{"id":"https://openalex.org/W4415221926","doi":"https://doi.org/10.1109/lra.2025.3621971","title":"Sce2DriveX: A Generalized MLLM Framework for Scene-to-Drive Learning","display_name":"Sce2DriveX: A Generalized MLLM Framework for Scene-to-Drive Learning","publication_year":2025,"publication_date":"2025-10-15","ids":{"openalex":"https://openalex.org/W4415221926","doi":"https://doi.org/10.1109/lra.2025.3621971"},"language":"en","primary_location":{"id":"doi:10.1109/lra.2025.3621971","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lra.2025.3621971","pdf_url":null,"source":{"id":"https://openalex.org/S4210169774","display_name":"IEEE Robotics and Automation Letters","issn_l":"2377-3766","issn":["2377-3766"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Robotics and Automation Letters","raw_type":"journal-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/A5024414161","display_name":"Rui Zhao","orcid":"https://orcid.org/0000-0003-1597-1961"},"institutions":[{"id":"https://openalex.org/I194450716","display_name":"Jilin University","ror":"https://ror.org/00js3aw79","country_code":"CN","type":"education","lineage":["https://openalex.org/I194450716"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Rui Zhao","raw_affiliation_strings":["School of Automotive Engineering, Jilin University, Changchun, China"],"raw_orcid":"https://orcid.org/0000-0003-1597-1961","affiliations":[{"raw_affiliation_string":"School of Automotive Engineering, Jilin University, Changchun, China","institution_ids":["https://openalex.org/I194450716"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101386710","display_name":"Qirui Yuan","orcid":null},"institutions":[{"id":"https://openalex.org/I194450716","display_name":"Jilin University","ror":"https://ror.org/00js3aw79","country_code":"CN","type":"education","lineage":["https://openalex.org/I194450716"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qirui Yuan","raw_affiliation_strings":["School of Automotive Engineering, Jilin University, Changchun, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Automotive Engineering, Jilin University, Changchun, China","institution_ids":["https://openalex.org/I194450716"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Jinyu Li","orcid":"https://orcid.org/0009-0001-8463-7732"},"institutions":[{"id":"https://openalex.org/I194450716","display_name":"Jilin University","ror":"https://ror.org/00js3aw79","country_code":"CN","type":"education","lineage":["https://openalex.org/I194450716"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jinyu Li","raw_affiliation_strings":["School of Automotive Engineering, Jilin University, Changchun, China"],"raw_orcid":"https://orcid.org/0009-0001-8463-7732","affiliations":[{"raw_affiliation_string":"School of Automotive Engineering, Jilin University, Changchun, China","institution_ids":["https://openalex.org/I194450716"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5053609325","display_name":"Haofeng Hu","orcid":"https://orcid.org/0009-0001-0124-6040"},"institutions":[{"id":"https://openalex.org/I194450716","display_name":"Jilin University","ror":"https://ror.org/00js3aw79","country_code":"CN","type":"education","lineage":["https://openalex.org/I194450716"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haofeng Hu","raw_affiliation_strings":["School of Automotive Engineering, Jilin University, Changchun, China"],"raw_orcid":"https://orcid.org/0009-0001-0124-6040","affiliations":[{"raw_affiliation_string":"School of Automotive Engineering, Jilin University, Changchun, China","institution_ids":["https://openalex.org/I194450716"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5071303232","display_name":"Yun Li","orcid":"https://orcid.org/0009-0002-7824-7751"},"institutions":[{"id":"https://openalex.org/I148798404","display_name":"Tokyo University of Technology","ror":"https://ror.org/021a26605","country_code":"JP","type":"education","lineage":["https://openalex.org/I148798404"]},{"id":"https://openalex.org/I161296585","display_name":"Tokyo University of Science","ror":"https://ror.org/05sj3n476","country_code":"JP","type":"education","lineage":["https://openalex.org/I161296585"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Yun Li","raw_affiliation_strings":["School of Information and Science Technology, The University of Tokyo, Tokyo, Japan"],"raw_orcid":"https://orcid.org/0009-0002-7824-7751","affiliations":[{"raw_affiliation_string":"School of Information and Science Technology, The University of Tokyo, Tokyo, Japan","institution_ids":["https://openalex.org/I161296585","https://openalex.org/I148798404"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5022668758","display_name":"Zhenhai Gao","orcid":"https://orcid.org/0000-0002-4623-3956"},"institutions":[{"id":"https://openalex.org/I194450716","display_name":"Jilin University","ror":"https://ror.org/00js3aw79","country_code":"CN","type":"education","lineage":["https://openalex.org/I194450716"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhenhai Gao","raw_affiliation_strings":["School of Automotive Engineering and the National Key