.006410","created_date":"2025-10-10T00:00:00"}
