{"id":"https://openalex.org/W4414241947","doi":"https://doi.org/10.1145/3767737","title":"Early Traffic Accident Anticipation via Feature Consistency Representation and Soft Label Regression","display_name":"Early Traffic Accident Anticipation via Feature Consistency Representation and Soft Label Regression","publication_year":2025,"publication_date":"2025-09-16","ids":{"openalex":"https://openalex.org/W4414241947","doi":"https://doi.org/10.1145/3767737"},"language":"en","primary_location":{"id":"doi:10.1145/3767737","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3767737","pdf_url":null,"source":{"id":"https://openalex.org/S19610489","display_name":"ACM Transactions on Multimedia Computing Communications and Applications","issn_l":"1551-6857","issn":["1551-6857","1551-6865"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Multimedia Computing, Communications, and Applications","raw_type":"journal-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/A5030673454","display_name":"Yuanhong Zhong","orcid":"https://orcid.org/0000-0001-5689-1146"},"institutions":[{"id":"https://openalex.org/I158842170","display_name":"Chongqing University","ror":"https://ror.org/023rhb549","country_code":"CN","type":"education","lineage":["https://openalex.org/I158842170"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuanhong Zhong","raw_affiliation_strings":["School of Microelectronics and Communication Engineering, Chongqing University, Chongqing, China","School of Microelectronics and Communication Engineering, Chongqing University, China"],"raw_orcid":"https://orcid.org/0000-0001-5689-1146","affiliations":[{"raw_affiliation_string":"School of Microelectronics and Communication Engineering, Chongqing University, Chongqing, China","institution_ids":["https://openalex.org/I158842170"]},{"raw_affiliation_string":"School of Microelectronics and Communication Engineering, Chongqing University, China","institution_ids":["https://openalex.org/I158842170"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103821751","display_name":"Ge Yan","orcid":"https://orcid.org/0009-0009-4664-688X"},"institutions":[{"id":"https://openalex.org/I158842170","display_name":"Chongqing University","ror":"https://ror.org/023rhb549","country_code":"CN","type":"education","lineage":["https://openalex.org/I158842170"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ge Yan","raw_affiliation_strings":["School of Microelectronics and Communication Engineering, Chongqing University, Chongqing, China","School of Microelectronics and Communication Engineering, Chongqing University, China"],"raw_orcid":"https://orcid.org/0009-0009-4664-688X","affiliations":[{"raw_affiliation_string":"School of Microelectronics and Communication Engineering, Chongqing University, Chongqing, China","institution_ids":["https://openalex.org/I158842170"]},{"raw_affiliation_string":"School of Microelectronics and Communication Engineering, Chongqing University, China","institution_ids":["https://openalex.org/I158842170"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5002664651","display_name":"Ran Zhu","orcid":"https://orcid.org/0009-0009-7936-3851"},"institutions":[{"id":"https://openalex.org/I158842170","display_name":"Chongqing University","ror":"https://ror.org/023rhb549","country_code":"CN","type":"education","lineage":["https://openalex.org/I158842170"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ruyue Zhu","raw_affiliation_strings":["School of Microelectronics and Communication Engineering, Chongqing University, Chongqing, China","School of Microelectronics and Communication Engineering, Chongqing University, China"],"raw_orcid":"https://orcid.org/0009-0009-7936-3851","affiliations":[{"raw_affiliation_string":"School of Microelectronics and Communication Engineering, Chongqing University, Chongqing, China","institution_ids":["https://openalex.org/I158842170"]},{"raw_affiliation_string":"School of Microelectronics and Communication Engineering, Chongqing University, China","institution_ids":["https://openalex.org/I158842170"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5104593905","display_name":"Ping Gan","orcid":null},"institutions":[{"id":"https://openalex.org/I158842170","display_name":"Chongqing University","ror":"https://ror.org/023rhb549","country_code":"CN","type":"education","lineage":["https://openalex.org/I158842170"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ping Gan","raw_affiliation_strings":["School of Microelectronics and Communication Engineering, Chongqing University, Chongqing, China","School of Microelectronics and Communication Engineering, Chongqing University, China"],"raw_orcid":"https://orcid.org/0009-0002-1320-9615","affiliations":[{"raw_affiliation_string":"School of Microelectronics and Communication Engineering, Chongqing University, Chongqing, China","institution_ids":["https://openalex.org/I158842170"]},{"raw_affiliation_string":"School of Microelectronics and Communication Engineering, Chongqing University, China","institution_ids":["https://openalex.org/I158842170"]}]},{"author_position":"last","author":{"id":null,"display_name":"Xuerui Shen","orcid":"https://orcid.org/0009-0006-4702-7106"},"institutions":[{"id":"https://openalex.org/I158842170","display_name":"Chongqing