Laboratory of Automotive Chassis Integration and Bionics, Jilin University, Changchun, China"],"raw_orcid":"https://orcid.org/0000-0002-4623-3956","affiliations":[{"raw_affiliation_string":"School of Automotive Engineering and the National Key Laboratory of Automotive Chassis Integration and Bionics, Jilin University, Changchun, China","institution_ids":["https://openalex.org/I194450716"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101688555","display_name":"Fei Gao","orcid":"https://orcid.org/0000-0001-9020-6720"},"institutions":[{"id":"https://openalex.org/I194450716","display_name":"Jilin University","ror":"https://ror.org/00js3aw79","country_code":"CN","type":"education","lineage":["https://openalex.org/I194450716"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fei Gao","raw_affiliation_strings":["School of Automotive Engineering and the National Key Laboratory of Automotive Chassis Integration and Bionics, Jilin University, Changchun, China"],"raw_orcid":"https://orcid.org/0000-0001-9020-6720","affiliations":[{"raw_affiliation_string":"School of Automotive Engineering and the National Key Laboratory of Automotive Chassis Integration and Bionics, Jilin University, Changchun, China","institution_ids":["https://openalex.org/I194450716"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":7,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":7.0351,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.96802638,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":95,"max":98},"biblio":{"volume":"10","issue":"12","first_page":"12580","last_page":"12587"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12535","display_name":"Machine Learning and Data Classification","score":0.9613999724388123,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.9613999724388123,"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/T12761","display_name":"Data Stream Mining Techniques","score":0.9531000256538391,"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/T10462","display_name":"Reinforcement Learning in Robotics","score":0.9453999996185303,"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/generalization","display_name":"Generalization","score":0.6144999861717224},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.550000011920929},{"id":"https://openalex.org/keywords/construct","display_name":"Construct (python library)","score":0.5234000086784363},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.5009999871253967},{"id":"https://openalex.org/keywords/motion","display_name":"Motion (physics)","score":0.47940000891685486},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.45100000500679016},{"id":"https://openalex.org/keywords/perception","display_name":"Perception","score":0.4456000030040741},{"id":"https://openalex.org/keywords/visual-reasoning","display_name":"Visual reasoning","score":0.4246000051498413},{"id":"https://openalex.org/keywords/task-analysis","display_name":"Task analysis","score":0.41290000081062317}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7300999760627747},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.6144999861717224},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6049000024795532},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.550000011920929},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.5234000086784363},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.5009999871253967},{"id":"https://openalex.org/C104114177","wikidata":"https://www.wikidata.org/wiki/Q79782","display_name":"Motion (physics)","level":2,"score":0.47940000891685486},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.45100000500679016},{"id":"https://openalex.org/C26760741","wikidata":"https://www.wikidata.org/wiki/Q160402","display_name":"Perception","level":2,"score":0.4456000030040741},{"id":"https://openalex.org/C2777508537","wikidata":"https://www.wikidata.org/wiki/Q7936620","display_name":"Visual reasoning","level":2,"score":0.4246000051498413},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.41290000081062317},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.40400001406669617},{"id":"https://openalex.org/C2775924081","wikidata":"https://www.wikidata.org/wiki/Q55608371","display_name":"Control (management)","level":2,"score":0.3912999927997589},{"id":"https://openalex.org/C100609095","wikidata":"https://www.wikidata.org/wiki/Q1335050","display_name":"Embodied cognition","level":2,"score":0.36230000853538513},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3312999904155731},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.31360000371932983},{"id":"https://openalex.org/C170494330","wikidata":"https://www.wikidata.org/wiki/Q1778434","display_name":"Cognitive map","level":3,"score":0.31060001254081726},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.30709999799728394},{"id":"https://openalex.org/C169900460","wikidata":"https://www.wikidata.org/wiki/Q2200417","display_name":"Cognition","level":2,"score":0.2919999957084656},{"id":"https://openalex.org/C178253425","wikidata":"https://www.wikidata.org/wiki/Q162668","display_name":"Visual