{"id":"https://openalex.org/W4400648183","doi":"https://doi.org/10.1109/iv55156.2024.10588493","title":"Exploring Generative AI for Sim2Real in Driving Data Synthesis","display_name":"Exploring Generative AI for Sim2Real in Driving Data Synthesis","publication_year":2024,"publication_date":"2024-06-02","ids":{"openalex":"https://openalex.org/W4400648183","doi":"https://doi.org/10.1109/iv55156.2024.10588493"},"language":"en","primary_location":{"id":"doi:10.1109/iv55156.2024.10588493","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iv55156.2024.10588493","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE Intelligent Vehicles Symposium (IV)","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/A5015454654","display_name":"Haonan Zhao","orcid":"https://orcid.org/0009-0001-6263-0847"},"institutions":[{"id":"https://openalex.org/I39555362","display_name":"University of Warwick","ror":"https://ror.org/01a77tt86","country_code":"GB","type":"education","lineage":["https://openalex.org/I39555362"]}],"countries":["GB"],"is_corresponding":true,"raw_author_name":"Haonan Zhao","raw_affiliation_strings":["University of Warwick,WMG"],"affiliations":[{"raw_affiliation_string":"University of Warwick,WMG","institution_ids":["https://openalex.org/I39555362"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102810576","display_name":"Yi\u2010Ting Wang","orcid":"https://orcid.org/0000-0003-2008-4435"},"institutions":[{"id":"https://openalex.org/I39555362","display_name":"University of Warwick","ror":"https://ror.org/01a77tt86","country_code":"GB","type":"education","lineage":["https://openalex.org/I39555362"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Yiting Wang","raw_affiliation_strings":["University of Warwick,WMG"],"affiliations":[{"raw_affiliation_string":"University of Warwick,WMG","institution_ids":["https://openalex.org/I39555362"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5019694462","display_name":"Thomas Bashford\u2010Rogers","orcid":"https://orcid.org/0000-0003-4669-0417"},"institutions":[{"id":"https://openalex.org/I39555362","display_name":"University of Warwick","ror":"https://ror.org/01a77tt86","country_code":"GB","type":"education","lineage":["https://openalex.org/I39555362"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Thomas Bashford-Rogers","raw_affiliation_strings":["University of Warwick,WMG"],"affiliations":[{"raw_affiliation_string":"University of Warwick,WMG","institution_ids":["https://openalex.org/I39555362"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5032382943","display_name":"Valentina Donzella","orcid":"https://orcid.org/0000-0002-3408-6135"},"institutions":[{"id":"https://openalex.org/I39555362","display_name":"University of Warwick","ror":"https://ror.org/01a77tt86","country_code":"GB","type":"education","lineage":["https://openalex.org/I39555362"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Valentina Donzella","raw_affiliation_strings":["University of Warwick,WMG"],"affiliations":[{"raw_affiliation_string":"University of Warwick,WMG","institution_ids":["https://openalex.org/I39555362"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5087052285","display_name":"Kurt Debattista","orcid":"https://orcid.org/0000-0003-2982-5199"},"institutions":[{"id":"https://openalex.org/I39555362","display_name":"University of Warwick","ror":"https://ror.org/01a77tt86","country_code":"GB","type":"education","lineage":["https://openalex.org/I39555362"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Kurt Debattista","raw_affiliation_strings":["University of Warwick,WMG"],"affiliations":[{"raw_affiliation_string":"University of Warwick,WMG","institution_ids":["https://openalex.org/I39555362"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5015454654"],"corresponding_institution_ids":["https://openalex.org/I39555362"],"apc_list":null,"apc_paid":null,"fwci":3.3384,"has_fulltext":false,"cited_by_count":12,"citation_normalized_percentile":{"value":0.91969271,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"3071","last_page":"3077"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9174000024795532,"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":0.9174000024795532,"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/T11195","display_name":"Simulation Techniques and Applications","score":0.911899983882904,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6477429270744324},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.636163055896759},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4336467385292053},{"id":"https://openalex.org/keywords/human\u2013computer-interaction","display_name":"Human\u2013computer interaction","score":0.37111908197402954}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6477429270744324},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.636163055896759},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4336467385292053},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.37111908197402954}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/iv55156.2024.10588493","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iv55156.2024.10588493","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE Intelligent Vehicles Symposium (IV)","raw_type":"proceedings-article"},{"id":"pmh:oai:wrap.warwick.ac.uk:187147","is_oa":false,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4306400665","display_name":"Warwick Research Archive Portal (University of Warwick)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I39555362","host_organization_name":"University