{"id":"https://openalex.org/W7160021054","doi":"https://doi.org/10.48550/arxiv.2605.00051","title":"Learning from the Unseen: Generative Data Augmentation for Geometric-Semantic Accident Anticipation","display_name":"Learning from the Unseen: Generative Data Augmentation for Geometric-Semantic Accident Anticipation","publication_year":2026,"publication_date":"2026-04-29","ids":{"openalex":"https://openalex.org/W7160021054","doi":"https://doi.org/10.48550/arxiv.2605.00051"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.00051","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.00051","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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.2605.00051","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5135156871","display_name":"Yanchen Guan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Guan, Yanchen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135247769","display_name":"Haicheng Liao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liao, Haicheng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135239142","display_name":"Chengyue Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Chengyue","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5082916240","display_name":"Xingcheng Liu","orcid":"https://orcid.org/0000-0002-1910-1376"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Xingcheng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135142469","display_name":"Jiaxun Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Jiaxun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135216166","display_name":"Keqiang Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Keqiang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5135164827","display_name":"Zhenning Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Zhenning","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/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.4756999909877777,"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":0.4756999909877777,"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":0.14319999516010284,"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":0.13120000064373016,"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/anticipation","display_name":"Anticipation (artificial intelligence)","score":0.7211999893188477},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.545799970626831},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5321999788284302},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.5120999813079834},{"id":"https://openalex.org/keywords/reliability","display_name":"Reliability (semiconductor)","score":0.5083000063896179},{"id":"https://openalex.org/keywords/semantic-feature","display_name":"Semantic feature","score":0.45210000872612},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.4133000075817108},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4075999855995178},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.38999998569488525}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7347000241279602},{"id":"https://openalex.org/C176777502","wikidata":"https://www.wikidata.org/wiki/Q4774623","display_name":"Anticipation (artificial intelligence)","level":2,"score":0.7211999893188477},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5856999754905701},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.545799970626831},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5321999788284302},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.527400016784668},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.5120999813079834},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.5083000063896179},{"id":"https://openalex.org/C2781122975","wikidata":"https://www.wikidata.org/wiki/Q16928266","display_name":"Semantic feature","level":2,"score":0.45210000872612},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.4133000075817108},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4075999855995178},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.38999998569488525},{"id":"https://openalex.org/C22367795","wikidata":"https://www.wikidata.org/wiki/Q7625208","display_name":"Structured prediction","level":2,"score":0.38420000672340393},{"id":"https://openalex.org/C2778827112","wikidata":"https://www.wikidata.org/wiki/Q22245680","display_name":"Feature engineering","level":3,"score":0.37610000371932983},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.36469998955726624},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.36309999227523804},{"id":"https://openalex.org/C2780428219","wikidata":"https://www.wikidata.org/wiki/Q16952335","display_name":"Cover (algebra)","level":2,"score":0.35830000042915344},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.3580000102519989},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.3492000102996826},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.33230000734329224},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.328900009393692},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.3264999985694885},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.31139999628067017},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2985999882221222},{"id":"https://openalex.org/C2780440489","wikidata":"https://www.wikidata.org/wiki/Q5227278","display_name":"Data-driven","level":2,"score":0.2888000011444092},{"id":"https://openalex.org/C90312973","wikidata":"https://www.wikidata.org/wiki/Q7449052","display_name":"Semantic data model","level":2,"score":0.2653000056743622}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.00051","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.00051","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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.2605.00051","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.00051","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":"Good health and well-being","id":"https://metadata.un.org/sdg/3","score":0.8056833148002625}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Anticipating":[0],"traffic":[1,128],"accidents":[2],"is":[3],"a":[4,37,46,81,109,121],"critical":[5],"yet":[6],"unresolved":[7],"problem":[8],"for":[9],"autonomous":[10,165],"driving,":[11],"hindered":[12],"by":[13,52],"the":[14,24,41,69,76,102,149,152,162],"inherent":[15],"complexity":[16],"of":[17,27,72,104,124,151,164],"modeling":[18],"interactions":[19],"between":[20],"road":[21],"users":[22],"and":[23,61,95,127,134,144,160],"limited":[25],"availability":[26],"diverse,":[28],"large-scale":[29],"datasets.":[30],"To":[31,100],"address":[32],"these":[33],"issues,":[34],"we":[35,44,79,107],"propose":[36],"dual-path":[38],"framework.":[39],"On":[40,75],"one":[42],"hand,":[43,78],"employ":[45],"video":[47,117],"synthesis":[48],"pipeline":[49],"that,":[50],"guided":[51],"structured":[53],"prompts,":[54],"derives":[55],"feature":[56],"distributions":[57],"from":[58],"existing":[59,132],"corpora":[60],"produces":[62],"high-fidelity":[63],"synthetic":[64],"driving":[65,166],"scenes":[66],"consistent":[67],"with":[68,86],"statistical":[70],"patterns":[71],"real":[73],"data.":[74],"other":[77],"design":[80],"graph":[82],"neural":[83],"network":[84],"enriched":[85],"semantic":[87,96],"cues,":[88],"enabling":[89],"dynamic":[90],"reasoning":[91],"over":[92],"both":[93,142],"spatial":[94],"relations":[97],"among":[98],"participants.":[99],"validate":[101],"effectiveness":[103],"our":[105,135],"approach,":[106],"release":[108],"new":[110,136],"benchmark":[111,137],"dataset":[112],"containing":[113],"standardized,":[114],"finely":[115],"annotated":[116],"sequences":[118],"that":[119],"cover":[120],"broad":[122],"spectrum":[123],"regions,":[125],"weather,":[126],"conditions.":[129],"Evaluations":[130],"across":[131],"datasets":[133],"confirm":[138],"notable":[139],"gains":[140],"in":[141],"accuracy":[143],"anticipation":[145],"lead":[146],"time,":[147],"highlighting":[148],"capacity":[150],"proposed":[153],"framework":[154],"to":[155],"mitigate":[156],"current":[157],"data":[158],"bottlenecks":[159],"enhance":[161],"reliability":[163],"systems.":[167]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-05T00:00:00"}
