{"id":"https://openalex.org/W2974619362","doi":"https://doi.org/10.1109/syscon.2019.8836918","title":"Neural Networks for End-to-End Refinement of Simulated Sensor Data for Automotive Applications","display_name":"Neural Networks for End-to-End Refinement of Simulated Sensor Data for Automotive Applications","publication_year":2019,"publication_date":"2019-04-01","ids":{"openalex":"https://openalex.org/W2974619362","doi":"https://doi.org/10.1109/syscon.2019.8836918","mag":"2974619362"},"language":"en","primary_location":{"id":"doi:10.1109/syscon.2019.8836918","is_oa":false,"landing_page_url":"https://doi.org/10.1109/syscon.2019.8836918","pdf_url":null,"source":{"id":"https://openalex.org/S4306498505","display_name":"2019 IEEE International Systems Conference (SysCon)","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":"2019 IEEE International Systems Conference (SysCon)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5003710401","display_name":"J\u00f6rn Thieling","orcid":"https://orcid.org/0000-0003-2503-6328"},"institutions":[{"id":"https://openalex.org/I887968799","display_name":"RWTH Aachen University","ror":"https://ror.org/04xfq0f34","country_code":"DE","type":"education","lineage":["https://openalex.org/I887968799"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Jorn Thieling","raw_affiliation_strings":["Institute for Man-Machine Interaction, RWTH Aachen University, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute for Man-Machine Interaction, RWTH Aachen University, Germany","institution_ids":["https://openalex.org/I887968799"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5091208983","display_name":"Philip Elspas","orcid":null},"institutions":[{"id":"https://openalex.org/I887968799","display_name":"RWTH Aachen University","ror":"https://ror.org/04xfq0f34","country_code":"DE","type":"education","lineage":["https://openalex.org/I887968799"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Philip Elspas","raw_affiliation_strings":["Institute for Man-Machine Interaction, RWTH Aachen University, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute for Man-Machine Interaction, RWTH Aachen University, Germany","institution_ids":["https://openalex.org/I887968799"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5108137607","display_name":"Jurgen Rosmann","orcid":null},"institutions":[{"id":"https://openalex.org/I887968799","display_name":"RWTH Aachen University","ror":"https://ror.org/04xfq0f34","country_code":"DE","type":"education","lineage":["https://openalex.org/I887968799"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Jurgen Rosmann","raw_affiliation_strings":["Institute for Man-Machine Interaction, RWTH Aachen University, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute for Man-Machine Interaction, RWTH Aachen University, Germany","institution_ids":["https://openalex.org/I887968799"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I887968799"],"apc_list":null,"apc_paid":null,"fwci":0.8737,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.67357378,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12810","display_name":"Real-time simulation and control systems","score":0.9872999787330627,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"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/T12810","display_name":"Real-time simulation and control systems","score":0.9872999787330627,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems 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/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.9781000018119812,"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/T10320","display_name":"Neural Networks and Applications","score":0.97079998254776,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/end-to-end-principle","display_name":"End-to-end principle","score":0.7910845279693604},{"id":"https://openalex.org/keywords/automotive-industry","display_name":"Automotive industry","score":0.7259446978569031},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6152603626251221},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5679007768630981},{"id":"https://openalex.org/keywords/end-milling","display_name":"End milling","score":0.48510801792144775},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.26027435064315796},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.21370229125022888},{"id":"https://openalex.org/keywords/mechanical-engineering","display_name":"Mechanical engineering","score":0.16767507791519165},{"id":"https://openalex.org/keywords/aerospace-engineering","display_name":"Aerospace engineering","score":0.08661121129989624}],"concepts":[{"id":"https://openalex.org/C74296488","wikidata":"https://www.wikidata.org/wiki/Q2527392","display_name":"End-to-end principle","level":2,"score":0.7910845279693604},{"id":"https://openalex.org/C526921623","wikidata":"https://www.wikidata.org/wiki/Q190117","display_name":"Automotive industry","level":2,"score":0.7259446978569031},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6152603626251221},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5679007768630981},{"id":"https://openalex.org/C2984155413","wikidata":"https://www.wikidata.org/wiki/Q8194616","display_name":"End milling","level":3,"score":0.48510801792144775},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.26027435064315796},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.21370229125022888},{"id":"https://openalex.org/C78519656","wikidata":"https://www.wikidata.org/wiki/Q101333","display_name":"Mechanical