{"id":"https://openalex.org/W2909268063","doi":"https://doi.org/10.1109/dicta.2018.8615784","title":"Driving Lane Detection Based on Recognition of Road Boundary Situation","display_name":"Driving Lane Detection Based on Recognition of Road Boundary Situation","publication_year":2018,"publication_date":"2018-12-01","ids":{"openalex":"https://openalex.org/W2909268063","doi":"https://doi.org/10.1109/dicta.2018.8615784","mag":"2909268063"},"language":"en","primary_location":{"id":"doi:10.1109/dicta.2018.8615784","is_oa":false,"landing_page_url":"https://doi.org/10.1109/dicta.2018.8615784","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 Digital Image Computing: Techniques and Applications (DICTA)","raw_type":"proceedings-article"},"type":"article","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/A5059364875","display_name":"Hiroyuki Komori","orcid":null},"institutions":[{"id":"https://openalex.org/I146516829","display_name":"Hirosaki University","ror":"https://ror.org/02syg0q74","country_code":"JP","type":"education","lineage":["https://openalex.org/I146516829"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Hiroyuki Komori","raw_affiliation_strings":["Hirosaki University, Hirosaki, Japan"],"affiliations":[{"raw_affiliation_string":"Hirosaki University, Hirosaki, Japan","institution_ids":["https://openalex.org/I146516829"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5015672702","display_name":"Kazunori Onoguchi","orcid":null},"institutions":[{"id":"https://openalex.org/I146516829","display_name":"Hirosaki University","ror":"https://ror.org/02syg0q74","country_code":"JP","type":"education","lineage":["https://openalex.org/I146516829"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Kazunori Onoguchi","raw_affiliation_strings":["Hirosaki University, Hirosaki, Japan"],"affiliations":[{"raw_affiliation_string":"Hirosaki University, Hirosaki, Japan","institution_ids":["https://openalex.org/I146516829"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5059364875"],"corresponding_institution_ids":["https://openalex.org/I146516829"],"apc_list":null,"apc_paid":null,"fwci":0.2949,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.64261138,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.9994999766349792,"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":0.9994999766349792,"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/T12707","display_name":"Vehicle License Plate Recognition","score":0.9922999739646912,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"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/T12549","display_name":"Image and Object Detection Techniques","score":0.9854000210762024,"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/boundary","display_name":"Boundary (topology)","score":0.7573353052139282},{"id":"https://openalex.org/keywords/boundary-line","display_name":"Boundary line","score":0.6536696553230286},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6497628688812256},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.6408301591873169},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5872535109519958},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5665975213050842},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.564878523349762},{"id":"https://openalex.org/keywords/line","display_name":"Line (geometry)","score":0.5622264742851257},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.48028799891471863},{"id":"https://openalex.org/keywords/road-surface","display_name":"Road surface","score":0.44338199496269226},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.2723729610443115},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.1985986828804016},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.15361949801445007},{"id":"https://openalex.org/keywords/geometry","display_name":"Geometry","score":0.09254088997840881}],"concepts":[{"id":"https://openalex.org/C62354387","wikidata":"https://www.wikidata.org/wiki/Q875399","display_name":"Boundary (topology)","level":2,"score":0.7573353052139282},{"id":"https://openalex.org/C2993043157","wikidata":"https://www.wikidata.org/wiki/Q4949967","display_name":"Boundary line","level":2,"score":0.6536696553230286},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6497628688812256},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6408301591873169},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5872535109519958},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5665975213050842},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.564878523349762},{"id":"https://openalex.org/C198352243","wikidata":"https://www.wikidata.org/wiki/Q37105","display_name":"Line (geometry)","level":2,"score":0.5622264742851257},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.48028799891471863},{"id":"https://openalex.org/C2780042925","wikidata":"https://www.wikidata.org/wiki/Q1049667","display_name":"Road