{"id":"https://openalex.org/W7222959634","doi":"https://doi.org/10.48550/arxiv.2610.23356","title":"STC-MGNet: Spatial-Temporal-Channel Attention-Enhanced Multimodal Graph Network for Pedestrian Intention Prediction","display_name":"STC-MGNet: Spatial-Temporal-Channel Attention-Enhanced Multimodal Graph Network for Pedestrian Intention Prediction","publication_year":2026,"publication_date":"2026-10-16","ids":{"openalex":"https://openalex.org/W7222959634","doi":"https://doi.org/10.48550/arxiv.2610.23356"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2610.23356","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2610.23356","pdf_url":"https://arxiv.org/pdf/2610.23356","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":null,"raw_type":"text"},"volume":null,"type":"preprint","is_traditional":true,"indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2610.23356","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5159082425","display_name":"Yuanzhe Li","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I4577782","display_name":"Technische Universit\u00e4t Berlin","ror":"https://ror.org/03v4gjf40","country_code":"DE","type":"education","lineage":["https://openalex.org/I4577782"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Li, Yuanzhe","raw_affiliation_strings":["Chair of Automotive Engineering Technische Universit\u00e4t Berlin Berlin , Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Chair of Automotive Engineering Technische Universit\u00e4t Berlin Berlin , Germany","institution_ids":["https://openalex.org/I4577782"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5159052412","display_name":"Meng Wang","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Meng","raw_affiliation_strings":[],"raw_orcid":null,"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":true,"cited_by_count":0,"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/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.899399995803833,"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":0.899399995803833,"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/T10812","display_name":"Human Pose and Action Recognition","score":0.04529999941587448,"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/T10370","display_name":"Traffic and Road Safety","score":0.026900000870227814,"subfield":{"id":"https://openalex.org/subfields/2213","display_name":"Safety, Risk, Reliability and Quality"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/pedestrian-intention-prediction","display_name":"pedestrian intention prediction","score":0.859000027179718},{"id":"https://openalex.org/keywords/attention-mechanism","display_name":"attention mechanism","score":0.5730000138282776},{"id":"https://openalex.org/keywords/autonomous-driving-safety","display_name":"autonomous driving safety","score":0.925000011920929},{"id":"https://openalex.org/keywords/pedestrian-vehicle-interaction","display_name":"pedestrian-vehicle interaction","score":0.9440000057220459}],"concepts":[{"id":"https://openalex.org/C2777113093","wikidata":"https://www.wikidata.org/wiki/Q221488","display_name":"Pedestrian","level":2,"score":0.7807999849319458},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7110000252723694},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6348999738693237},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.6324999928474426},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.5830000042915344},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5543000102043152},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.5266000032424927},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.4115999937057495},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3995000123977661},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3977999985218048},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.3871999979019165},{"id":"https://openalex.org/C2780156472","wikidata":"https://www.wikidata.org/wiki/Q2355550","display_name":"Pedestrian detection","level":3,"score":0.34130001068115234},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.3393999934196472},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.33079999685287476},{"id":"https://openalex.org/C104114177","wikidata":"https://www.wikidata.org/wiki/Q79782","display_name":"Motion (physics)","level":2,"score":0.3271999955177307},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.299699991941452},{"id":"https://openalex.org/C2993807640","wikidata":"https://www.wikidata.org/wiki/Q103709453","display_name":"Attention