{"id":"https://openalex.org/W4416750895","doi":"https://doi.org/10.1109/iros60139.2025.11246391","title":"LEGO-Motion: Learning-Enhanced Grids with Occupancy Instance Modeling for Class-Agnostic Motion Prediction","display_name":"LEGO-Motion: Learning-Enhanced Grids with Occupancy Instance Modeling for Class-Agnostic Motion Prediction","publication_year":2025,"publication_date":"2025-10-19","ids":{"openalex":"https://openalex.org/W4416750895","doi":"https://doi.org/10.1109/iros60139.2025.11246391"},"language":null,"primary_location":{"id":"doi:10.1109/iros60139.2025.11246391","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iros60139.2025.11246391","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":null,"display_name":"Kangan Qian","orcid":null},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Kangan Qian","raw_affiliation_strings":["Tsinghua University,School of Vehicle and Mobility,Beijing,China"],"affiliations":[{"raw_affiliation_string":"Tsinghua University,School of Vehicle and Mobility,Beijing,China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5043682012","display_name":"Jinyu Miao","orcid":"https://orcid.org/0000-0001-8558-9173"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jinyu Miao","raw_affiliation_strings":["Tsinghua University,School of Vehicle and Mobility,Beijing,China"],"affiliations":[{"raw_affiliation_string":"Tsinghua University,School of Vehicle and Mobility,Beijing,China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5111257558","display_name":"Ziang Luo","orcid":null},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ziang Luo","raw_affiliation_strings":["Tsinghua University,School of Vehicle and Mobility,Beijing,China"],"affiliations":[{"raw_affiliation_string":"Tsinghua University,School of Vehicle and Mobility,Beijing,China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5012854401","display_name":"Fu Zheng","orcid":"https://orcid.org/0000-0001-6129-9251"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zheng Fu","raw_affiliation_strings":["Tsinghua University,School of Vehicle and Mobility,Beijing,China"],"affiliations":[{"raw_affiliation_string":"Tsinghua University,School of Vehicle and Mobility,Beijing,China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5052851316","display_name":"Jinchen Li","orcid":"https://orcid.org/0000-0003-3335-9303"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jinchen Li","raw_affiliation_strings":["Tsinghua University,School of Vehicle and Mobility,Beijing,China"],"affiliations":[{"raw_affiliation_string":"Tsinghua University,School of Vehicle and Mobility,Beijing,China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5114171391","display_name":"Yining Shi","orcid":null},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yining Shi","raw_affiliation_strings":["Tsinghua University,School of Vehicle and Mobility,Beijing,China"],"affiliations":[{"raw_affiliation_string":"Tsinghua University,School of Vehicle and Mobility,Beijing,China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102992438","display_name":"Yunlong Wang","orcid":"https://orcid.org/0009-0004-7557-6263"},"institutions":[{"id":"https://openalex.org/I4210119619","display_name":"Arbeitsgemeinschaft Dermatologische Onkologie","ror":"https://ror.org/01xfewa62","country_code":"DE","type":"healthcare","lineage":["https://openalex.org/I4210119619"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Yunlong Wang","raw_affiliation_strings":["AD Division of NIO Inc,China"],"affiliations":[{"raw_affiliation_string":"AD Division of NIO Inc,China","institution_ids":["https://openalex.org/I4210119619"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102957468","display_name":"Kun Jiang","orcid":"https://orcid.org/0000-0001-7282-172X"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kun Jiang","raw_affiliation_strings":["Tsinghua University,School of Vehicle and Mobility,Beijing,China"],"affiliations":[{"raw_affiliation_string":"Tsinghua University,School of Vehicle and Mobility,Beijing,China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5014512074","display_name":"Mengmeng Yang","orcid":"https://orcid.org/0000-0002-3294-6437"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Mengmeng Yang","raw_affiliation_strings":["Tsinghua University,School of Vehicle and Mobility,Beijing,China"],"affiliations":[{"raw_affiliation_string":"Tsinghua University,School of Vehicle and Mobility,Beijing,China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5009072257","display_name":"Diange Yang","orcid":"https://orcid.org/0000-0003-0825-5609"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Diange Yang","raw_affiliation_strings":["Tsinghua University,School of Vehicle and Mobility,Beijing,China"],"affiliations":[{"raw_affiliation_string":"Tsinghua University,School of Vehicle and Mobility,Beijing,China","institution_ids":["https://openalex.org/I99065089"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":10,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I99065089"],"apc_list":null,"apc_paid":null,"fwci":0.6289,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.75386693,"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":"14178","last_page":"14185"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.871399998664856,"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":0.871399998664856,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.026499999687075615,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.014800000004470348,"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":0.6665999889373779},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.6452999711036682},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5680000185966492},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.5486000180244446},{"id":"https://openalex.org/keywords/reliability","display_name":"Reliability (semiconductor)","score":0.5414000153541565},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.4964999854564667},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.4510999917984009},{"id":"https://openalex.org/keywords/structure-from-motion","display_name":"Structure from motion","score":0.40709999203681946}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7261000275611877},{"id":"https://openalex.org/C104114177","wikidata":"https://www.wikidata.org/wiki/Q79782","display_name":"Motion (physics)","level":2,"score":0.6665999889373779},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.6452999711036682},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6175000071525574},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5680000185966492},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.5486000180244446},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.5414000153541565},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.4964999854564667},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.4510999917984009},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4510999917984009},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4284000098705292},{"id":"https://openalex.org/C146159030","wikidata":"https://www.wikidata.org/wiki/Q7625099","display_name":"Structure from motion","level":3,"score":0.40709999203681946},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.39579999446868896},{"id":"https://openalex.org/C26760741","wikidata":"https://www.wikidata.org/wiki/Q160402","display_name":"Perception","level":2,"score":0.34290000796318054},{"id":"https://openalex.org/C10161872","wikidata":"https://www.wikidata.org/wiki/Q557891","display_name":"Motion estimation","level":2,"score":0.3402999937534332},{"id":"https://openalex.org/C171268870","wikidata":"https://www.wikidata.org/wiki/Q1486676","display_name":"GRASP","level":2,"score":0.3384999930858612},{"id":"https://openalex.org/C12713177","wikidata":"https://www.wikidata.org/wiki/Q1900281","display_name":"Perspective (graphical)","level":2,"score":0.30309998989105225},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.30309998989105225},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.29409998655319214},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.29260000586509705},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2596000134944916},{"id":"https://openalex.org/C188198153","wikidata":"https://www.wikidata.org/wiki/Q1613840","display_name":"Limiting","level":2,"score":0.25619998574256897},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.2556000053882599}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iros60139.2025.11246391","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iros60139.2025.11246391","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":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W78159342","https://openalex.org/W1507506748","https://openalex.org/W2222512263","https://openalex.org/W2949708697","https://openalex.org/W2962771259","https://openalex.org/W2963150697","https://openalex.org/W2968296999","https://openalex.org/W2971686478","https://openalex.org/W3034295100","https://openalex.org/W3035574168","https://openalex.org/W3167095230","https://openalex.org/W3175582404","https://openalex.org/W4221162784","https://openalex.org/W4293363567","https://openalex.org/W4312359105","https://openalex.org/W4312443676","https://openalex.org/W4385245566","https://openalex.org/W4386065821","https://openalex.org/W4393148279","https://openalex.org/W4393150401","https://openalex.org/W4395017503","https://openalex.org/W4399564041","https://openalex.org/W4402727531","https://openalex.org/W4405785615","https://openalex.org/W4405907309","https://openalex.org/W4405975757","https://openalex.org/W4413858428"],"related_works":[],"abstract_inverted_index":{"Accurate":[0],"spatial":[1],"and":[2,24,42,77,108,134,190],"motion":[3,49,67,117,137,154,192],"understanding":[4],"is":[5],"critical":[6],"for":[7,199],"autonomous":[8],"driving":[9],"systems.":[10,202],"While":[11],"object-level":[12],"perception":[13,201],"models":[14,101],"excel":[15],"in":[16,48,54,153,184],"structured":[17],"environments,":[18],"they":[19],"struggle":[20],"with":[21],"open-set":[22],"categories":[23],"often":[25],"lack":[26],"precise":[27],"geometric":[28],"representation.":[29],"Occupancy-based,":[30],"class-agnostic":[31,66],"methods":[32,83],"offer":[33],"better":[34],"scene":[35,188],"expressiveness":[36],"but":[37],"typically":[38],"ignore":[39],"inter-agent":[40],"interactions":[41,102],"fail":[43],"to":[44,130],"ensure":[45],"physical":[46],"consistency":[47,118],"predictions,":[50],"limiting":[51],"their":[52],"reliability":[53],"complex":[55],"traffic":[56],"scenarios.":[57],"In":[58],"this":[59],"paper,":[60],"we":[61],"propose":[62],"LEGO-Motion,":[63],"a":[64,149,174,196],"novel":[65],"prediction":[68,155],"framework":[69],"that":[70,84,146],"bridges":[71],"the":[72,95,110,142,158],"gap":[73],"between":[74],"instance-level":[75],"reasoning":[76],"occupancy-based":[78],"modeling.":[79],"Unlike":[80],"conventional":[81],"grid-based":[82],"treat":[85],"each":[86],"cell":[87],"independently,":[88],"LEGO-Motion":[89,147],"introduces":[90],"two":[91],"key":[92],"components:":[93],"(1)":[94],"Interaction-Augmented":[96],"Instance":[97],"Encoder":[98,113],"(IaIE),":[99],"which":[100,115],"among":[103],"dynamic":[104],"agents":[105],"via":[106],"cross-attention,":[107],"(2)":[109],"Instance-Enhanced":[111],"BEV":[112],"(IeBE),":[114],"improves":[116],"across":[119],"instances":[120],"through":[121],"multi-stage":[122],"feature":[123],"fusion.":[124],"These":[125,179],"components":[126],"enable":[127],"our":[128,168],"model":[129],"learn":[131],"semantically":[132],"coherent":[133],"physically":[135],"plausible":[136],"fields.":[138],"Extensive":[139],"experiments":[140],"on":[141,173],"nuScenes":[143],"dataset":[144],"show":[145],"achieves":[148],"around":[150],"6%":[151],"improvement":[152],"accuracy":[156],"over":[157],"previous":[159],"state-of-the-art,":[160],"while":[161],"maintaining":[162],"real-time":[163],"inference":[164],"at":[165],"21ms.":[166],"Moreover,":[167],"method":[169],"demonstrates":[170],"strong":[171],"generalization":[172],"proprietary":[175],"FMCW":[176],"LiDAR":[177],"benchmark.":[178],"results":[180],"validate":[181],"LEGO-Motion's":[182],"effectiveness":[183],"capturing":[185],"both":[186],"global":[187],"structure":[189],"fine-grained":[191],"dynamics,":[193],"making":[194],"it":[195],"promising":[197],"foundation":[198],"next-generation":[200]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-04-17T18:11:37.981687","created_date":"2025-11-28T00:00:00"}
