{"id":"https://openalex.org/W3210033960","doi":null,"title":"CTP-Net For Cross-Domain Trajectory Prediction","display_name":"CTP-Net For Cross-Domain Trajectory Prediction","publication_year":2021,"publication_date":"2021-10-22","ids":{"openalex":"https://openalex.org/W3210033960","mag":"3210033960"},"language":"en","primary_location":{"id":"mag:3210033960","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2110.11645","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"arXiv (Cornell University)","raw_type":null},"volume":null,"type":"preprint","is_traditional":true,"indexed_in":[],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"http://arxiv.org/abs/2110.11645","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5022336702","display_name":"Pingxuan Huang","orcid":null,"observed_orcids":[]},"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":"Pingxuan Huang","raw_affiliation_strings":["\u2020University of Michigan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"\u2020University of Michigan","institution_ids":["https://openalex.org/I27837315"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5002275591","display_name":"Yanyan Fang","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yanyan Fang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5083412798","display_name":"Bo Hu","orcid":"https://orcid.org/0000-0001-6348-010X","observed_orcids":["https://orcid.org/0000-0001-6348-010X"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bo Hu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5034339267","display_name":"Shenghua Gao","orcid":"https://orcid.org/0000-0003-1626-2040","observed_orcids":["https://orcid.org/0000-0003-1626-2040"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shenghua Gao","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0003-1626-2040","affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5100336782","display_name":"Jing Li","orcid":"https://orcid.org/0000-0001-7027-7513","observed_orcids":["https://orcid.org/0000-0001-7027-7513","https://orcid.org/0000-0001-5664-9907"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jing Li","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0001-7027-7513","affiliations":[]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":1,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9442999958992004,"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"}},"topics":[{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9442999958992004,"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"}},{"id":"https://openalex.org/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.04450000077486038,"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/T10462","display_name":"Reinforcement Learning in Robotics","score":0.002099999925121665,"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/trajectory-prediction","display_name":"trajectory prediction","score":0.8360000252723694},{"id":"https://openalex.org/keywords/cross-domain-adaptation","display_name":"cross-domain adaptation","score":0.7260000109672546},{"id":"https://openalex.org/keywords/domain-adaptation","display_name":"domain adaptation","score":0.9860000014305115},{"id":"https://openalex.org/keywords/adversarial-regularization","display_name":"adversarial regularization","score":0.7329999804496765},{"id":"https://openalex.org/keywords/trajectory-consistency","display_name":"trajectory consistency","score":0.4819999933242798}],"concepts":[{"id":"https://openalex.org/C2779803651","wikidata":"https://www.wikidata.org/wiki/Q5282088","display_name":"Discriminator","level":3,"score":0.8972793817520142},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.8191385269165039},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7803314924240112},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6373454332351685},{"id":"https://openalex.org/C175291020","wikidata":"https://www.wikidata.org/wiki/Q1156822","display_name":"Offset (computer science)","level":2,"score":0.619988203048706},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.6092040538787842},{"id":"https://openalex.org/C2776434776","wikidata":"https://www.wikidata.org/wiki/Q19246213","display_name":"Domain adaptation","level":3,"score":0.5654758810997009},{"id":"https://openalex.org/C66746571","wikidata":"https://www.wikidata.org/wiki/Q1134833","display_name":"ENCODE","level":3,"score":0.4991116523742676},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4971299469470978},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.47094690799713135},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3957967460155487},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.33016952872276306},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.11640989780426025},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"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/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"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/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.0},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"mag:3210033960","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2110.11645","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"arXiv (Cornell University)","raw_type":null}],"best_oa_location":{"id":"mag:3210033960","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2110.11645","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"arXiv (Cornell University)","raw_type":null},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":29,"referenced_works":["https://openalex.org/W304676660","https://openalex.org/W1565327149","https://openalex.org/W1967777429","https://openalex.org/W1970206276","https://openalex.org/W2096943734","https://openalex.org/W2159291411","https://openalex.org/W2164489414","https://openalex.org/W2424778531","https://openalex.org/W2467483865","https://openalex.org/W2511131004","https://openalex.org/W2518708963","https://openalex.org/W2532516272","https://openalex.org/W2770645414","https://openalex.org/W2799059904","https://openalex.org/W2805102305","https://openalex.org/W2892051223","https://openalex.org/W2943516367","https://openalex.org/W2948971113","https://openalex.org/W2949407705","https://openalex.org/W2962879692","https://openalex.org/W2963168418","https://openalex.org/W2963353290","https://openalex.org/W2963818059","https://openalex.org/W2963888093","https://openalex.org/W2967177252","https://openalex.org/W2974402281","https://openalex.org/W3035054225","https://openalex.org/W3035692480","https://openalex.org/W3102532606"],"related_works":["https://openalex.org/W4312682601","https://openalex.org/W4406611378","https://openalex.org/W4408280909","https://openalex.org/W4391759873","https://openalex.org/W4403420259","https://openalex.org/W4407742864","https://openalex.org/W4392903935","https://openalex.org/W4391833588","https://openalex.org/W4391770579","https://openalex.org/W4405717454"],"abstract_inverted_index":{"Deep":[0],"learning":[1],"based":[2],"trajectory":[3,66,127,147,159],"prediction":[4,148],"methods":[5],"rely":[6],"on":[7,155],"large":[8],"amount":[9],"of":[10,86,141],"annotated":[11,53],"future":[12,126],"trajectories,":[13],"but":[14],"may":[15],"not":[16],"generalize":[17],"well":[18,151],"to":[19,46,57,81,122,129],"a":[20,32,70,95,115],"new":[21,36],"scenario":[22,37],"captured":[23],"by":[24,94],"another":[25],"camera.":[26],"Meanwhile,":[27],"annotating":[28],"trajectories":[29,56,85,106,110],"for":[30,34,65,146,158],"training":[31],"network":[33],"this":[35],"is":[38,44,120],"time-consuming":[39],"and":[40,89,107],"expensive,":[41],"therefore":[42],"it":[43],"desirable":[45],"adapt":[47],"the":[48,52,58,83,101,104,108,112,125,133,139,142],"model":[49],"trained":[50],"with":[51,132],"source":[54],"domain":[55,63,117,144,156],"target":[59,113,116],"domain.":[60],"To":[61],"tackle":[62],"adaptation":[64,145,157],"prediction,":[67],"we":[68],"propose":[69],"Cross-domain":[71],"Trajectory":[72],"Prediction":[73],"Network":[74],"(CTP-Net),":[75],"in":[76,111],"which":[77],"LSTMs":[78],"are":[79,92],"used":[80],"encode":[82],"observed":[84,105,134],"both":[87],"domain,":[88,114],"their":[90],"features":[91],"aligned":[93],"cross-domain":[96],"feature":[97],"discriminator.":[98],"Further,":[99],"considering":[100],"consistency":[102],"between":[103],"predicted":[109],"offset":[118],"discriminator":[119],"utilized":[121],"adversarially":[123],"regularize":[124],"predictions":[128],"be":[130],"consistent":[131],"trajectories.":[135],"Extensive":[136],"experiments":[137],"demonstrate":[138],"effectiveness":[140],"proposed":[143],"setting":[149],"as":[150,152],"our":[153],"method":[154],"prediction.":[160]},"counts_by_year":[{"year":2022,"cited_by_count":1}],"updated_date":"2026-11-04T10:54:05.112534","created_date":"2021-11-08T00:00:00"}
