{"id":"https://openalex.org/W7164055227","doi":"https://doi.org/10.48550/arxiv.2606.09142","title":"Decoding Pedestrian Crossing Intention from Egocentric Vision via Vision Language Models","display_name":"Decoding Pedestrian Crossing Intention from Egocentric Vision via Vision Language Models","publication_year":2026,"publication_date":"2026-06-08","ids":{"openalex":"https://openalex.org/W7164055227","doi":"https://doi.org/10.48550/arxiv.2606.09142"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.09142","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.09142","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,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2606.09142","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5130354268","display_name":"Danya Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Danya","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138241046","display_name":"Xiang Su","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Su, Xiang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138273692","display_name":"Yan Feng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Feng, Yan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5038885838","display_name":"Rico Krueger","orcid":"https://orcid.org/0000-0002-5372-741X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Krueger, Rico","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.8172000050544739,"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"}},"topics":[{"id":"https://openalex.org/T11714","display_name":"Multimodal Machine Learning Applications","score":0.8172000050544739,"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":0.05590000003576279,"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/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.04270000010728836,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/gaze","display_name":"Gaze","score":0.6955000162124634},{"id":"https://openalex.org/keywords/pedestrian","display_name":"Pedestrian","score":0.6636000275611877},{"id":"https://openalex.org/keywords/decoding-methods","display_name":"Decoding methods","score":0.5358999967575073},{"id":"https://openalex.org/keywords/perception","display_name":"Perception","score":0.5040000081062317},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.44940000772476196},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.42100000381469727},{"id":"https://openalex.org/keywords/task-analysis","display_name":"Task analysis","score":0.3481999933719635},{"id":"https://openalex.org/keywords/visual-perception","display_name":"Visual perception","score":0.34689998626708984}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7193999886512756},{"id":"https://openalex.org/C2779916870","wikidata":"https://www.wikidata.org/wiki/Q14467155","display_name":"Gaze","level":2,"score":0.6955000162124634},{"id":"https://openalex.org/C2777113093","wikidata":"https://www.wikidata.org/wiki/Q221488","display_name":"Pedestrian","level":2,"score":0.6636000275611877},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.576200008392334},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.5358999967575073},{"id":"https://openalex.org/C26760741","wikidata":"https://www.wikidata.org/wiki/Q160402","display_name":"Perception","level":2,"score":0.5040000081062317},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.44940000772476196},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.42100000381469727},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.42010000348091125},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.37439998984336853},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.35670000314712524},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.3481999933719635},{"id":"https://openalex.org/C178253425","wikidata":"https://www.wikidata.org/wiki/Q162668","display_name":"Visual perception","level":3,"score":0.34689998626708984},{"id":"https://openalex.org/C56461940","wikidata":"https://www.wikidata.org/wiki/Q970687","display_name":"Eye tracking","level":2,"score":0.3456000089645386},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.33959999680519104},{"id":"https://openalex.org/C3017944768","wikidata":"https://www.wikidata.org/wiki/Q1450463","display_name":"Poison control","level":2,"score":0.3253999948501587},{"id":"https://openalex.org/C104114177","wikidata":"https://www.wikidata.org/wiki/Q79782","display_name":"Motion (physics)","level":2,"score":0.30709999799728394},{"id":"https://openalex.org/C153050134","wikidata":"https://www.wikidata.org/wiki/Q760256","display_name":"Eye movement","level":2,"score":0.2985999882221222},{"id":"https://openalex.org/C15123163","wikidata":"https://www.wikidata.org/wiki/Q500096","display_name":"Psychophysics","level":3,"score":0.2935999929904938},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.2921999990940094},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.27219998836517334},{"id":"https://openalex.org/C200220432","wikidata":"https://www.wikidata.org/wiki/Q7936208","display_name":"Vision science","level":2,"score":0.26919999718666077}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.09142","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.09142","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,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2606.09142","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.09142","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,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16","score":0.812030553817749}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Egocentric":[0],"vision":[1,53],"offers":[2],"a":[3,44,71,118,123,161,170],"first-person":[4],"view":[5],"of":[6,27,67,173],"human":[7],"perception":[8],"and":[9,51,116,140,157],"decision":[10],"making,":[11],"yet":[12],"its":[13],"potential":[14],"for":[15,176],"traffic-safety":[16],"prediction":[17],"remains":[18],"underexplored.":[19],"In":[20,147],"this":[21,38],"work,":[22],"we":[23,93,128],"study":[24],"the":[25,41,59,98,108,149,166,174],"decoding":[26],"pedestrian":[28,178],"crossing":[29],"intentions":[30],"from":[31],"short":[32],"egocentric":[33,177],"video":[34],"clips.":[35],"We":[36,62],"approach":[37],"by":[39,90,154],"formulating":[40],"task":[42,100],"as":[43],"closed-ended":[45],"visual":[46],"question":[47],"answering":[48],"(VQA)":[49],"problem":[50],"leveraging":[52],"language":[54],"models":[55,110],"(VLMs)":[56],"to":[57,97],"predict":[58],"pedestrians'":[60],"intent.":[61],"first":[63],"benchmark":[64],"three":[65],"families":[66],"state-of-the-art":[68],"VLMs":[69,96],"in":[70],"zero-shot":[72,114],"setting,":[73],"finding":[74],"that":[75,107,130],"they":[76],"achieve":[77,117],"moderate":[78],"gains":[79],"over":[80,122,165],"random":[81],"guessing":[82],"but":[83],"exhibit":[84],"limited":[85],"higher-level":[86],"traffic":[87],"reasoning.":[88],"Motivated":[89],"these":[91],"findings,":[92],"further":[94,143],"adapt":[95],"target":[99],"using":[101],"parameter-efficient":[102],"fine-tuning.":[103],"Our":[104],"results":[105],"show":[106],"fine-tuned":[109,150],"substantially":[111],"outperform":[112],"their":[113],"counterparts":[115],"9\\%":[119],"accuracy":[120,163],"improvement":[121,164],"specialized":[124],"transformer-based":[125],"baseline.":[126],"Finally,":[127],"demonstrate":[129],"incorporating":[131],"additional":[132],"contextual":[133],"cues,":[134],"including":[135],"ego":[136,158],"motion,":[137,139],"vehicle":[138],"eye":[141,155],"gaze,":[142],"improves":[144],"predictive":[145],"performance.":[146],"particular,":[148],"Qwen3-VL-2B":[151],"model":[152],"guided":[153],"gaze":[156],"motion":[159],"achieves":[160],"14.5%":[162],"transformer":[167],"baseline,":[168],"establishing":[169],"new":[171],"state":[172],"art":[175],"intent":[179],"decoding.":[180]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-06-10T00:00:00"}