of Warwick","host_organization_lineage":["https://openalex.org/I39555362"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"acceptedVersion","is_accepted":true,"is_published":false,"raw_source_name":"","raw_type":"Conference Item"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":57,"referenced_works":["https://openalex.org/W1580389772","https://openalex.org/W1821462560","https://openalex.org/W1982471090","https://openalex.org/W2102166818","https://openalex.org/W2115579991","https://openalex.org/W2141983208","https://openalex.org/W2150066425","https://openalex.org/W2150734399","https://openalex.org/W2340897893","https://openalex.org/W2487365028","https://openalex.org/W2889054948","https://openalex.org/W2962785568","https://openalex.org/W2962793481","https://openalex.org/W2963073614","https://openalex.org/W2963800363","https://openalex.org/W3000673855","https://openalex.org/W3034839660","https://openalex.org/W3035564946","https://openalex.org/W3049043369","https://openalex.org/W3108316907","https://openalex.org/W3162926177","https://openalex.org/W3163842339","https://openalex.org/W3216352822","https://openalex.org/W4226125322","https://openalex.org/W4283066310","https://openalex.org/W4283805732","https://openalex.org/W4296193306","https://openalex.org/W4301206121","https://openalex.org/W4312469475","https://openalex.org/W4312576277","https://openalex.org/W4312696983","https://openalex.org/W4312740349","https://openalex.org/W4312911498","https://openalex.org/W4312933868","https://openalex.org/W4319299887","https://openalex.org/W4366149348","https://openalex.org/W4376851267","https://openalex.org/W4385271281","https://openalex.org/W4385800865","https://openalex.org/W4386071839","https://openalex.org/W4386076027","https://openalex.org/W4386494508","https://openalex.org/W4386876077","https://openalex.org/W4390873054","https://openalex.org/W4391770563","https://openalex.org/W4392182560","https://openalex.org/W6638523607","https://openalex.org/W6679045638","https://openalex.org/W6745935785","https://openalex.org/W6765779288","https://openalex.org/W6791353385","https://openalex.org/W6795288823","https://openalex.org/W6810940779","https://openalex.org/W6839517220","https://openalex.org/W6856768204","https://openalex.org/W6860038054","https://openalex.org/W6861779526"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2748952813","https://openalex.org/W2380075625","https://openalex.org/W2390279801","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W4396696052","https://openalex.org/W2382290278"],"abstract_inverted_index":{"Datasets":[0],"are":[1,138,155],"essential":[2],"for":[3,77,105,196,212],"training":[4],"and":[5,13,20,125,134,143,174,192],"testing":[6],"vehicle":[7,206],"perception":[8],"algorithms.":[9],"However,":[10],"the":[11,37,48,54,62,83,106,120,185,204,210,217],"collection":[12],"annotation":[14],"of":[15,47,108,114,122,209],"real-world":[16],"images":[17,133,160],"is":[18,117],"time-consuming":[19],"expensive.":[21],"Driving":[22],"simulators":[23],"offer":[24],"a":[25,43,99,103],"solution":[26],"by":[27],"automatically":[28],"generating":[29,158],"various":[30],"driving":[31,78,100,132],"scenarios":[32],"with":[33,140,163,171],"corresponding":[34],"annotations,":[35,137],"but":[36],"simulation-to-reality":[38],"(Sim2Real)":[39],"domain":[40],"gap":[41],"remains":[42],"challenge.":[44],"While":[45],"most":[46],"Generative":[49,57],"Artificial":[50],"Intelligence":[51],"(AI)":[52],"follows":[53],"de":[55],"facto":[56],"Adversarial":[58],"Nets":[59],"(GANs)-based":[60],"methods,":[61],"recent":[63],"emerging":[64],"diffusion":[65,213],"probabilistic":[66],"models":[67,214],"have":[68],"not":[69],"been":[70],"fully":[71],"explored":[72],"in":[73],"mitigating":[74],"Sim2Real":[75,198,218],"challenges":[76],"data":[79],"synthesis.":[80],"To":[81],"explore":[82],"performance,":[84],"this":[85],"paper":[86],"applied":[87],"three":[88],"different":[89],"generative":[90],"AI":[91],"methods":[92,116,154],"to":[93,203,215],"leverage":[94],"semantic":[95],"label":[96],"maps":[97],"from":[98,119],"simulator":[101],"as":[102],"bridge":[104],"creation":[107],"realistic":[109],"datasets.":[110],"A":[111],"comparative":[112],"analysis":[113],"these":[115],"presented":[118],"perspective":[121],"image":[123],"quality":[124],"perception.":[126],"New":[127],"synthetic":[128,169],"datasets,":[129],"which":[130],"include":[131],"auto-generated":[135],"high-quality":[136,159],"produced":[139],"low":[141],"costs":[142],"high":[144],"scene":[145],"variability.":[146],"The":[147],"experimental":[148],"results":[149],"show":[150],"that":[151,184],"although":[152],"GAN-based":[153],"adept":[156],"at":[157],"when":[161,178],"provided":[162],"manually":[164],"annotated":[165],"labels,":[166],"ControlNet":[167],"produces":[168],"datasets":[170],"fewer":[172],"artefacts":[173],"more":[175],"structural":[176],"fidelity":[177],"using":[179],"simulator-generated":[180],"labels.":[181],"This":[182],"suggests":[183],"diffusion-based":[186],"approach":[187],"may":[188],"provide":[189],"improved":[190],"stability":[191],"an":[193],"alternative":[194],"method":[195],"addressing":[197],"challenges.":[199],"These":[200],"insights":[201],"contribute":[202],"intelligent":[205],"community\u2019s":[207],"understanding":[208],"potential":[211],"mitigate":[216],"gap.":[219]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":9},{"year":2024,"cited_by_count":1}],"updated_date":"2025-12-26T23:08:49.675405","created_date":"2025-10-10T00:00:00"}