{"id":"https://openalex.org/W3209288812","doi":"https://doi.org/10.1109/itsc48978.2021.9564733","title":"A Learning-Based Method for Predicting Heterogeneous Traffic Agent Trajectories: Implications for Transfer Learning","display_name":"A Learning-Based Method for Predicting Heterogeneous Traffic Agent Trajectories: Implications for Transfer Learning","publication_year":2021,"publication_date":"2021-09-19","ids":{"openalex":"https://openalex.org/W3209288812","doi":"https://doi.org/10.1109/itsc48978.2021.9564733","mag":"3209288812"},"language":"en","primary_location":{"id":"doi:10.1109/itsc48978.2021.9564733","is_oa":false,"landing_page_url":"https://doi.org/10.1109/itsc48978.2021.9564733","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Intelligent Transportation Systems Conference (ITSC)","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/A5086173776","display_name":"Ethan Zhang","orcid":"https://orcid.org/0000-0003-3249-0617"},"institutions":[{"id":"https://openalex.org/I27837315","display_name":"University of Michigan","ror":"https://ror.org/00jmfr291","country_code":"US","type":"education","lineage":["https://openalex.org/I27837315"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ethan Zhang","raw_affiliation_strings":["Department of Civil and Environmental Engineering, University of Michigan, Ann Arbor, MI, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Civil and Environmental Engineering, University of Michigan, Ann Arbor, MI, USA","institution_ids":["https://openalex.org/I27837315"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5010805966","display_name":"Sion Pizzi","orcid":null},"institutions":[{"id":"https://openalex.org/I27837315","display_name":"University of Michigan","ror":"https://ror.org/00jmfr291","country_code":"US","type":"education","lineage":["https://openalex.org/I27837315"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Sion Pizzi","raw_affiliation_strings":["Department of Civil and Environmental Engineering, University of Michigan, Ann Arbor, MI, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Civil and Environmental Engineering, University of Michigan, Ann Arbor, MI, USA","institution_ids":["https://openalex.org/I27837315"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5083290583","display_name":"Neda Masoud","orcid":"https://orcid.org/0000-0002-6526-3317"},"institutions":[{"id":"https://openalex.org/I27837315","display_name":"University of Michigan","ror":"https://ror.org/00jmfr291","country_code":"US","type":"education","lineage":["https://openalex.org/I27837315"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Neda Masoud","raw_affiliation_strings":["Department of Civil and Environmental Engineering, University of Michigan, Ann Arbor, MI, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Civil and Environmental Engineering, University of Michigan, Ann Arbor, MI, USA","institution_ids":["https://openalex.org/I27837315"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I27837315"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1853","last_page":"1858"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.9991999864578247,"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"}},"topics":[{"id":"https://openalex.org/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.9991999864578247,"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/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9991999864578247,"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/T10698","display_name":"Transportation Planning and Optimization","score":0.9968000054359436,"subfield":{"id":"https://openalex.org/subfields/3313","display_name":"Transportation"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.8494135737419128},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7891522645950317},{"id":"https://openalex.org/keywords/transfer-of-learning","display_name":"Transfer of learning","score":0.6846518516540527},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6354197263717651},{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.6342966556549072},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6270443797111511},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5472341775894165},{"id":"https://openalex.org/keywords/kalman-filter","display_name":"Kalman