{"id":"https://openalex.org/W3166657548","doi":"https://doi.org/10.1109/icme51207.2021.9428166","title":"DeepWORD: A GCN-Based Approach for Owner-Member Relationship Detection in Autonomous Driving","display_name":"DeepWORD: A GCN-Based Approach for Owner-Member Relationship Detection in Autonomous Driving","publication_year":2021,"publication_date":"2021-06-09","ids":{"openalex":"https://openalex.org/W3166657548","doi":"https://doi.org/10.1109/icme51207.2021.9428166","mag":"3166657548"},"language":"en","primary_location":{"id":"doi:10.1109/icme51207.2021.9428166","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icme51207.2021.9428166","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Conference on Multimedia and Expo (ICME)","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/A5091037704","display_name":"Zizhang Wu","orcid":"https://orcid.org/0000-0002-2169-8271"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Zizhang Wu","raw_affiliation_strings":["Zongmu Technology"],"affiliations":[{"raw_affiliation_string":"Zongmu Technology","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049057785","display_name":"Man Wang","orcid":"https://orcid.org/0000-0002-1201-7518"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Man Wang","raw_affiliation_strings":["Zongmu Technology"],"affiliations":[{"raw_affiliation_string":"Zongmu Technology","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100700440","display_name":"Jason Wang","orcid":"https://orcid.org/0009-0003-1875-7693"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jason Wang","raw_affiliation_strings":["Zongmu Technology"],"affiliations":[{"raw_affiliation_string":"Zongmu Technology","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100721586","display_name":"Wenkai Zhang","orcid":"https://orcid.org/0000-0002-8903-2708"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wenkai Zhang","raw_affiliation_strings":["Zongmu Technology"],"affiliations":[{"raw_affiliation_string":"Zongmu Technology","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5010396627","display_name":"Muqing Fang","orcid":null},"institutions":[{"id":"https://openalex.org/I177477856","display_name":"Polytechnic University of Turin","ror":"https://ror.org/00bgk9508","country_code":"IT","type":"education","lineage":["https://openalex.org/I177477856"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Muqing Fang","raw_affiliation_strings":["Politecnico di Torino"],"affiliations":[{"raw_affiliation_string":"Politecnico di Torino","institution_ids":["https://openalex.org/I177477856"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5039802736","display_name":"Tianhao Xu","orcid":"https://orcid.org/0000-0002-7483-3940"},"institutions":[{"id":"https://openalex.org/I94509681","display_name":"Technische Universit\u00e4t Braunschweig","ror":"https://ror.org/010nsgg66","country_code":"DE","type":"education","lineage":["https://openalex.org/I94509681"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Tianhao Xu","raw_affiliation_strings":["Technical University of Braunschweig"],"affiliations":[{"raw_affiliation_string":"Technical University of Braunschweig","institution_ids":["https://openalex.org/I94509681"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5091037704"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0961,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.36650327,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9995999932289124,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9995999932289124,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.9994999766349792,"subfield":{"id":"https://openalex.org/subfields/2203","display_name":"Automotive Engineering"},"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9986000061035156,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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":0.7773777842521667},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.632729709148407},{"id":"https://openalex.org/keywords/attention-network","display_name":"Attention network","score":0.6256842613220215},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5749914646148682},{"id":"https://openalex.org/keywords/heuristic","display_name":"Heuristic","score":0.5705771446228027},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.5392443537712097},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.46790939569473267},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.460858017206192},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4249691367149353},{"id":"https://openalex.org/keywords/backbone-network","display_name":"Backbone network","score":0.4216860234737396},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3838409185409546},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.2567395567893982},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.15457066893577576},{"id":"https://openalex.org/keywords/computer-network","display_name":"Computer