surface","level":2,"score":0.44338199496269226},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.2723729610443115},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.1985986828804016},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.15361949801445007},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.09254088997840881},{"id":"https://openalex.org/C147176958","wikidata":"https://www.wikidata.org/wiki/Q77590","display_name":"Civil engineering","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/dicta.2018.8615784","is_oa":false,"landing_page_url":"https://doi.org/10.1109/dicta.2018.8615784","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 Digital Image Computing: Techniques and Applications (DICTA)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities","score":0.7599999904632568}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":29,"referenced_works":["https://openalex.org/W33116912","https://openalex.org/W561899398","https://openalex.org/W1533861849","https://openalex.org/W1686810756","https://openalex.org/W1795068553","https://openalex.org/W1967007359","https://openalex.org/W2003128603","https://openalex.org/W2095905764","https://openalex.org/W2097117768","https://openalex.org/W2131076267","https://openalex.org/W2142834259","https://openalex.org/W2146575011","https://openalex.org/W2150066425","https://openalex.org/W2159132531","https://openalex.org/W2166819896","https://openalex.org/W2279098554","https://openalex.org/W2332618872","https://openalex.org/W2505004417","https://openalex.org/W2511969587","https://openalex.org/W2739837873","https://openalex.org/W2764012408","https://openalex.org/W2769926989","https://openalex.org/W2789539512","https://openalex.org/W2796853305","https://openalex.org/W2962835968","https://openalex.org/W4293402673","https://openalex.org/W6615525469","https://openalex.org/W6631943919","https://openalex.org/W6637373629"],"related_works":["https://openalex.org/W2969228573","https://openalex.org/W4248799186","https://openalex.org/W2080454692","https://openalex.org/W2350866966","https://openalex.org/W110222820","https://openalex.org/W2360278453","https://openalex.org/W2382815780","https://openalex.org/W2764154546","https://openalex.org/W2308164229","https://openalex.org/W2145759396"],"abstract_inverted_index":{"This":[0],"paper":[1],"presents":[2],"the":[3,7,22,35,63,81,85,89,128,132,139,145,148,166,176,180,183,187,193,227,242,246,249,254,260,266,274,277,282,285,291,294],"method":[4,129,136,287],"that":[5],"recognizes":[6],"road":[8,64,90,116,140,149,184,295],"boundary":[9,82,117,141,150,181,247,292],"situation":[10,146,207,238],"from":[11],"a":[12,17,122,197,204,211,217,223,231],"single":[13,123],"image":[14],"and":[15,37,88,106,162,186,216,245,269],"detects":[16],"driving":[18,133],"lane":[19,26,43,52,69,119,134,177,194,199,218,224,232,261],"based":[20,41,172],"on":[21,42,173,203],"recognition":[23],"result.":[24],"Driving":[25],"detection":[27,135],"is":[28,65,151,190,201,208,220,234,239,257,288],"important":[29],"for":[30],"lateral":[31],"motion":[32],"control":[33],"of":[34,115,147,213,248,276,284,293],"vehicle":[36],"it":[38],"usually":[39],"realized":[40],"mark":[44,178,200,219,233],"detection.":[45],"However,":[46],"there":[47],"are":[48,58,95],"some":[49,154,299],"roads":[50],"where":[51],"marks":[53,70,120],"such":[54,101,156],"as":[55,102,153,157,192,210,222,241,259],"white":[56,158],"lines":[57],"not":[59,235],"drawn.":[60],"Also,":[61],"when":[62,230],"covered":[66],"with":[67,290],"snow,":[68],"cannot":[71],"be":[72],"seen.":[73],"In":[74,280],"these":[75],"cases,":[76],"it's":[77,109],"necessary":[78],"to":[79,111,138,253],"detect":[80,112],"line":[83],"between":[84,182],"roadside":[86,99,188,250],"object":[87,189,251],"surfaces.":[91],"Since":[92],"traffic":[93],"lanes":[94],"divided":[96],"by":[97,121,165,298],"various":[98],"objects,":[100],"curbs,":[103],"grass,":[104],"walls":[105],"so":[107,163],"on,":[108,164],"difficult":[110],"all":[113],"kinds":[114],"including":[118],"algorithm.":[124],"Therefore,":[125],"we":[126],"propose":[127],"which":[130],"changes":[131],"according":[137],"situation.":[142],"At":[143],"first,":[144],"identified":[152,209,240,255],"classes,":[155],"line,":[159],"curb,":[160],"grass":[161],"Convolutional":[167],"Neural":[168],"Network":[169],"(CNN).":[170],"Then,":[171],"this":[174,206,237],"result,":[175],"or":[179],"surface":[185],"detected":[191,221,258],"boundary.":[195,225,262],"When":[196],"clear":[198],"drawn":[202],"road,":[205],"class":[212,244,256],"\"White":[214],"line\"":[215],"On":[226],"other":[228,243],"hand,":[229],"present,":[236],"corresponding":[252],"Experimental":[263],"results":[264],"using":[265],"KITTI":[267],"dataset":[268,272],"our":[270],"own":[271],"show":[273],"effectiveness":[275],"proposed":[278,286],"method.":[279,302],"addition,":[281],"result":[283],"compared":[289],"area":[296],"extracted":[297],"semantic":[300],"segmentation":[301]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}