engineering","level":1,"score":0.16767507791519165},{"id":"https://openalex.org/C146978453","wikidata":"https://www.wikidata.org/wiki/Q3798668","display_name":"Aerospace engineering","level":1,"score":0.08661121129989624},{"id":"https://openalex.org/C523214423","wikidata":"https://www.wikidata.org/wiki/Q192047","display_name":"Machining","level":2,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/syscon.2019.8836918","is_oa":false,"landing_page_url":"https://doi.org/10.1109/syscon.2019.8836918","pdf_url":null,"source":{"id":"https://openalex.org/S4306498505","display_name":"2019 IEEE International Systems Conference (SysCon)","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":"2019 IEEE International Systems Conference (SysCon)","raw_type":"proceedings-article"},{"id":"pmh:oai:publications.rwth-aachen.de:767104","is_oa":false,"landing_page_url":"https://publications.rwth-aachen.de/record/767104","pdf_url":null,"source":{"id":"https://openalex.org/S4306401362","display_name":"RWTH Publications (RWTH Aachen)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I887968799","host_organization_name":"RWTH Aachen University","host_organization_lineage":["https://openalex.org/I887968799"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"SYSCON 2019 : the 13th Annual IEEE International Systems Conference : April 8-11, 2019, Hyatt Grand Cypress Hotel, Orlando, Florida, USA : 2019 conference proceedings / sponsors and organizers: IEEE Systems Council, IEEE<br/>13. Annual IEEE International Systems Conference, SysCon 2019, Orlando, USA, 2019-04-08 - 2019-04-11","raw_type":"info:eu-repo/semantics/conferenceObject"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9","score":0.5199999809265137}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W1686810756","https://openalex.org/W1710476689","https://openalex.org/W2173520492","https://openalex.org/W2279098554","https://openalex.org/W2340897893","https://openalex.org/W2431874326","https://openalex.org/W2593414223","https://openalex.org/W2962785568","https://openalex.org/W2962793481","https://openalex.org/W2962947361","https://openalex.org/W2963073614","https://openalex.org/W2963800363","https://openalex.org/W2963841322","https://openalex.org/W2963890275","https://openalex.org/W4298289240"],"related_works":["https://openalex.org/W2748952813","https://openalex.org/W3179968364","https://openalex.org/W4382644535","https://openalex.org/W2522768275","https://openalex.org/W2352938035","https://openalex.org/W2390279801","https://openalex.org/W4237894622","https://openalex.org/W4240479622","https://openalex.org/W4252643043","https://openalex.org/W4250565955"],"abstract_inverted_index":{"The":[0],"rising":[1],"use":[2],"of":[3,21,59,71,95,140,164,191,207],"Artificial":[4],"Intelligence":[5],"(AI)":[6],"for":[7],"Advanced":[8],"Driver":[9],"Assistance":[10],"Systems":[11],"(ADAS)":[12],"and":[13,25,55,89,148,172,181,218],"Autonomous":[14],"Vehicles":[15],"(AVs)":[16],"comes":[17],"with":[18,38,146,179],"the":[19,51,93,112,115,165,188,198,205,208],"need":[20,62],"comprehensive":[22],"tests,":[23],"verification":[24],"validation.":[26],"This":[27,135],"is":[28,87,103,117,202,216],"hardly":[29],"achievable":[30],"in":[31,41,143,197,214],"real":[32,60,107],"test":[33],"drives":[34],"alone,":[35],"but":[36],"validation":[37],"simulated":[39,109,128],"sensors":[40,61],"Virtual":[42],"Testbeds":[43],"(VTBs)":[44],"becomes":[45],"a":[46,80,100,123,150,192],"popular":[47],"supplement.":[48],"To":[49],"reduce":[50],"gap":[52],"between":[53],"simulation":[54],"reality,":[56],"Digital":[57],"Twins":[58],"to":[63,105,121,131,152,177,187,211],"generate":[64,153],"data":[65],"as":[66,68,75,119],"realistic":[67,155,194],"possible.":[69],"Instead":[70],"classical":[72],"methods":[73,174],"such":[74],"rasterization":[76],"or":[77],"ray":[78],"tracing,":[79],"novel":[81,209],"approach":[82,210],"based":[83],"on":[84,92],"neural":[85],"networks":[86],"developed":[88],"evaluated.":[90],"Based":[91],"concept":[94],"Generative":[96],"Adversarial":[97],"Networks":[98],"(GANs)":[99],"classification":[101],"network":[102,125],"trained":[104],"distinguish":[106],"from":[108],"images.":[110],"At":[111],"same":[113],"time":[114],"classifier":[116],"used":[118,213],"critic":[120],"improve":[122],"generation":[124],"that":[126],"refines":[127],"sensor":[129,156,200],"images":[130,157,201],"look":[132],"more":[133,154,193],"realistic.":[134],"contribution":[136],"gives":[137],"an":[138],"overview":[139],"recent":[141],"research":[142],"image-to-image":[144,167],"translation":[145,168],"GANs":[147],"suggests":[149],"framework":[151],"for-but":[158],"not":[159],"limited":[160],"to-automotive":[161],"applications.":[162],"State":[163],"art":[166],"architectures":[169],"are":[170,175],"evaluated":[171],"several":[173],"suggested":[176],"deal":[178],"drawbacks":[180],"shortcomings.":[182],"An":[183],"evaluation":[184],"metric":[185],"according":[186],"subjective":[189],"assessment":[190],"color":[195],"distribution":[196],"refined":[199],"introduced.":[203],"Finally,":[204],"potential":[206],"be":[212],"VTBs":[215],"analyzed":[217],"discussed.":[219]},"counts_by_year":[{"year":2024,"cited_by_count":2},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1}],"updated_date":"2026-08-22T07:34:49.880490","created_date":"2025-10-10T00:00:00"}