University","ror":"https://ror.org/023rhb549","country_code":"CN","type":"education","lineage":["https://openalex.org/I158842170"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xuerui Shen","raw_affiliation_strings":["School of Microelectronics and Communication Engineering, Chongqing University, Chongqing, China","School of Microelectronics and Communication Engineering, Chongqing University, China"],"raw_orcid":"https://orcid.org/0009-0006-4702-7106","affiliations":[{"raw_affiliation_string":"School of Microelectronics and Communication Engineering, Chongqing University, Chongqing, China","institution_ids":["https://openalex.org/I158842170"]},{"raw_affiliation_string":"School of Microelectronics and Communication Engineering, Chongqing University, China","institution_ids":["https://openalex.org/I158842170"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I158842170"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.12403356,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"21","issue":"11","first_page":"1","last_page":"21"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9987999796867371,"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":0.9987999796867371,"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/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9970999956130981,"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/T10400","display_name":"Network Security and Intrusion Detection","score":0.9803000092506409,"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/consistency","display_name":"Consistency (knowledge bases)","score":0.6715999841690063},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5861999988555908},{"id":"https://openalex.org/keywords/frame","display_name":"Frame (networking)","score":0.5590000152587891},{"id":"https://openalex.org/keywords/anticipation","display_name":"Anticipation (artificial intelligence)","score":0.4959999918937683},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.4832000136375427},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.46880000829696655},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.44269999861717224},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.4424999952316284},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.44029998779296875}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8725000023841858},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.6715999841690063},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6157000064849854},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5861999988555908},{"id":"https://openalex.org/C126042441","wikidata":"https://www.wikidata.org/wiki/Q1324888","display_name":"Frame (networking)","level":2,"score":0.5590000152587891},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5084999799728394},{"id":"https://openalex.org/C176777502","wikidata":"https://www.wikidata.org/wiki/Q4774623","display_name":"Anticipation (artificial intelligence)","level":2,"score":0.4959999918937683},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.4832000136375427},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.46880000829696655},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.44269999861717224},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.4424999952316284},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.44029998779296875},{"id":"https://openalex.org/C183469790","wikidata":"https://www.wikidata.org/wiki/Q333501","display_name":"Crash","level":2,"score":0.43650001287460327},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.4350999891757965},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4246000051498413},{"id":"https://openalex.org/C2780289543","wikidata":"https://www.wikidata.org/wiki/Q424630","display_name":"Accident (philosophy)","level":2,"score":0.41679999232292175},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.349700003862381},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.3481999933719635},{"id":"https://openalex.org/C43126263","wikidata":"https://www.wikidata.org/wiki/Q128751","display_name":"Source code","level":2,"score":0.3278999924659729},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.30469998717308044},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.28690001368522644},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.27300000190734863},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.26989999413490295},{"id":"https://openalex.org/C3017944768","wikidata":"https://www.wikidata.org/wiki/Q1450463","display_name":"Poison control","level":2,"score":0.26809999346733093},{"id":"https://openalex.org/C51929080","wikidata":"https://www.wikidata.org/wiki/Q2425187","display_name":"Codebase","level":3,"score":0.2648000121116638},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.25609999895095825},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.2542000114917755},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.25360000133514404}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3767737","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3767737","pdf_url":null,"source":{"id":"https://openalex.org/S19610489","display_name":"ACM Transactions on Multimedia Computing Communications and