network","level":2,"score":0.29840001463890076},{"id":"https://openalex.org/C155542232","wikidata":"https://www.wikidata.org/wiki/Q736111","display_name":"Optical flow","level":3,"score":0.29750001430511475},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.2867000102996826},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.2777999937534332},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.2754000127315521},{"id":"https://openalex.org/C88230418","wikidata":"https://www.wikidata.org/wiki/Q131476","display_name":"Graph theory","level":2,"score":0.27469998598098755},{"id":"https://openalex.org/C176809094","wikidata":"https://www.wikidata.org/wiki/Q15401496","display_name":"Traverse","level":2,"score":0.2700999975204468},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.26080000400543213},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.251800000667572}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2610.23356","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2610.23356","pdf_url":"https://arxiv.org/pdf/2610.23356","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":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2610.23356","doi":"https://doi.org/10.48550/arxiv.2610.23356","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2610.23356","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":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2610.23356","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2610.23356","pdf_url":"https://arxiv.org/pdf/2610.23356","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":null,"raw_type":"text"},"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","score":0.5142999887466431,"id":"https://metadata.un.org/sdg/11"}],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7222959634.pdf","grobid_xml":"https://content.openalex.org/works/W7222959634.grobid-xml"},"referenced_works_count":2,"referenced_works":["https://openalex.org/W3022272795","https://openalex.org/W7106810501"],"related_works":["https://openalex.org/W7168393296","https://openalex.org/W4413148244","https://openalex.org/W7213675613","https://openalex.org/W7137267760","https://openalex.org/W4410427553","https://openalex.org/W7106810501","https://openalex.org/W4416078083","https://openalex.org/W4410114576","https://openalex.org/W4385497100","https://openalex.org/W4404724663"],"abstract_inverted_index":{"Accurate":[0],"prediction":[1,164],"of":[2,14,132,157],"pedestrian":[3,27,51,69],"crossing":[4,52],"intentions":[5],"plays":[6],"a":[7,43,57,102],"critical":[8],"role":[9],"in":[10,17,26],"enhancing":[11],"the":[12,138,154],"safety":[13],"pedestrian-vehicle":[15],"interactions":[16],"autonomous":[18],"driving.":[19],"However,":[20],"effectively":[21],"capturing":[22],"subtle":[23],"spatiotemporal":[24],"patterns":[25],"pose":[28,61,70,113],"sequences":[29],"and":[30,34,83,98,135,141,159],"integrating":[31],"complementary":[32,96,155],"visual":[33,97,158],"ego-vehicle":[35,99,160],"motion":[36,100,161],"information":[37,162],"remain":[38],"challenging.":[39],"This":[40],"paper":[41],"proposes":[42],"spatial-temporal-channel":[44,58],"attention-enhanced":[45,60],"multimodal":[46],"graph":[47,62,121],"network":[48],"(STC-MGNet)":[49],"for":[50],"intention":[53],"prediction.":[54],"STC-MGNet":[55,128],"introduces":[56],"(STC)":[59],"encoder":[63],"to":[64,110,163],"learn":[65],"discriminative":[66],"representations":[67,114],"from":[68],"sequences.":[71],"Three":[72],"parallel":[73],"attention":[74],"branches":[75],"selectively":[76],"emphasize":[77],"informative":[78],"skeletal":[79],"joints,":[80],"observation":[81],"frames,":[82],"feature":[84],"channels,":[85],"with":[86],"their":[87],"outputs":[88],"integrated":[89],"through":[90,115],"learnable":[91],"fusion":[92],"weights.":[93],"To":[94],"leverage":[95],"information,":[101],"multi-stage":[103],"cross-modal":[104],"channel":[105,117],"modulation":[106],"mechanism":[107],"is":[108],"introduced":[109],"adaptively":[111],"refine":[112],"modality-specific":[116],"gating":[118],"at":[119],"successive":[120],"encoding":[122],"stages.":[123],"Experimental":[124],"results":[125],"demonstrate":[126],"that":[127],"achieves":[129],"accuracy":[130],"rates":[131],"70%,":[133],"87%,":[134],"88%":[136],"on":[137],"JAADbeh,":[139],"JAADall,":[140],"PIE":[142],"datasets,":[143],"respectively,":[144],"outperforming":[145],"several":[146],"representative":[147],"baseline":[148],"methods.":[149],"Ablation":[150],"experiments":[151],"further":[152],"highlight":[153],"contributions":[156],"performance.":[165]},"counts_by_year":[],"updated_date":"2026-11-07T11:25:07.637180","created_date":"2026-10-20T00:00:00"}