{"id":"https://openalex.org/W4416749205","doi":"https://doi.org/10.1109/iros60139.2025.11246909","title":"Enhanced Motion Forecasting with Plug-and-Play Multimodal Large Language Models","display_name":"Enhanced Motion Forecasting with Plug-and-Play Multimodal Large Language Models","publication_year":2025,"publication_date":"2025-10-19","ids":{"openalex":"https://openalex.org/W4416749205","doi":"https://doi.org/10.1109/iros60139.2025.11246909"},"language":null,"primary_location":{"id":"doi:10.1109/iros60139.2025.11246909","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iros60139.2025.11246909","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","raw_type":"proceedings-article"},"type":"article","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/A5110725449","display_name":"Katie Luo","orcid":null},"institutions":[{"id":"https://openalex.org/I205783295","display_name":"Cornell University","ror":"https://ror.org/05bnh6r87","country_code":"US","type":"education","lineage":["https://openalex.org/I205783295"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Katie Luo","raw_affiliation_strings":["Cornell University,Computer and Information Sciences Department"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Cornell University,Computer and Information Sciences Department","institution_ids":["https://openalex.org/I205783295"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5019481782","display_name":"Jingwei Ji","orcid":"https://orcid.org/0000-0002-9527-8296"},"institutions":[{"id":"https://openalex.org/I4210145145","display_name":"Nomor Research (Germany)","ror":"https://ror.org/04727qm97","country_code":"DE","type":"company","lineage":["https://openalex.org/I4210145145"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Jingwei Ji","raw_affiliation_strings":["Waymo LLC"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Waymo LLC","institution_ids":["https://openalex.org/I4210145145"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100604494","display_name":"Tong He","orcid":"https://orcid.org/0000-0003-2772-9320"},"institutions":[{"id":"https://openalex.org/I4210145145","display_name":"Nomor Research (Germany)","ror":"https://ror.org/04727qm97","country_code":"DE","type":"company","lineage":["https://openalex.org/I4210145145"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Tong He","raw_affiliation_strings":["Waymo LLC"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Waymo LLC","institution_ids":["https://openalex.org/I4210145145"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100700826","display_name":"Runsheng Xu","orcid":"https://orcid.org/0000-0001-7375-9833"},"institutions":[{"id":"https://openalex.org/I4210145145","display_name":"Nomor Research (Germany)","ror":"https://ror.org/04727qm97","country_code":"DE","type":"company","lineage":["https://openalex.org/I4210145145"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Runsheng Xu","raw_affiliation_strings":["Waymo LLC"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Waymo LLC","institution_ids":["https://openalex.org/I4210145145"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5046598816","display_name":"Yichen Xie","orcid":"https://orcid.org/0000-0002-0974-5979"},"institutions":[{"id":"https://openalex.org/I134446601","display_name":"Berkeley College","ror":"https://ror.org/02xewxa75","country_code":"US","type":"education","lineage":["https://openalex.org/I134446601"]},{"id":"https://openalex.org/I95457486","display_name":"University of California, Berkeley","ror":"https://ror.org/01an7q238","country_code":"US","type":"education","lineage":["https://openalex.org/I95457486"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yichen Xie","raw_affiliation_strings":["UC Berkeley"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"UC Berkeley","institution_ids":["https://openalex.org/I134446601","https://openalex.org/I95457486"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5081024054","display_name":"Dragomir Anguelov","orcid":null},"institutions":[{"id":"https://openalex.org/I4210145145","display_name":"Nomor Research (Germany)","ror":"https://ror.org/04727qm97","country_code":"DE","type":"company","lineage":["https://openalex.org/I4210145145"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Dragomir Anguelov","raw_affiliation_strings":["Waymo LLC"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Waymo LLC","institution_ids":["https://openalex.org/I4210145145"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5110774377","display_name":"Mingxing Tan","orcid":null},"institutions":[{"id":"https://openalex.org/I4210145145","display_name":"Nomor Research (Germany)","ror":"https://ror.org/04727qm97","country_code":"DE","type":"company","lineage":["https://openalex.org/I4210145145"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Mingxing Tan","raw_affiliation_strings":["Waymo LLC"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Waymo LLC","institution_ids":["https://openalex.org/I4210145145"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5110725449"],"corresponding_institution_ids":["https://openalex.org/I205783295"],"apc_list":null,"apc_paid":null,"fwci":0.569,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.73509279,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"12510","last_page":"12517"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.625,"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":0.625,"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/T12290","display_name":"Human Motion and Animation","score":0.056299999356269836,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.040300000458955765,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/motion","display_name":"Motion (physics)","score":0.7360000014305115},{"id":"https://openalex.org/keywords/adaptation","display_name":"Adaptation (eye)","score":0.49639999866485596},{"id":"https://openalex.org/keywords/natural-language","display_name":"Natural language","score":0.41110000014305115},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.40709999203681946},{"id":"https://openalex.org/keywords/computational-model","display_name":"Computational model","score":0.3285999894142151},{"id":"https://openalex.org/keywords/predictive-modelling","display_name":"Predictive modelling","score":0.30640000104904175},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.3021000027656555}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7829999923706055},{"id":"https://openalex.org/C104114177","wikidata":"https://www.wikidata.org/wiki/Q79782","display_name":"Motion (physics)","level":2,"score":0.7360000014305115},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6463000178337097},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5446000099182129},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.49639999866485596},{"id":"https://openalex.org/C195324797","wikidata":"https://www.wikidata.org/wiki/Q33742","display_name":"Natural language","level":2,"score":0.41110000014305115},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.40709999203681946},{"id":"https://openalex.org/C66024118","wikidata":"https://www.wikidata.org/wiki/Q1122506","display_name":"Computational model","level":2,"score":0.3285999894142151},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.30640000104904175},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.3021000027656555},{"id":"https://openalex.org/C2983448237","wikidata":"https://www.wikidata.org/wiki/Q1078276","display_name":"Language understanding","level":2,"score":0.30000001192092896},{"id":"https://openalex.org/C2779439875","wikidata":"https://www.wikidata.org/wiki/Q1078276","display_name":"Natural language understanding","level":3,"score":0.28519999980926514},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.2777000069618225},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.2703000009059906},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.2685999870300293},{"id":"https://openalex.org/C2776608160","wikidata":"https://www.wikidata.org/wiki/Q4785462","display_name":"Natural (archaeology)","level":2,"score":0.2662000060081482},{"id":"https://openalex.org/C48007421","wikidata":"https://www.wikidata.org/wiki/Q676252","display_name":"Motion capture","level":3,"score":0.2612000107765198},{"id":"https://openalex.org/C2777036941","wikidata":"https://www.wikidata.org/wiki/Q6917771","display_name":"Motion analysis","level":2,"score":0.2596000134944916}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iros60139.2025.11246909","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iros60139.2025.11246909","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W2150066425","https://openalex.org/W2949708697","https://openalex.org/W2963001155","https://openalex.org/W2963759562","https://openalex.org/W2968008415","https://openalex.org/W2982745079","https://openalex.org/W2985871763","https://openalex.org/W3035574168","https://openalex.org/W3090789587","https://openalex.org/W3108486966","https://openalex.org/W3156216502","https://openalex.org/W3172477795","https://openalex.org/W3214950490","https://openalex.org/W4312894406","https://openalex.org/W4383172002","https://openalex.org/W4385346108","https://openalex.org/W4386076400","https://openalex.org/W4390872108","https://openalex.org/W4390873481","https://openalex.org/W4401386967","https://openalex.org/W4401416505","https://openalex.org/W4402772377","https://openalex.org/W4402781630","https://openalex.org/W4404769984","https://openalex.org/W4404820176"],"related_works":[],"abstract_inverted_index":{"Current":[0],"autonomous":[1],"driving":[2],"systems":[3],"rely":[4],"on":[5,55,130],"specialized":[6],"models":[7,46,51,135],"for":[8],"perceiving":[9],"and":[10,68,88,142],"predicting":[11],"motion,":[12],"which":[13],"demonstrate":[14],"reliable":[15],"performance":[16,148],"in":[17,114],"standard":[18],"conditions.":[19],"However,":[20],"generalizing":[21],"cost-effectively":[22],"to":[23,66,75,81,95,110,124],"diverse":[24],"real-world":[25],"scenarios":[26],"remains":[27],"a":[28,38,62],"significant":[29,112],"challenge.":[30],"To":[31],"address":[32],"this,":[33],"we":[34],"propose":[35],"Plug-and-Forecast":[36],"(PnF),":[37],"plug-and-play":[39],"approach":[40,129],"that":[41,58],"augments":[42],"existing":[43,97],"motion":[44,115,133],"forecasting":[45,134],"with":[47],"multimodal":[48],"large":[49],"language":[50,60],"(MLLMs).":[52],"PnF":[53],"builds":[54],"the":[56,104,137,143],"insight":[57],"natural":[59],"provides":[61],"more":[63],"effective":[64],"way":[65],"describe":[67],"handle":[69],"complex":[70],"scenarios,":[71],"enabling":[72],"quick":[73],"adaptation":[74],"targeted":[76],"behaviors.":[77],"We":[78,126],"design":[79],"prompts":[80],"extract":[82],"structured":[83],"scene":[84],"understanding":[85],"from":[86],"MLLMs":[87,109],"distill":[89],"this":[90],"information":[91],"into":[92],"learnable":[93],"embeddings":[94],"augment":[96],"behavior":[98],"prediction":[99,116],"models.":[100],"Our":[101],"method":[102],"leverages":[103],"zero-shot":[105],"reasoning":[106],"capabilities":[107],"of":[108],"achieve":[111],"improvements":[113,149],"performance,":[117],"while":[118],"requiring":[119],"no":[120],"fine-tuning\u2014making":[121],"it":[122],"practical":[123],"adopt.":[125],"validate":[127],"our":[128],"two":[131],"state-of-the-art":[132],"using":[136],"Waymo":[138],"Open":[139],"Motion":[140],"Dataset":[141],"nuScenes":[144],"Dataset,":[145],"demonstrating":[146],"consistent":[147],"across":[150],"both":[151],"benchmarks.":[152]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-05-12T08:28:47.272897","created_date":"2025-11-28T00:00:00"}