Applications","issn_l":"1551-6857","issn":["1551-6857","1551-6865"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Multimedia Computing, Communications, and Applications","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G3921365549","display_name":null,"funder_award_id":"62372074","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8940484741","display_name":null,"funder_award_id":"2023CDJKYJH050","funder_id":"https://openalex.org/F4320335787","funder_display_name":"Fundamental Research Funds for the Central Universities"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320335787","display_name":"Fundamental Research Funds for the Central Universities","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":45,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W2064675550","https://openalex.org/W2116341502","https://openalex.org/W2122723498","https://openalex.org/W2600200270","https://openalex.org/W2769735038","https://openalex.org/W2796314858","https://openalex.org/W2913427224","https://openalex.org/W2963524571","https://openalex.org/W2963737762","https://openalex.org/W2964217160","https://openalex.org/W2968393007","https://openalex.org/W2989676862","https://openalex.org/W3042609801","https://openalex.org/W3046330049","https://openalex.org/W3101279734","https://openalex.org/W3101787898","https://openalex.org/W3108027406","https://openalex.org/W3118519864","https://openalex.org/W3127561923","https://openalex.org/W3159326902","https://openalex.org/W3162428900","https://openalex.org/W3176454305","https://openalex.org/W3203151276","https://openalex.org/W3208673210","https://openalex.org/W4212847381","https://openalex.org/W4213101961","https://openalex.org/W4226095921","https://openalex.org/W4226424593","https://openalex.org/W4285411154","https://openalex.org/W4306174033","https://openalex.org/W4323923566","https://openalex.org/W4324290198","https://openalex.org/W4375796415","https://openalex.org/W4376464579","https://openalex.org/W4379538705","https://openalex.org/W4386088285","https://openalex.org/W4387735499","https://openalex.org/W4387967270","https://openalex.org/W4389252877","https://openalex.org/W4391407195","https://openalex.org/W4394597592","https://openalex.org/W4396671396","https://openalex.org/W4403780635","https://openalex.org/W4405894078"],"related_works":[],"abstract_inverted_index":{"Early":[0],"traffic":[1,26],"accident":[2,87,152,218],"prediction":[3,88,153,219],"using":[4,164],"dashcam":[5],"videos":[6],"plays":[7],"a":[8,91,181],"crucial":[9],"role":[10],"in":[11,21],"enhancing":[12],"the":[13,50,53,58,62,84,97,108,137,141,151,170,175],"safety":[14],"of":[15,86,100,140,177],"intelligent":[16],"vehicles.":[17],"Accurately":[18],"predicting":[19],"accidents":[20,27],"advance":[22],"can":[23],"significantly":[24],"reduce":[25],"and":[28,61,89,124,184,200],"improve":[29],"overall":[30,145],"road":[31],"safety.":[32],"However,":[33],"despite":[34],"extensive":[35],"research":[36],"efforts":[37],"to":[38,120,172,206],"capture":[39],"more":[40,182],"visual":[41],"information":[42],"by":[43,155],"employing":[44],"different":[45,67],"feature":[46,93],"extraction":[47],"methods":[48],"within":[49,57],"same":[51,59,109],"frame,":[52],"consistency":[54],"between":[55,64],"features":[56,65,106,125],"frame":[60,110],"discrepancy":[63],"across":[66],"frames":[68,115,129],"have":[69],"not":[70],"been":[71],"sufficiently":[72],"emphasized.":[73],"To":[74],"address":[75],"this":[76],"critical":[77],"issue,":[78],"we":[79,149],"introduce":[80],"contrastive":[81],"learning":[82],"into":[83,158],"field":[85],"propose":[90],"novel":[92],"fusion":[94],"module":[95],"for":[96,224],"deep":[98],"integration":[99],"diverse":[101],"features.":[102],"Our":[103],"method":[104,190,215],"treats":[105],"from":[107,126],"as":[111,116,130],"positive":[112],"pairs,":[113],"neighboring":[114],"sub-positive":[117],"pairs":[118],"due":[119],"their":[121],"high":[122],"correlation,":[123],"temporally":[127],"distant":[128],"negative":[131],"pairs.":[132],"This":[133,167],"approach":[134],"effectively":[135],"strengthens":[136],"representation":[138],"capability":[139],"model,":[142],"thereby":[143],"improving":[144],"predictive":[146],"performance.":[147,209],"Additionally,":[148],"redefine":[150],"task":[154],"converting":[156],"it":[157],"an":[159,178],"anomaly":[160],"score":[161],"regression":[162],"problem":[163],"soft":[165],"labels.":[166],"redefinition":[168],"allows":[169],"model":[171],"better":[173],"quantify":[174],"likelihood":[176],"accident,":[179],"offering":[180],"nuanced":[183],"accurate":[185],"prediction.":[186],"We":[187],"evaluate":[188],"our":[189,214],"comprehensively":[191],"on":[192],"two":[193],"publicly":[194],"available":[195,230],"Dashcam":[196],"Accident":[197],"Dataset":[198,203],"(DAD)":[199],"Car":[201],"Crash":[202],"(CCD)":[204],"datasets":[205],"assess":[207],"its":[208,222],"The":[210],"results":[211],"demonstrate":[212],"that":[213],"outperforms":[216],"state-of-the-art":[217],"approaches,":[220],"highlighting":[221],"potential":[223],"practical":[225],"applications.":[226],"Code":[227],"will":[228],"be":[229],"at":[231],"https://github.com/yangugu/TAP_CC":[232],".":[233]},"counts_by_year":[],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-10-10T00:00:00"}