{"id":"https://openalex.org/W7138961461","doi":"https://doi.org/10.1109/amc67705.2026.11435851","title":"Robust Lane Tracking in Non-Standard Road Conditions using Confidence-Adaptive EKF","display_name":"Robust Lane Tracking in Non-Standard Road Conditions using Confidence-Adaptive EKF","publication_year":2026,"publication_date":"2026-03-09","ids":{"openalex":"https://openalex.org/W7138961461","doi":"https://doi.org/10.1109/amc67705.2026.11435851"},"language":null,"primary_location":{"id":"doi:10.1109/amc67705.2026.11435851","is_oa":false,"landing_page_url":"https://doi.org/10.1109/amc67705.2026.11435851","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2026 IEEE 19th International Conference on Advanced Motion Control (AMC)","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/A5130053693","display_name":"Bumyeon Lee","orcid":null},"institutions":[{"id":"https://openalex.org/I52010207","display_name":"Keimyung University","ror":"https://ror.org/00tjv0s33","country_code":"KR","type":"education","lineage":["https://openalex.org/I52010207"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Bumyeon Lee","raw_affiliation_strings":["Keimyung University,Dept. of Mechanical Engineering,Daegu,South Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Keimyung University,Dept. of Mechanical Engineering,Daegu,South Korea","institution_ids":["https://openalex.org/I52010207"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129962336","display_name":"Jongbin Kim","orcid":null},"institutions":[{"id":"https://openalex.org/I52010207","display_name":"Keimyung University","ror":"https://ror.org/00tjv0s33","country_code":"KR","type":"education","lineage":["https://openalex.org/I52010207"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Jongbin Kim","raw_affiliation_strings":["Keimyung University,Dept. of Automotive Engineering,Daegu,South Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Keimyung University,Dept. of Automotive Engineering,Daegu,South Korea","institution_ids":["https://openalex.org/I52010207"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5123037105","display_name":"Junghyun Choi","orcid":null},"institutions":[{"id":"https://openalex.org/I52010207","display_name":"Keimyung University","ror":"https://ror.org/00tjv0s33","country_code":"KR","type":"education","lineage":["https://openalex.org/I52010207"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Junghyun Choi","raw_affiliation_strings":["Keimyung University,Dept. of Automotive Engineering,Daegu,South Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Keimyung University,Dept. of Automotive Engineering,Daegu,South Korea","institution_ids":["https://openalex.org/I52010207"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I52010207"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.27686275,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"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/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.9776999950408936,"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":0.9776999950408936,"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/T10805","display_name":"Vehicle Dynamics and Control Systems","score":0.005799999926239252,"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/T10524","display_name":"Traffic control and management","score":0.003000000026077032,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems 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/kalman-filter","display_name":"Kalman filter","score":0.5501000285148621},{"id":"https://openalex.org/keywords/extended-kalman-filter","display_name":"Extended Kalman filter","score":0.5478000044822693},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.5162000060081482},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.5142999887466431},{"id":"https://openalex.org/keywords/tracking","display_name":"Tracking (education)","score":0.5081999897956848},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.43849998712539673},{"id":"https://openalex.org/keywords/tracking-system","display_name":"Tracking system","score":0.4343999922275543},{"id":"https://openalex.org/keywords/covariance-matrix","display_name":"Covariance matrix","score":0.42829999327659607}],"concepts":[{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.7150999903678894},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7023000121116638},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6625000238418579},{"id":"https://openalex.org/C157286648","wikidata":"https://www.wikidata.org/wiki/Q846780","display_name":"Kalman filter","level":2,"score":0.5501000285148621},{"id":"https://openalex.org/C206833254","wikidata":"https://www.wikidata.org/wiki/Q5421817","display_name":"Extended Kalman filter","level":3,"score":0.5478000044822693},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.5162000060081482},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.5142999887466431},{"id":"https://openalex.org/C2775936607","wikidata":"https://www.wikidata.org/wiki/Q466845","display_name":"Tracking (education)","level":2,"score":0.5081999897956848},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.43849998712539673},{"id":"https://openalex.org/C154586513","wikidata":"https://www.wikidata.org/wiki/Q4420972","display_name":"Tracking system","level":3,"score":0.4343999922275543},{"id":"https://openalex.org/C185142706","wikidata":"https://www.wikidata.org/wiki/Q1134404","display_name":"Covariance matrix","level":2,"score":0.42829999327659607},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.3903000056743622},{"id":"https://openalex.org/C178650346","wikidata":"https://www.wikidata.org/wiki/Q201984","display_name":"Covariance","level":2,"score":0.36160001158714294},{"id":"https://openalex.org/C90119067","wikidata":"https://www.wikidata.org/wiki/Q43260","display_name":"Polynomial","level":2,"score":0.33820000290870667},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.31869998574256897},{"id":"https://openalex.org/C87833898","wikidata":"https://www.wikidata.org/wiki/Q1060280","display_name":"Advanced driver assistance systems","level":2,"score":0.30219998955726624},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.28299999237060547},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.28130000829696655},{"id":"https://openalex.org/C56461940","wikidata":"https://www.wikidata.org/wiki/Q970687","display_name":"Eye tracking","level":2,"score":0.27549999952316284},{"id":"https://openalex.org/C2777891301","wikidata":"https://www.wikidata.org/wiki/Q3475123","display_name":"Navigation system","level":2,"score":0.2734000086784363},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.25699999928474426}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/amc67705.2026.11435851","is_oa":false,"landing_page_url":"https://doi.org/10.1109/amc67705.2026.11435851","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2026 IEEE 19th International Conference on Advanced Motion Control (AMC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.6949825286865234,"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W2036021531","https://openalex.org/W2106976646","https://openalex.org/W2118545852","https://openalex.org/W2145023731","https://openalex.org/W2159132531","https://openalex.org/W2336416123","https://openalex.org/W2780740184","https://openalex.org/W2785634791","https://openalex.org/W2964059111","https://openalex.org/W2964199920","https://openalex.org/W3014504385","https://openalex.org/W3176231046","https://openalex.org/W3211490618","https://openalex.org/W4313535464","https://openalex.org/W4388004784","https://openalex.org/W4401700293"],"related_works":[],"abstract_inverted_index":{"Detection":[0],"of":[1,159],"damaged":[2,164],"or":[3],"non-standard":[4,184],"lane":[5,26,54,69,77,120,165],"markings":[6],"is":[7,122],"a":[8,23,59,155],"critical":[9],"challenge":[10],"for":[11,51],"autonomous":[12,139],"driving":[13],"systems":[14],"and":[15,39,84,99,124,179],"automated":[16],"road":[17,185],"maintenance":[18],"robots.":[19],"This":[20],"paper":[21],"proposes":[22],"robust":[24],"real-time":[25,149],"tracking":[27,157],"system":[28,48,114,133],"that":[29,62,170],"combines":[30],"deep":[31],"learning-based":[32],"semantic":[33],"segmentation":[34],"with":[35,143,154],"graph-based":[36],"connectivity":[37],"analysis":[38],"the":[40,53,67,105,113,171],"confidence-adaptive":[41],"Extended":[42],"Kalman":[43],"Filter":[44],"(EKF).":[45],"The":[46,92,132],"proposed":[47,172],"uses":[49],"SegFormer":[50],"segmenting":[52],"in":[55,183],"pixels,":[56],"followed":[57],"by":[58,79],"graph":[60],"model":[61],"establishes":[63],"spatial":[64],"relationships":[65],"between":[66],"detected":[68],"segments.":[70,91],"A":[71],"mask":[72],"enhancement":[73],"module":[74],"reconstructs":[75],"discontinuous":[76],"regions":[78],"analyzing":[80],"window-wise":[81],"pixel":[82,97],"density":[83,98],"interpolating":[85],"gaps":[86],"based":[87],"on":[88,126,136,163],"neighboring":[89],"valid":[90],"confidence":[93],"score,":[94],"derived":[95],"from":[96],"polynomial":[100],"fit":[101],"quality,":[102],"dynamically":[103],"adapts":[104],"EKF\u2019s":[106],"measurement":[107],"noise":[108],"covariance":[109],"matrix":[110],"(R),":[111],"enabling":[112],"to":[115],"trust":[116],"visual":[117],"measurements":[118],"when":[119,129],"quality":[121,130],"high":[123],"rely":[125],"motion":[127],"predictions":[128],"degrades.":[131],"was":[134],"validated":[135],"an":[137],"embedded":[138],"vehicle":[140],"platform":[141],"equipped":[142],"NVIDIA":[144],"Jetson":[145],"AGX":[146],"Orin,":[147],"achieving":[148],"performance":[150],"at":[151],"40":[152],"Hz":[153],"lateral":[156],"error":[158],"0.64":[160],"cm":[161],"RMS":[162],"sections.":[166],"Experimental":[167],"results":[168],"demonstrate":[169],"method":[173],"significantly":[174],"outperforms":[175],"both":[176],"fixed-parameter":[177],"filtering":[178],"conventional":[180],"vision-based":[181],"approaches":[182],"conditions.":[186]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2026-03-20T00:00:00"}
