{"id":"https://openalex.org/W4416961984","doi":"https://doi.org/10.1109/ist66504.2025.11268387","title":"A Re-Calibration Method for Object Detection with Multimodal Alignment Bias in Autonomous Driving","display_name":"A Re-Calibration Method for Object Detection with Multimodal Alignment Bias in Autonomous Driving","publication_year":2025,"publication_date":"2025-10-15","ids":{"openalex":"https://openalex.org/W4416961984","doi":"https://doi.org/10.1109/ist66504.2025.11268387"},"language":null,"primary_location":{"id":"doi:10.1109/ist66504.2025.11268387","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ist66504.2025.11268387","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Imaging Systems and Techniques (IST)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5007344622","display_name":"Zhihang Song","orcid":"https://orcid.org/0009-0006-1967-0639"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhihang Song","raw_affiliation_strings":["Tsinghua University,Department of Automation,Beijing"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University,Department of Automation,Beijing","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5060930959","display_name":"Dingyi Yao","orcid":null},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dingyi Yao","raw_affiliation_strings":["Tsinghua University,Department of Automation,Beijing"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University,Department of Automation,Beijing","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5039158347","display_name":"Ruibo Ming","orcid":"https://orcid.org/0009-0002-4530-1205"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ruibo Ming","raw_affiliation_strings":["Tsinghua University,Department of Automation,Beijing"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University,Department of Automation,Beijing","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5013482491","display_name":"Lihui Peng","orcid":"https://orcid.org/0000-0001-7363-6374"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lihui Peng","raw_affiliation_strings":["Tsinghua University,Department of Automation,Beijing"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University,Department of Automation,Beijing","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5081663090","display_name":"Danya Yao","orcid":"https://orcid.org/0000-0001-5032-6322"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Danya Yao","raw_affiliation_strings":["Tsinghua University,Department of Automation,Beijing"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University,Department of Automation,Beijing","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100388053","display_name":"Yi Zhang","orcid":"https://orcid.org/0000-0001-7201-2092"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yi Zhang","raw_affiliation_strings":["Tsinghua University,Department of Automation,Beijing"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University,Department of Automation,Beijing","institution_ids":["https://openalex.org/I99065089"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I99065089"],"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":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.7419999837875366,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.7419999837875366,"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":0.10220000147819519,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.06970000267028809,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/robustness","display_name":"Robustness (evolution)","score":0.7358999848365784},{"id":"https://openalex.org/keywords/calibration","display_name":"Calibration","score":0.6844000220298767},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.6402000188827515},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5149000287055969},{"id":"https://openalex.org/keywords/lidar","display_name":"Lidar","score":0.5091000199317932},{"id":"https://openalex.org/keywords/sensor-fusion","display_name":"Sensor fusion","score":0.4875999987125397},{"id":"https://openalex.org/keywords/fusion","display_name":"Fusion","score":0.42250001430511475},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.4049000144004822},{"id":"https://openalex.org/keywords/sensitivity","display_name":"Sensitivity (control systems)","score":0.397599995136261}],"concepts":[{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.7358999848365784},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.7330999970436096},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6996999979019165},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6887000203132629},{"id":"https://openalex.org/C165838908","wikidata":"https://www.wikidata.org/wiki/Q736777","display_name":"Calibration","level":2,"score":0.6844000220298767},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.6402000188827515},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5149000287055969},{"id":"https://openalex.org/C51399673","wikidata":"https://www.wikidata.org/wiki/Q504027","display_name":"Lidar","level":2,"score":0.5091000199317932},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.4875999987125397},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.42250001430511475},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.4049000144004822},{"id":"https://openalex.org/C21200559","wikidata":"https://www.wikidata.org/wiki/Q7451068","display_name":"Sensitivity (control systems)","level":2,"score":0.397599995136261},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.3700999915599823},{"id":"https://openalex.org/C110898773","wikidata":"https://www.wikidata.org/wiki/Q2933935","display_name":"Camera