{"id":"https://openalex.org/W7150847868","doi":"https://doi.org/10.48550/arxiv.2604.02930","title":"BEVPredFormer: Spatio-temporal Attention for BEV Instance Prediction in Autonomous Driving","display_name":"BEVPredFormer: Spatio-temporal Attention for BEV Instance Prediction in Autonomous Driving","publication_year":2026,"publication_date":"2026-04-03","ids":{"openalex":"https://openalex.org/W7150847868","doi":"https://doi.org/10.48550/arxiv.2604.02930"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.02930","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.02930","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2604.02930","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5133027431","display_name":"Miguel Antunes-Garc\u00eda","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Antunes-Garc\u00eda, Miguel","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5067490426","display_name":"Santiago Montiel-Mar\u00edn","orcid":"https://orcid.org/0009-0000-5492-0839"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Montiel-Mar\u00edn, Santiago","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133036332","display_name":"Fabio S\u00e1nchez-Garc\u00eda","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"S\u00e1nchez-Garc\u00eda, Fabio","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5067956094","display_name":"Rodrigo Guti\u00e9rrez-Moreno","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Guti\u00e9rrez-Moreno, Rodrigo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133029241","display_name":"Rafael Barea","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Barea, Rafael","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5077780013","display_name":"Luis M. Bergasa","orcid":"https://orcid.org/0000-0002-0087-3077"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bergasa, Luis M.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":6,"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":0.7904000282287598,"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":0.7904000282287598,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.057500001043081284,"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/T10191","display_name":"Robotics and Sensor-Based Localization","score":0.031599998474121094,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace 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/modular-design","display_name":"Modular design","score":0.5817000269889832},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.4778999984264374},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.42579999566078186},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.4101000130176544},{"id":"https://openalex.org/keywords/motion","display_name":"Motion (physics)","score":0.36410000920295715},{"id":"https://openalex.org/keywords/motion-estimation","display_name":"Motion estimation","score":0.36230000853538513},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.3587000072002411},{"id":"https://openalex.org/keywords/perception","display_name":"Perception","score":0.3544999957084656}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7287999987602234},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6535000205039978},{"id":"https://openalex.org/C101468663","wikidata":"https://www.wikidata.org/wiki/Q1620158","display_name":"Modular design","level":2,"score":0.5817000269889832},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.4778999984264374},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.462799996137619},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.42579999566078186},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.4101000130176544},{"id":"https://openalex.org/C104114177","wikidata":"https://www.wikidata.org/wiki/Q79782","display_name":"Motion (physics)","level":2,"score":0.36410000920295715},{"id":"https://openalex.org/C10161872","wikidata":"https://www.wikidata.org/wiki/Q557891","display_name":"Motion estimation","level":2,"score":0.36230000853538513},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.3587000072002411},{"id":"https://openalex.org/C26760741","wikidata":"https://www.wikidata.org/wiki/Q160402","display_name":"Perception","level":2,"score":0.3544999957084656},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.351500004529953},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3398999869823456},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.3345000147819519},{"id":"https://openalex.org/C511192102","wikidata":"https://www.wikidata.org/wiki/Q5156948","display_name":"Comprehension","level":2,"score":0.29840001463890076},{"id":"https://openalex.org/C57493831","wikidata":"https://www.wikidata.org/wiki/Q3134666","display_name":"Projection (relational algebra)","level":2,"score":0.29330000281333923},{"id":"https://openalex.org/C65909025","wikidata":"https://www.wikidata.org/wiki/Q1945033","display_name":"Monocular","level":2,"score":0.2842999994754791},{"id":"https://openalex.org/C159620131","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Spatial analysis","level":2,"score":0.258899986743927},{"id":"https://openalex.org/C187691185","wikidata":"https://www.wikidata.org/wiki/Q2020720","display_name":"Grid","level":2,"score":0.25850000977516174},{"id":"https://openalex.org/C81074085","wikidata":"https://www.wikidata.org/wiki/Q366872","display_name":"Motion planning","level":3,"score":0.257099986076355},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.2540000081062317},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.25189998745918274}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.02930","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.02930","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2604.02930","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.02930","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities","score":0.5087352395057678}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"A":[0],"robust":[1,205],"awareness":[2],"of":[3,24,80,94,99,133,143,183],"how":[4],"dynamic":[5,89],"scenes":[6],"evolve":[7],"is":[8],"essential":[9],"for":[10,118,204],"Autonomous":[11,208],"Driving":[12,209],"systems,":[13],"as":[14],"they":[15],"must":[16],"accurately":[17],"detect,":[18],"track,":[19],"and":[20,41,53,58,84,104,130,136,161,206],"predict":[21],"the":[22,77,81,134,144,181,190],"behaviour":[23],"surrounding":[25],"obstacles.":[26],"Traditional":[27],"perception":[28],"pipelines":[29],"that":[30,122,152,173],"rely":[31],"on":[32,138,189,195],"modular":[33],"architectures":[34,97],"tend":[35],"to":[36,127],"suffer":[37],"from":[38,65],"cumulative":[39],"errors":[40],"latency.":[42],"Instance":[43],"Prediction":[44],"models":[45,74],"provide":[46],"a":[47,69,114,149,168],"unified":[48],"solution,":[49],"performing":[50],"Bird's-Eye-View":[51],"segmentation":[52],"motion":[54,102],"estimation":[55],"across":[56],"current":[57],"future":[59],"frames":[60],"using":[61],"information":[62,86],"directly":[63],"obtained":[64],"different":[66],"sensors.":[67],"However,":[68],"key":[70],"challenge":[71],"in":[72,76,88],"these":[73],"lies":[75],"effective":[78],"processing":[79,126],"dense":[82],"spatial":[83,131],"temporal":[85,125,129,175],"inherent":[87],"driving":[90],"environments.":[91],"This":[92],"level":[93],"complexity":[95],"demands":[96],"capable":[98],"capturing":[100],"fine-grained":[101],"patterns":[103],"long-range":[105],"dependencies":[106],"without":[107],"compromising":[108],"real-time":[109],"performance.":[110],"We":[111],"introduce":[112],"BEVPredFormer,":[113],"novel":[115],"camera-only":[116],"architecture":[117],"BEV":[119],"instance":[120],"prediction":[121],"uses":[123],"attention-based":[124,140],"improve":[128],"comprehension":[132],"scene":[135],"relies":[137],"an":[139],"3D":[141],"projection":[142],"camera":[145],"information.":[146],"BEVPredFormer":[147,193],"employs":[148],"recurrent-free":[150],"design":[151],"incorporates":[153],"gated":[154],"transformer":[155],"layers,":[156],"divided":[157],"spatio-temporal":[158],"attention":[159],"mechanisms,":[160],"multi-scale":[162],"head":[163],"tasks.":[164],"Additionally,":[165],"we":[166],"incorporate":[167],"difference-guided":[169],"feature":[170],"extraction":[171],"module":[172],"enhances":[174],"representations.":[176],"Extensive":[177],"ablation":[178],"studies":[179],"validate":[180],"effectiveness":[182],"each":[184],"architectural":[185],"component.":[186],"When":[187],"evaluated":[188],"nuScenes":[191],"dataset,":[192],"was":[194],"par":[196],"or":[197],"surpassed":[198],"State-Of-The-Art":[199],"methods,":[200],"highlighting":[201],"its":[202],"potential":[203],"efficient":[207],"perception.":[210]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-04-07T00:00:00"}