{"id":"https://openalex.org/W7125768543","doi":"https://doi.org/10.1109/tits.2026.3654084","title":"Pedestrian Crossing Intention Prediction Based on Cross-Modal Motion Query and Global\u2013Local Context Co-Learning for Autonomous Driving","display_name":"Pedestrian Crossing Intention Prediction Based on Cross-Modal Motion Query and Global\u2013Local Context Co-Learning for Autonomous Driving","publication_year":2026,"publication_date":"2026-01-27","ids":{"openalex":"https://openalex.org/W7125768543","doi":"https://doi.org/10.1109/tits.2026.3654084"},"language":null,"primary_location":{"id":"doi:10.1109/tits.2026.3654084","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tits.2026.3654084","pdf_url":null,"source":{"id":"https://openalex.org/S144771191","display_name":"IEEE Transactions on Intelligent Transportation Systems","issn_l":"1524-9050","issn":["1524-9050","1558-0016"],"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 Transactions on Intelligent Transportation Systems","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":null,"display_name":"Chenxi Lin","orcid":"https://orcid.org/0000-0001-7734-6761"},"institutions":[{"id":"https://openalex.org/I76569877","display_name":"Southeast University","ror":"https://ror.org/04ct4d772","country_code":"CN","type":"education","lineage":["https://openalex.org/I76569877"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chenxi Lin","raw_affiliation_strings":["Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Southeast University, Nanjing, China"],"raw_orcid":"https://orcid.org/0000-0001-7734-6761","affiliations":[{"raw_affiliation_string":"Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Southeast University, Nanjing, China","institution_ids":["https://openalex.org/I76569877"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5123973855","display_name":"Xiaojian Hu","orcid":null},"institutions":[{"id":"https://openalex.org/I76569877","display_name":"Southeast University","ror":"https://ror.org/04ct4d772","country_code":"CN","type":"education","lineage":["https://openalex.org/I76569877"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaojian Hu","raw_affiliation_strings":["Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Southeast University, Nanjing, China"],"raw_orcid":"https://orcid.org/0000-0001-5799-0568","affiliations":[{"raw_affiliation_string":"Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Southeast University, Nanjing, China","institution_ids":["https://openalex.org/I76569877"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I76569877"],"apc_list":null,"apc_paid":null,"fwci":5.2101,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.90929544,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":"27","issue":"5","first_page":"5804","last_page":"5819"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.8913999795913696,"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":0.8913999795913696,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.02759999968111515,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.014700000174343586,"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/pedestrian","display_name":"Pedestrian","score":0.7505999803543091},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5856999754905701},{"id":"https://openalex.org/keywords/motion","display_name":"Motion (physics)","score":0.5734000205993652},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5109999775886536},{"id":"https://openalex.org/keywords/perception","display_name":"Perception","score":0.4359999895095825},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.4284999966621399},{"id":"https://openalex.org/keywords/spatial-contextual-awareness","display_name":"Spatial contextual awareness","score":0.39899998903274536}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7645999789237976},{"id":"https://openalex.org/C2777113093","wikidata":"https://www.wikidata.org/wiki/Q221488","display_name":"Pedestrian","level":2,"score":0.7505999803543091},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5964000225067139},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5856999754905701},{"id":"https://openalex.org/C104114177","wikidata":"https://www.wikidata.org/wiki/Q79782","display_name":"Motion (physics)","level":2,"score":0.5734000205993652},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5109999775886536},{"id":"https://openalex.org/C26760741","wikidata":"https://www.wikidata.org/wiki/Q160402","display_name":"Perception","level":2,"score":0.4359999895095825},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.4284999966621399},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4146000146865845},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.4083999991416931},{"id":"https://openalex.org/C64754055","wikidata":"https://www.wikidata.org/wiki/Q7574053","display_name":"Spatial