network","score":0.08475297689437866}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7773777842521667},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.632729709148407},{"id":"https://openalex.org/C2993807640","wikidata":"https://www.wikidata.org/wiki/Q103709453","display_name":"Attention network","level":2,"score":0.6256842613220215},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5749914646148682},{"id":"https://openalex.org/C173801870","wikidata":"https://www.wikidata.org/wiki/Q201413","display_name":"Heuristic","level":2,"score":0.5705771446228027},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.5392443537712097},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.46790939569473267},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.460858017206192},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4249691367149353},{"id":"https://openalex.org/C88796919","wikidata":"https://www.wikidata.org/wiki/Q1142907","display_name":"Backbone network","level":2,"score":0.4216860234737396},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3838409185409546},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.2567395567893982},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.15457066893577576},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.08475297689437866},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icme51207.2021.9428166","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icme51207.2021.9428166","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Conference on Multimedia and Expo (ICME)","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":27,"referenced_works":["https://openalex.org/W1662382123","https://openalex.org/W1902044805","https://openalex.org/W2070380640","https://openalex.org/W2101491865","https://openalex.org/W2148870548","https://openalex.org/W2172713084","https://openalex.org/W2607855566","https://openalex.org/W2744749505","https://openalex.org/W2804656766","https://openalex.org/W2915010861","https://openalex.org/W2937970997","https://openalex.org/W2948519073","https://openalex.org/W2963536419","https://openalex.org/W2964015378","https://openalex.org/W2964321699","https://openalex.org/W2981207549","https://openalex.org/W4256073327","https://openalex.org/W4294558607","https://openalex.org/W4297733535","https://openalex.org/W4302218341","https://openalex.org/W6637178625","https://openalex.org/W6679502496","https://openalex.org/W6720006811","https://openalex.org/W6726873649","https://openalex.org/W6738964360","https://openalex.org/W6745122088","https://openalex.org/W6760424586"],"related_works":["https://openalex.org/W3171329138","https://openalex.org/W4372263906","https://openalex.org/W4293056039","https://openalex.org/W2791559790","https://openalex.org/W2929240682","https://openalex.org/W4200210037","https://openalex.org/W4312787111","https://openalex.org/W4310510964","https://openalex.org/W3120085233","https://openalex.org/W3025672433"],"abstract_inverted_index":{"It\u2019s":[0],"worth":[1],"noting":[2],"that":[3,147],"the":[4,16,23,34,50,55,68,94,101,107,113,121],"owner-member":[5,130],"relationship":[6,36,79,131],"between":[7],"wheels":[8],"and":[9,153],"vehicles":[10],"has":[11],"an":[12,63,77,128],"significant":[13],"contribution":[14],"to":[15,61,105,118],"3D":[17],"perception":[18],"of":[19,103],"vehicles,":[20],"especially":[21],"in":[22,155],"embedded":[24],"environment.":[25],"However,":[26],"there":[27],"are":[28],"currently":[29],"two":[30],"main":[31],"challenges":[32],"about":[33],"above":[35],"prediction:":[37],"i)":[38],"The":[39,144],"traditional":[40],"heuristic":[41],"methods":[42],"based":[43],"on":[44],"IoU":[45],"can":[46],"hardly":[47],"deal":[48],"with":[49,97],"traffic":[51],"jam":[52],"scenarios":[53],"for":[54,67],"occlusion.":[56],"ii)":[57],"It":[58],"is":[59],"difficult":[60],"establish":[62,127],"efficient":[64],"applicable":[65],"solution":[66,149],"vehicle-mounted":[69],"system.":[70],"To":[71],"address":[72],"these":[73],"issues,":[74],"we":[75,92,111,126],"propose":[76],"innovative":[78],"prediction":[80],"method,":[81],"namely":[82],"DeepWORD,":[83],"by":[84],"designing":[85],"a":[86,136],"graph":[87,114],"convolution":[88],"network":[89,116],"(GCN).":[90],"Specifically,":[91],"utilize":[93],"feature":[95],"maps":[96],"local":[98],"correlation":[99],"as":[100,135],"input":[102],"nodes":[104],"improve":[106],"information":[108],"richness.":[109],"Besides,":[110],"introduce":[112],"attention":[115],"(GAT)":[117],"dynamically":[119],"amend":[120],"prior":[122],"estimation":[123],"deviation.":[124],"Furthermore,":[125],"annotated":[129],"dataset":[132],"called":[133],"WORD":[134],"large-scale":[137],"benchmark,":[138],"which":[139],"will":[140],"be":[141],"available":[142],"soon.":[143],"experiments":[145],"demonstrate":[146],"our":[148],"achieves":[150],"state-of-the-art":[151],"accuracy":[152],"real-time":[154],"practice.":[156]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2022,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