resectioning","level":2,"score":0.35350000858306885},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.3352000117301941},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.3179999887943268},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.3052000105381012},{"id":"https://openalex.org/C69744172","wikidata":"https://www.wikidata.org/wiki/Q860822","display_name":"Image fusion","level":3,"score":0.30489999055862427},{"id":"https://openalex.org/C19768560","wikidata":"https://www.wikidata.org/wiki/Q320727","display_name":"Dependency (UML)","level":2,"score":0.30399999022483826},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.30250000953674316},{"id":"https://openalex.org/C99404194","wikidata":"https://www.wikidata.org/wiki/Q163362","display_name":"Vanishing point","level":3,"score":0.29330000281333923},{"id":"https://openalex.org/C192299074","wikidata":"https://www.wikidata.org/wiki/Q2160034","display_name":"Robot calibration","level":5,"score":0.26080000400543213},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.2542000114917755},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.25040000677108765}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ist66504.2025.11268387","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ist66504.2025.11268387","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Imaging Systems and Techniques (IST)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":43,"referenced_works":["https://openalex.org/W2115579991","https://openalex.org/W2341309557","https://openalex.org/W2518401284","https://openalex.org/W2555618208","https://openalex.org/W2905528244","https://openalex.org/W2908599206","https://openalex.org/W2949708697","https://openalex.org/W2953941229","https://openalex.org/W2962888833","https://openalex.org/W2963270286","https://openalex.org/W2964062501","https://openalex.org/W2968296999","https://openalex.org/W3001225245","https://openalex.org/W3035461736","https://openalex.org/W3088256830","https://openalex.org/W3108426750","https://openalex.org/W3130463448","https://openalex.org/W3143550294","https://openalex.org/W3167095230","https://openalex.org/W3170030651","https://openalex.org/W3175380624","https://openalex.org/W3177330511","https://openalex.org/W3186678659","https://openalex.org/W3193939309","https://openalex.org/W3209639308","https://openalex.org/W4200134404","https://openalex.org/W4200629389","https://openalex.org/W4225986494","https://openalex.org/W4281753180","https://openalex.org/W4308080092","https://openalex.org/W4308080348","https://openalex.org/W4312419284","https://openalex.org/W4313149358","https://openalex.org/W4324116476","https://openalex.org/W4383066393","https://openalex.org/W4385804883","https://openalex.org/W4385804922","https://openalex.org/W4385975752","https://openalex.org/W4386076667","https://openalex.org/W4390874575","https://openalex.org/W4401415880","https://openalex.org/W4403841936","https://openalex.org/W4408564064"],"related_works":[],"abstract_inverted_index":{"Multi-modal":[0],"object":[1,180],"detection":[2,79,82,105,134,170,181],"in":[3,23,38,42,133,192],"autonomous":[4],"driving":[5],"has":[6],"achieved":[7],"great":[8],"breakthroughs":[9],"due":[10],"to":[11,35,129],"the":[12,49,52,73,99,102,117,131],"usage":[13],"of":[14,75,101],"fusing":[15],"complementary":[16],"information":[17],"from":[18],"different":[19],"sensors.":[20],"The":[21,163],"calibration":[22,44,64,76,86,114,146,176,198],"fusion":[24,78],"between":[25],"sensors":[26],"such":[27],"as":[28,148],"LiDAR":[29,139],"and":[30,59,107,144,157,178],"camera":[31,142],"was":[32],"always":[33],"supposed":[34],"be":[36],"precise":[37],"previous":[39],"work.":[40],"However,":[41],"reality,":[43],"matrices":[45],"are":[46],"fixed":[47],"when":[48],"vehicles":[50],"leave":[51],"factory,":[53],"but":[54],"mechanical":[55],"vibration,":[56],"road":[57],"bumps,":[58],"data":[60],"lags":[61],"may":[62],"cause":[63],"bias.":[65],"As":[66],"there":[67],"is":[68],"relatively":[69],"limited":[70],"research":[71],"on":[72,77,113],"impact":[74],"performance,":[80],"multi-sensor":[81],"methods":[83],"with":[84,168],"flexible":[85],"dependency":[87],"have":[88],"remained":[89],"a":[90,126,158,186],"key":[91],"objective.":[92],"In":[93],"this":[94,122],"paper,":[95],"we":[96,124],"systematically":[97],"evaluate":[98],"sensitivity":[100],"SOTA":[103],"EPNet++":[104],"framework":[106],"prove":[108],"that":[109],"even":[110],"slight":[111],"bias":[112,152,177],"can":[115,166],"reduce":[116],"performance":[118],"seriously.":[119],"To":[120],"address":[121],"vulnerability,":[123],"propose":[125],"re-calibration":[127,164],"model":[128,137,165],"re-calibrate":[130],"misalignment":[132],"tasks.":[135],"This":[136],"integrates":[138],"point":[140],"cloud,":[141],"image,":[143],"initial":[145],"matrix":[147],"inputs,":[149],"generating":[150],"re-calibrated":[151],"through":[153],"semantic":[154],"segmentation":[155],"guidance":[156],"tailored":[159],"loss":[160],"function":[161],"design.":[162],"operate":[167],"existing":[169],"algorithms,":[171],"enhancing":[172],"both":[173],"robustness":[174],"against":[175],"overall":[179],"performance.":[182],"Our":[183],"approach":[184],"establishes":[185],"foundational":[187],"methodology":[188],"for":[189],"maintaining":[190],"reliability":[191],"multi-modal":[193],"perception":[194],"systems":[195],"under":[196],"real-world":[197],"uncertainties.":[199]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-12-03T00:00:00"}
