{"id":"https://openalex.org/W7148483976","doi":"https://doi.org/10.48550/arxiv.2604.00402","title":"COTTA: Context-Aware Transfer Adaptation for Trajectory Prediction in Autonomous Driving","display_name":"COTTA: Context-Aware Transfer Adaptation for Trajectory Prediction in Autonomous Driving","publication_year":2026,"publication_date":"2026-04-01","ids":{"openalex":"https://openalex.org/W7148483976","doi":"https://doi.org/10.48550/arxiv.2604.00402"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.00402","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.00402","pdf_url":null,"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":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2604.00402","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5132807243","display_name":"Seohyoung Park","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Park, Seohyoung","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5126318481","display_name":"Jaeyeol Lim","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lim, Jaeyeol","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5073034437","display_name":"Seoyoung Ju","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ju, Seoyoung","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5126342227","display_name":"Kyeonghun Kim","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kim, Kyeonghun","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5132816820","display_name":"Nam-Joon Kim","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kim, Nam-Joon","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5132830650","display_name":"Hyuk-Jae Lee","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lee, Hyuk-Jae","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5132807243"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.9546999931335449,"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":0.9546999931335449,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.006500000134110451,"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/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.005900000222027302,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.8007000088691711},{"id":"https://openalex.org/keywords/adaptability","display_name":"Adaptability","score":0.7261000275611877},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.545799970626831},{"id":"https://openalex.org/keywords/mean-squared-prediction-error","display_name":"Mean squared prediction error","score":0.525600016117096},{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.51419997215271},{"id":"https://openalex.org/keywords/transfer-of-learning","display_name":"Transfer of learning","score":0.4690000116825104},{"id":"https://openalex.org/keywords/motion","display_name":"Motion (physics)","score":0.46380001306533813},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.3869999945163727}],"concepts":[{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.8007000088691711},{"id":"https://openalex.org/C177606310","wikidata":"https://www.wikidata.org/wiki/Q5674297","display_name":"Adaptability","level":2,"score":0.7261000275611877},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6820999979972839},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.545799970626831},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5303000211715698},{"id":"https://openalex.org/C167085575","wikidata":"https://www.wikidata.org/wiki/Q6803654","display_name":"Mean squared prediction error","level":2,"score":0.525600016117096},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.51419997215271},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.4690000116825104},{"id":"https://openalex.org/C104114177","wikidata":"https://www.wikidata.org/wiki/Q79782","display_name":"Motion (physics)","level":2,"score":0.46380001306533813},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.45339998602867126},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.3869999945163727},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.3862000107765198},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.3765999972820282},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.3564999997615814},{"id":"https://openalex.org/C2776434776","wikidata":"https://www.wikidata.org/wiki/Q19246213","display_name":"Domain adaptation","level":3,"score":0.3165999948978424},{"id":"https://openalex.org/C2780440489","wikidata":"https://www.wikidata.org/wiki/Q5227278","display_name":"Data-driven","level":2,"score":0.30869999527931213},{"id":"https://openalex.org/C2776175482","wikidata":"https://www.wikidata.org/wiki/Q1195816","display_name":"Transfer (computing)","level":2,"score":0.3066999912261963},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.30079999566078186},{"id":"https://openalex.org/C2779679103","wikidata":"https://www.wikidata.org/wiki/Q5251805","display_name":"Degradation (telecommunications)","level":2,"score":0.27129998803138733},{"id":"https://openalex.org/C44154836","wikidata":"https://www.wikidata.org/wiki/Q45045","display_name":"Simulation","level":1,"score":0.2667999863624573},{"id":"https://openalex.org/C207685749","wikidata":"https://www.wikidata.org/wiki/Q2088941","display_name":"Domain knowledge","level":2,"score":0.2581999897956848},{"id":"https://openalex.org/C173246807","wikidata":"https://www.wikidata.org/wiki/Q7833062","display_name":"Trajectory optimization","level":3,"score":0.2556000053882599},{"id":"https://openalex.org/C87833898","wikidata":"https://www.wikidata.org/wiki/Q1060280","display_name":"Advanced driver assistance systems","level":2,"score":0.2540000081062317},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.25200000405311584},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.25040000677108765}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.00402","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.00402","pdf_url":null,"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":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2604.00402","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.00402","pdf_url":null,"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":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9","score":0.6330713629722595}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Developing":[0],"robust":[1],"models":[2,63,170],"to":[3,13,58,91,152],"accurately":[4],"predict":[5],"the":[6,23,40,79,130,134,137],"trajectories":[7],"of":[8,48,81],"surrounding":[9],"agents":[10],"is":[11],"fundamental":[12],"autonomous":[14,98],"driving":[15,46,99],"safety.":[16],"However,":[17],"most":[18],"public":[19],"datasets,":[20],"such":[21],"as":[22],"Waymo":[24],"Open":[25],"Motion":[26],"Dataset":[27],"and":[28,36,45,113,142],"Argoverse,":[29],"are":[30,68],"collected":[31],"in":[32,70,171],"Western":[33,66],"road":[34,93],"environments":[35],"do":[37],"not":[38],"reflect":[39],"unique":[41],"traffic":[42],"patterns,":[43],"infrastructure,":[44],"behaviors":[47],"other":[49],"regions,":[50],"including":[51],"South":[52],"Korea.":[53],"This":[54,156],"domain":[55],"discrepancy":[56],"leads":[57],"performance":[59],"degradation":[60],"when":[61,86],"state-of-the-art":[62],"trained":[64],"on":[65],"data":[67,90],"deployed":[69],"different":[71],"geographic":[72,173],"contexts.":[73],"In":[74],"this":[75],"work,":[76],"we":[77,101],"investigate":[78],"adaptability":[80],"Query-Centric":[82],"Trajectory":[83],"Prediction":[84],"(QCNet)":[85],"transferred":[87],"from":[88,109,154],"U.S.-based":[89],"Korean":[92,97],"environments.":[94],"Using":[95],"a":[96],"dataset,":[100],"compare":[102],"four":[103],"training":[104,108,143,153],"strategies:":[105],"zero-shot":[106],"transfer,":[107],"scratch,":[110],"full":[111],"fine-tuning,":[112],"encoder":[114,135],"freezing.":[115],"Experimental":[116],"results":[117],"demonstrate":[118],"that":[119],"leveraging":[120],"pretrained":[121],"knowledge":[122],"significantly":[123],"improves":[124],"prediction":[125,146,169],"performance.":[126],"Specifically,":[127],"selectively":[128],"fine-tuning":[129],"decoder":[131],"while":[132],"freezing":[133],"yields":[136],"best":[138],"trade-off":[139],"between":[140],"accuracy":[141],"efficiency,":[144],"reducing":[145],"error":[147],"by":[148],"over":[149],"66%":[150],"compared":[151],"scratch.":[155],"study":[157],"provides":[158],"practical":[159],"insights":[160],"into":[161],"effective":[162],"transfer":[163],"learning":[164],"strategies":[165],"for":[166],"deploying":[167],"trajectory":[168],"new":[172],"domains.":[174]},"counts_by_year":[],"updated_date":"2026-04-03T16:44:17.987007","created_date":"2026-04-03T00:00:00"}