filter","score":0.496648371219635}],"concepts":[{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.8494135737419128},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7891522645950317},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.6846518516540527},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6354197263717651},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.6342966556549072},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6270443797111511},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5472341775894165},{"id":"https://openalex.org/C157286648","wikidata":"https://www.wikidata.org/wiki/Q846780","display_name":"Kalman filter","level":2,"score":0.496648371219635},{"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},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/itsc48978.2021.9564733","is_oa":false,"landing_page_url":"https://doi.org/10.1109/itsc48978.2021.9564733","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Intelligent Transportation Systems Conference (ITSC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11","score":0.6000000238418579}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":45,"referenced_works":["https://openalex.org/W316935178","https://openalex.org/W1508389067","https://openalex.org/W1546862366","https://openalex.org/W2097545165","https://openalex.org/W2112222053","https://openalex.org/W2115476210","https://openalex.org/W2165698076","https://openalex.org/W2206222117","https://openalex.org/W2411988314","https://openalex.org/W2424778531","https://openalex.org/W2507412229","https://openalex.org/W2513866924","https://openalex.org/W2741222326","https://openalex.org/W2763318067","https://openalex.org/W2766836212","https://openalex.org/W2774231091","https://openalex.org/W2792764194","https://openalex.org/W2797522824","https://openalex.org/W2803184913","https://openalex.org/W2891058410","https://openalex.org/W2897804989","https://openalex.org/W2899817162","https://openalex.org/W2963914175","https://openalex.org/W2963945905","https://openalex.org/W2964193755","https://openalex.org/W2969995408","https://openalex.org/W2975262648","https://openalex.org/W2991484432","https://openalex.org/W2996180273","https://openalex.org/W3003094473","https://openalex.org/W3010518806","https://openalex.org/W3037058446","https://openalex.org/W3038712064","https://openalex.org/W3046209901","https://openalex.org/W3105115779","https://openalex.org/W3109483932","https://openalex.org/W3114950604","https://openalex.org/W3124885228","https://openalex.org/W3214535627","https://openalex.org/W4411417900","https://openalex.org/W6715190435","https://openalex.org/W6748963695","https://openalex.org/W6754520643","https://openalex.org/W6772097985","https://openalex.org/W6772925490"],"related_works":["https://openalex.org/W2378211422","https://openalex.org/W2745001401","https://openalex.org/W4321353415","https://openalex.org/W2130974462","https://openalex.org/W972276598","https://openalex.org/W4246352526","https://openalex.org/W2028665553","https://openalex.org/W2086519370","https://openalex.org/W2087343574","https://openalex.org/W2121910908"],"abstract_inverted_index":{"In":[0],"this":[1],"paper":[2],"we":[3],"present":[4],"a":[5,35,48,86,100],"learning-based":[6,106],"trajectory":[7,56],"prediction":[8,119,130],"method":[9,107,111],"for":[10,44,70],"different":[11,41,45,71],"road":[12],"users,":[13],"including":[14],"vehicles,":[15],"pedestrians,":[16],"and":[17,28,112],"cyclists.":[18],"The":[19],"model":[20,42,50,77],"uses":[21],"history":[22],"position":[23],"information":[24],"of":[25,32,39,88],"traffic":[26],"agents,":[27],"predicts":[29],"future":[30],"positions":[31],"subjects":[33],"within":[34],"finite":[36],"horizon.":[37],"Instead":[38],"developing":[40],"architectures":[43],"agent":[46,72,123],"types,":[47],"generic":[49],"architecture":[51,60],"is":[52,61],"used":[53],"to":[54],"learn":[55],"patterns.":[57],"This":[58],"common":[59],"then":[62],"trained":[63],"using":[64,139],"agent-specific":[65],"datasets,":[66],"providing":[67],"individualized":[68],"models":[69],"types.":[73,124],"We":[74,125],"evaluate":[75],"the":[76,79,105,109,129],"on":[78,132],"Lyft":[80],"dataset-a":[81],"public":[82],"dataset":[83],"collected":[84],"by":[85],"set":[87],"autonomous":[89],"vehicles-and":[90],"compare":[91],"its":[92],"performance":[93],"against":[94],"extended":[95],"Kalman":[96],"filter":[97],"(EKF)":[98],"as":[99],"benchmark.":[101],"Results":[102],"indicate":[103],"that":[104,128],"outperforms":[108],"benchmark":[110],"provides":[113],"high":[114],"accuracy":[115,131],"predictions":[116],"in":[117],"5-second":[118],"horizons":[120],"across":[121],"all":[122],"also":[126],"show":[127],"rarely-seen":[133],"agents":[134],"can":[135],"be":[136],"greatly":[137],"improved":[138],"transfer":[140],"learning,":[141]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":2}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