contextual awareness","level":2,"score":0.39899998903274536},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3628999888896942},{"id":"https://openalex.org/C183322885","wikidata":"https://www.wikidata.org/wiki/Q17007702","display_name":"Context model","level":3,"score":0.3176000118255615},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.30660000443458557},{"id":"https://openalex.org/C178253425","wikidata":"https://www.wikidata.org/wiki/Q162668","display_name":"Visual perception","level":3,"score":0.30379998683929443},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.296099990606308},{"id":"https://openalex.org/C146159030","wikidata":"https://www.wikidata.org/wiki/Q7625099","display_name":"Structure from motion","level":3,"score":0.28630000352859497},{"id":"https://openalex.org/C87833898","wikidata":"https://www.wikidata.org/wiki/Q1060280","display_name":"Advanced driver assistance systems","level":2,"score":0.25609999895095825}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tits.2026.3654084","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tits.2026.3654084","pdf_url":null,"source":{"id":"https://openalex.org/S144771191","display_name":"IEEE Transactions on Intelligent Transportation Systems","issn_l":"1524-9050","issn":["1524-9050","1558-0016"],"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 Transactions on Intelligent Transportation Systems","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11","score":0.7830178737640381}],"awards":[{"id":"https://openalex.org/G262025747","display_name":null,"funder_award_id":"52272344","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G4551343182","display_name":null,"funder_award_id":"BE2023802","funder_id":"https://openalex.org/F4320327777","funder_display_name":"Jiangsu Provincial Key Research and Development Program"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320327777","display_name":"Jiangsu Provincial Key Research and Development Program","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Ensuring":[0],"safety":[1],"in":[2,10,55],"autonomous":[3],"driving":[4],"requires":[5],"accurate":[6],"pedestrian":[7,15,122],"intention":[8],"prediction":[9],"complex":[11],"urban":[12],"environments.":[13],"Nevertheless,":[14],"motion":[16,36,92,112,145,164],"is":[17],"inherently":[18],"influenced":[19],"by":[20],"environmental":[21],"factors":[22],"and":[23,41,59,79,124,171,190],"visual":[24,43],"appearance,":[25],"which":[26,75],"poses":[27],"a":[28,104,114,125],"significant":[29],"challenge.":[30],"Although":[31],"existing":[32],"multi-modal":[33,163,179],"methods":[34],"integrate":[35],"data":[37,93],"with":[38,147,177,201],"both":[39,77],"global":[40,80,132],"local":[42,78,121],"cues":[44],"during":[45,82],"training,":[46],"applying":[47],"the":[48,69,154,160,168,198],"same":[49],"model":[50],"structure":[51],"for":[52,84,94,109,119,130],"inference":[53],"results":[54],"high":[56],"computational":[57,192],"cost":[58],"reduces":[60],"real-time":[61,96,188],"performance.":[62],"To":[63],"address":[64],"this":[65],"limitation,":[66],"we":[67],"propose":[68],"Global-Local":[70],"Context":[71],"Co-learning":[72],"(GLCCL)":[73],"framework,":[74],"leverages":[76],"context":[81],"training":[83,137],"collaborative":[85],"learning":[86],"while":[87,185],"relying":[88],"solely":[89],"on":[90,167],"lightweight":[91],"efficient":[95],"inference.":[97],"Our":[98],"framework":[99,142],"integrates":[100],"three":[101],"key":[102],"components:":[103],"Cross-modal":[105],"Query":[106],"Module":[107,117,128],"(CQM)":[108],"modeling":[110],"vehicle-pedestrian":[111],"feature,":[113],"Local":[115],"Perception":[116],"(LPM)":[118],"extracting":[120],"features,":[123],"Global":[126],"Guidance":[127],"(GGM)":[129],"encoding":[131],"traffic":[133],"environment":[134],"descriptions.":[135],"Collaboratively":[136],"these":[138],"modules":[139],"enables":[140],"our":[141],"to":[143,158],"enrich":[144],"representations":[146],"global-local":[148],"contextual":[149],"information.":[150],"We":[151],"further":[152],"visualize":[153],"CQM\u2019s":[155],"cross-attention":[156],"matrices":[157],"interpret":[159],"interactions":[161],"of":[162],"features.":[165],"Experiments":[166],"public":[169],"PIE":[170],"JAAD":[172],"benchmarks":[173],"demonstrate":[174],"that,":[175],"compared":[176],"state-of-the-art":[178],"models,":[180],"GLCCL":[181],"achieves":[182],"competitive":[183],"performance":[184],"offering":[186],"superior":[187],"capabilities":[189],"lower":[191],"cost.":[193],"Extensive":[194],"ablation":[195],"studies":[196],"confirm":[197],"framework\u2019s":[199],"compatibility":[200],"diverse":[202],"sequence-learning":[203],"methods.":[204]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-05-08T13:12:06.581006","created_date":"2026-01-28T00:00:00"}