{"id":"https://openalex.org/W7151321034","doi":"https://doi.org/10.48550/arxiv.2604.04797","title":"Multi-Modal Sensor Fusion using Hybrid Attention for Autonomous Driving","display_name":"Multi-Modal Sensor Fusion using Hybrid Attention for Autonomous Driving","publication_year":2026,"publication_date":"2026-04-06","ids":{"openalex":"https://openalex.org/W7151321034","doi":"https://doi.org/10.48550/arxiv.2604.04797"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.04797","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.04797","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":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2604.04797","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5132881253","display_name":"Mayank Mayank","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mayank, Mayank","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5046915434","display_name":"Bharanidhar Duraisamy","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Duraisamy, Bharanidhar","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5082409317","display_name":"Florian Gei\u00df","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gei\u00df, Florian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5133122753","display_name":"Abhinav Valada","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Valada, Abhinav","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/T11038","display_name":"Advanced SAR Imaging Techniques","score":0.5360000133514404,"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"}},"topics":[{"id":"https://openalex.org/T11038","display_name":"Advanced SAR Imaging Techniques","score":0.5360000133514404,"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"}},{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.1395999938249588,"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/T12153","display_name":"Advanced Optical Sensing Technologies","score":0.04479999840259552,"subfield":{"id":"https://openalex.org/subfields/3105","display_name":"Instrumentation"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/radar","display_name":"Radar","score":0.6606000065803528},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.5758000016212463},{"id":"https://openalex.org/keywords/fusion","display_name":"Fusion","score":0.5669999718666077},{"id":"https://openalex.org/keywords/sensor-fusion","display_name":"Sensor fusion","score":0.5392000079154968},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5123999714851379},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.4999000132083893},{"id":"https://openalex.org/keywords/range","display_name":"Range (aeronautics)","score":0.4507000148296356},{"id":"https://openalex.org/keywords/modality","display_name":"Modality (human\u2013computer interaction)","score":0.37130001187324524}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7675999999046326},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.7426000237464905},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7178999781608582},{"id":"https://openalex.org/C554190296","wikidata":"https://www.wikidata.org/wiki/Q47528","display_name":"Radar","level":2,"score":0.6606000065803528},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.5758000016212463},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.5669999718666077},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.5392000079154968},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5123999714851379},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.4999000132083893},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.4507000148296356},{"id":"https://openalex.org/C2780226545","wikidata":"https://www.wikidata.org/wiki/Q6888030","display_name":"Modality (human\u2013computer interaction)","level":2,"score":0.37130001187324524},{"id":"https://openalex.org/C134406370","wikidata":"https://www.wikidata.org/wiki/Q832005","display_name":"Radar engineering details","level":4,"score":0.3441999852657318},{"id":"https://openalex.org/C10929652","wikidata":"https://www.wikidata.org/wiki/Q7279985","display_name":"Radar imaging","level":3,"score":0.32749998569488525},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.32589998841285706},{"id":"https://openalex.org/C32283439","wikidata":"https://www.wikidata.org/wiki/Q1407014","display_name":"Radar tracker","level":3,"score":0.32010000944137573},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.30570000410079956},{"id":"https://openalex.org/C69744172","wikidata":"https://www.wikidata.org/wiki/Q860822","display_name":"Image fusion","level":3,"score":0.30239999294281006},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.2653000056743622},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.2567000091075897},{"id":"https://openalex.org/C141353440","wikidata":"https://www.wikidata.org/wiki/Q182221","display_name":"Fuse (electrical)","level":2,"score":0.2558000087738037}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.04797","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.04797","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":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2604.04797","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.04797","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":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Accurate":[0],"3D":[1],"object":[2,143],"detection":[3],"for":[4,41],"autonomous":[5],"driving":[6],"requires":[7],"complementary":[8],"sensors.":[9],"Cameras":[10],"provide":[11],"dense":[12],"semantics":[13],"but":[14],"unreliable":[15],"depth,":[16],"while":[17],"millimeter-wave":[18],"radar":[19,50,64,117],"offers":[20],"precise":[21],"range":[22],"and":[23,60,71,85,118,133,151],"velocity":[24],"measurements":[25],"with":[26,68,111],"sparse":[27],"geometry.":[28],"We":[29,79],"propose":[30],"MMF-BEV,":[31],"a":[32,55,61,75],"radar-camera":[33],"BEV":[34],"fusion":[35,119,139],"framework":[36],"that":[37,127],"leverages":[38],"deformable":[39],"attention":[40],"cross-modal":[42],"feature":[43],"alignment":[44],"on":[45,124],"the":[46,108,147],"View-of-Delft":[47],"(VoD)":[48],"4D":[49],"dataset":[51],"[1].":[52],"MMF-BEV":[53,128],"builds":[54],"BEVDepth":[56],"[2]":[57],"camera":[58,109],"branch":[59,110],"RadarBEVNet":[62],"[3]":[63],"branch,":[65],"each":[66],"enhanced":[67],"Deformable":[69,76],"Self-Attention,":[70],"fuses":[72],"them":[73],"via":[74],"Cross-Attention":[77],"module.":[78],"evaluate":[80],"three":[81],"configurations:":[82],"camera-only,":[83],"radar-only,":[84],"hybrid":[86],"fusion.":[87],"A":[88,102],"sensor":[89,100],"contribution":[90],"analysis":[91],"quantifies":[92],"per-distance":[93],"modality":[94],"weighting,":[95],"providing":[96],"interpretable":[97],"evidence":[98],"of":[99,154],"complementarity.":[101],"two-stage":[103],"training":[104,116],"strategy":[105],"-":[106],"pre-training":[107],"depth":[112],"supervision,":[113],"then":[114],"jointly":[115],"modules":[120],"stabilizes":[121],"learning.":[122],"Experiments":[123],"VoD":[125],"show":[126],"consistently":[129],"outperforms":[130],"unimodal":[131],"baselines":[132],"achieves":[134],"competitive":[135],"results":[136],"against":[137],"prior":[138],"methods":[140],"across":[141],"all":[142],"classes":[144],"in":[145],"both":[146],"full":[148],"annotated":[149],"area":[150],"near-range":[152],"Region":[153],"Interest.":[155]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-04-08T00:00:00"}
