{"id":"https://openalex.org/W3091625626","doi":"https://doi.org/10.1109/ijcnn48605.2020.9206627","title":"The Importance of Balanced Data Sets: Analyzing a Vehicle Trajectory Prediction Model based on Neural Networks and Distributed Representations","display_name":"The Importance of Balanced Data Sets: Analyzing a Vehicle Trajectory Prediction Model based on Neural Networks and Distributed Representations","publication_year":2020,"publication_date":"2020-07-01","ids":{"openalex":"https://openalex.org/W3091625626","doi":"https://doi.org/10.1109/ijcnn48605.2020.9206627","mag":"3091625626"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn48605.2020.9206627","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn48605.2020.9206627","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},"type":"article","indexed_in":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2010.00084","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5077954862","display_name":"Florian Mirus","orcid":"https://orcid.org/0000-0001-8923-6009"},"institutions":[{"id":"https://openalex.org/I1283382300","display_name":"BMW (Germany)","ror":"https://ror.org/05vs9tj88","country_code":"DE","type":"company","lineage":["https://openalex.org/I1283382300","https://openalex.org/I4210156768"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Florian Mirus","raw_affiliation_strings":["Research, New Technologies, Innovations, BMW AG, Garching, Germany"],"affiliations":[{"raw_affiliation_string":"Research, New Technologies, Innovations, BMW AG, Garching, Germany","institution_ids":["https://openalex.org/I1283382300"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5069590039","display_name":"Terrence C. Stewart","orcid":"https://orcid.org/0000-0002-1445-7613"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Terrence C. Stewart","raw_affiliation_strings":["Applied Brain Research Inc., Waterloo, Ontario, Canada"],"affiliations":[{"raw_affiliation_string":"Applied Brain Research Inc., Waterloo, Ontario, Canada","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5032405883","display_name":"J\u00f6rg Conradt","orcid":"https://orcid.org/0000-0001-5998-9640"},"institutions":[{"id":"https://openalex.org/I86987016","display_name":"KTH Royal Institute of Technology","ror":"https://ror.org/026vcq606","country_code":"SE","type":"education","lineage":["https://openalex.org/I86987016"]}],"countries":["SE"],"is_corresponding":false,"raw_author_name":"Jorg Conradt","raw_affiliation_strings":["Dep. of Comp. Science and Technology, KTH Royal Institute of Technology, Stockholm, Sweden"],"affiliations":[{"raw_affiliation_string":"Dep. of Comp. Science and Technology, KTH Royal Institute of Technology, Stockholm, Sweden","institution_ids":["https://openalex.org/I86987016"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5077954862"],"corresponding_institution_ids":["https://openalex.org/I1283382300"],"apc_list":null,"apc_paid":null,"fwci":1.1704,"has_fulltext":false,"cited_by_count":11,"citation_normalized_percentile":{"value":0.7765381,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":"abs 1604 7316","issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9979000091552734,"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":0.9979000091552734,"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/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.996999979019165,"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9840999841690063,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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":0.7467014789581299},{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.7334399223327637},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6707332134246826},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5189720988273621},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.4618251919746399},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4018033444881439},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3906792402267456},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.06509223580360413}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7467014789581299},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.7334399223327637},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6707332134246826},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5189720988273621},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.4618251919746399},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4018033444881439},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3906792402267456},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.06509223580360413},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C1276947","wikidata":"https://www.wikidata.org/wiki/Q333","display_name":"Astronomy","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/ijcnn48605.2020.9206627","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn48605.2020.9206627","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2010.00084","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2010.00084","pdf_url":"https://arxiv.org/pdf/2010.00084","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2010.00084","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2010.00084","pdf_url":"https://arxiv.org/pdf/2010.00084","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":34,"referenced_works":["https://openalex.org/W215825141","https://openalex.org/W1488378957","https://openalex.org/W1615576556","https://openalex.org/W1861492603","https://openalex.org/W2064675550","https://openalex.org/W2069230091","https://openalex.org/W2097545165","https://openalex.org/W2101545882","https://openalex.org/W2108069432","https://openalex.org/W2108598243","https://openalex.org/W2115579991","https://openalex.org/W2134239466","https://openalex.org/W2340897893","https://openalex.org/W2342840547","https://openalex.org/W2343381408","https://openalex.org/W2424778531","https://openalex.org/W2517324061","https://openalex.org/W2791763440","https://openalex.org/W2792764867","https://openalex.org/W2803184913","https://openalex.org/W2952122856","https://openalex.org/W2963292632","https://openalex.org/W2963906196","https://openalex.org/W2963914175","https://openalex.org/W2970783931","https://openalex.org/W2980087597","https://openalex.org/W2980740513","https://openalex.org/W3105115779","https://openalex.org/W4233030948","https://openalex.org/W6629215626","https://openalex.org/W6704559304","https://openalex.org/W6749825310","https://openalex.org/W6767368257","https://openalex.org/W6768870957"],"related_works":["https://openalex.org/W4323768008","https://openalex.org/W1941703695","https://openalex.org/W4248382324","https://openalex.org/W3131574667","https://openalex.org/W4360995134","https://openalex.org/W2039473718","https://openalex.org/W2387529410","https://openalex.org/W3023605104","https://openalex.org/W2383578611","https://openalex.org/W2987583674"],"abstract_inverted_index":{"Predicting":[0],"future":[1],"behavior":[2],"of":[3,55,58,100,109,140],"other":[4],"traffic":[5],"participants":[6],"is":[7,147],"an":[8,131],"essential":[9],"task":[10],"that":[11,116,137],"needs":[12],"to":[13,23,31,87],"be":[14],"solved":[15],"by":[16],"automated":[17],"vehicles":[18,32],"and":[19,26,135],"human":[20],"drivers":[21],"alike":[22],"achieve":[24],"safe":[25],"situation-aware":[27],"driving.":[28],"Modern":[29],"approaches":[30],"trajectory":[33,77,145],"prediction":[34,78,111,146],"typically":[35],"rely":[36],"on":[37,72,75,80,106,130,156],"data-driven":[38],"models":[39,83,118],"like":[40],"neural":[41,81],"networks,":[42],"in":[43,91,102,143],"particular":[44],"LSTMs":[45],"(Long":[46],"Short-Term":[47],"Memorys),":[48],"achieving":[49],"promising":[50],"results.":[51],"However,":[52],"the":[53,59,98,103,107,117,125,138],"question":[54],"optimal":[56],"composition":[57,139],"underlying":[60],"training":[61,104,141],"data":[62,105,133,142],"has":[63],"received":[64],"less":[65],"attention.":[66],"In":[67],"this":[68],"paper,":[69],"we":[70,114],"expand":[71],"previous":[73],"work":[74],"vehicle":[76,144],"based":[79],"network":[82],"employing":[84,119],"distributed":[85],"representations":[86],"encode":[88],"automotive":[89],"scenes":[90],"a":[92],"semantic":[93,121],"vector":[94,122],"substrate.":[95],"We":[96,152],"analyze":[97],"influence":[99],"variations":[101],"performance":[108],"our":[110,120,154],"models.":[112],"Thereby,":[113],"show":[115],"representation":[123],"outperform":[124],"numerical":[126],"model":[127],"when":[128],"trained":[129],"adequate":[132],"set":[134],"thereby,":[136],"crucial":[148],"for":[149],"successful":[150],"training.":[151],"conduct":[153],"analysis":[155],"challenging":[157],"real-world":[158],"driving":[159],"data.":[160]},"counts_by_year":[{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":3}],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2025-10-10T00:00:00"}