perception","level":3,"score":0.29170000553131104},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.2825999855995178},{"id":"https://openalex.org/C2780660688","wikidata":"https://www.wikidata.org/wiki/Q25052564","display_name":"Multimodal learning","level":2,"score":0.2639000117778778},{"id":"https://openalex.org/C18555067","wikidata":"https://www.wikidata.org/wiki/Q8375051","display_name":"Joint (building)","level":2,"score":0.26100000739097595},{"id":"https://openalex.org/C28006648","wikidata":"https://www.wikidata.org/wiki/Q6934509","display_name":"Multi-task learning","level":3,"score":0.26089999079704285},{"id":"https://openalex.org/C192327766","wikidata":"https://www.wikidata.org/wiki/Q1038799","display_name":"Cognitive robotics","level":3,"score":0.2606000006198883},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.2605000138282776},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.25929999351501465},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.25870001316070557}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/lra.2025.3621971","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lra.2025.3621971","pdf_url":null,"source":{"id":"https://openalex.org/S4210169774","display_name":"IEEE Robotics and Automation Letters","issn_l":"2377-3766","issn":["2377-3766"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Robotics and Automation Letters","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2822289035","display_name":null,"funder_award_id":"52572475","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"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":16,"referenced_works":["https://openalex.org/W1956340063","https://openalex.org/W2101105183","https://openalex.org/W2950697717","https://openalex.org/W2964220823","https://openalex.org/W2968008415","https://openalex.org/W3174177541","https://openalex.org/W4312396550","https://openalex.org/W4386076400","https://openalex.org/W4390872423","https://openalex.org/W4393148430","https://openalex.org/W4401386967","https://openalex.org/W4401416408","https://openalex.org/W4402353994","https://openalex.org/W4402727764","https://openalex.org/W4404784276","https://openalex.org/W4409366257"],"related_works":[],"abstract_inverted_index":{"End-to-end":[0],"autonomous":[1,141],"driving,":[2,135,198],"which":[3,162],"directly":[4],"maps":[5,102],"raw":[6],"sensor":[7],"inputs":[8],"to":[9,38,69,77,103,179,196],"low-level":[10,46],"vehicle":[11,83],"controls,":[12],"is":[13],"an":[14],"crucial":[15],"part":[16],"of":[17,92],"Embodied":[18],"AI.":[19],"Despite":[20],"successes":[21],"in":[22,133,204],"applying":[23],"Multimodal":[24],"Large":[25],"Language":[26],"Models":[27],"(MLLMs)":[28],"for":[29,164],"high-level":[30],"traffic":[31],"scene":[32,75,94,116,194],"semantic":[33],"understanding,":[34],"it":[35,126],"remains":[36],"challenging":[37],"effectively":[39],"translate":[40],"these":[41],"conceptual":[42],"semantics":[43],"understandings":[44],"into":[45],"motion":[47,80],"control":[48,84],"commands":[49],"and":[50,55,82,96,109,118,121,143,168,172],"achieve":[51,70],"cross-scene":[52,123],"driving":[53,64,85,142,159,207],"generalization":[54,203],"consensus.":[56],"We":[57],"propose":[58],"Sce2DriveX,":[59],"a":[60,174],"human-like":[61],"chain-of-thought":[62],"(CoT)":[63],"reasoning":[65,119],"MLLM":[66],"framework,":[67],"designed":[68],"progressive":[71],"learning":[72,91],"from":[73,193],"multi-view":[74],"understanding":[76,167,195],"behavior":[78],"analysis,":[79],"planning,":[81],"process.":[86],"Sce2DriveX":[87,187],"utilizes":[88],"multimodal":[89],"joint":[90],"local":[93],"videos":[95],"global":[97],"Bird's":[98],"Eye":[99],"View":[100],"(BEV)":[101],"deeply":[104],"understand":[105],"long-range":[106],"spatiotemporal":[107],"relationships":[108],"road":[110],"topology,":[111],"enhancing":[112,137],"its":[113],"3D":[114,165],"dynamic/static":[115],"perception":[117],"capabilities":[120],"achieving":[122],"generalization.":[124],"Meanwhile,":[125],"reconstructs":[127],"the":[128,138,152,210],"implicit":[129],"cognitive":[130],"chain":[131],"inherent":[132],"human":[134,144],"further":[136],"consensus":[139],"between":[140],"thought.":[145],"To":[146],"improve":[147],"model":[148],"performance,":[149],"we":[150],"construct":[151],"first":[153],"comprehensive":[154],"Visual":[155],"Question":[156],"Answering":[157],"(VQA)":[158],"instruction":[160],"dataset,":[161],"tailored":[163],"spatial":[166],"long-axis":[169],"task":[170],"reasoning,":[171],"introduce":[173],"task-oriented":[175],"three-stage":[176],"training":[177],"pipeline":[178],"support":[180],"supervised":[181],"fine-tuning.":[182],"Extensive":[183],"experiments":[184],"demonstrate":[185],"that":[186],"achieves":[188],"state-of-the-art":[189],"performance":[190],"across":[191],"tasks":[192],"end-to-end":[197],"as":[199,201],"well":[200],"robust":[202],"handling":[205],"diverse":[206],"scenes":[208],"on":[209],"CARLA":[211],"Bench2Drive":[212],"benchmark.":[213]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":3}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-16T00:00:00"}