{"id":"https://openalex.org/W2896929926","doi":"https://doi.org/10.1109/ivs.2018.8500598","title":"An efficient encoder-decoder CNN architecture for reliable multilane detection in real time","display_name":"An efficient encoder-decoder CNN architecture for reliable multilane detection in real time","publication_year":2018,"publication_date":"2018-06-01","ids":{"openalex":"https://openalex.org/W2896929926","doi":"https://doi.org/10.1109/ivs.2018.8500598","mag":"2896929926"},"language":"en","primary_location":{"id":"doi:10.1109/ivs.2018.8500598","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ivs.2018.8500598","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE Intelligent Vehicles Symposium (IV)","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/A5091666360","display_name":"Shriyash Chougule","orcid":"https://orcid.org/0000-0002-0240-8208"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shriyash Chougule","raw_affiliation_strings":["Visteon Corporation, Pune, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Visteon Corporation, Pune, India","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5087554392","display_name":"Asad Ismail","orcid":"https://orcid.org/0000-0002-4138-9505"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Asad Ismail","raw_affiliation_strings":["Visteon Corporation, Karlsruhe, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Visteon Corporation, Karlsruhe, Germany","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5112878586","display_name":"Ajay Soni","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ajay Soni","raw_affiliation_strings":["Visteon Corporation, Pune, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Visteon Corporation, Pune, India","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5080638618","display_name":"Nora Kozonek","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Nora Kozonek","raw_affiliation_strings":["Visteon Corporation, Karlsruhe, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Visteon Corporation, Karlsruhe, Germany","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5083176636","display_name":"Vikram Narayan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Vikram Narayan","raw_affiliation_strings":["Visteon Corporation, Karlsruhe, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Visteon Corporation, Karlsruhe, Germany","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5026943834","display_name":"Matthias Schulze","orcid":"https://orcid.org/0000-0002-2250-0431"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Matthias Schulze","raw_affiliation_strings":["Visteon Corporation, Karlsruhe, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Visteon Corporation, Karlsruhe, Germany","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.363,"has_fulltext":false,"cited_by_count":18,"citation_normalized_percentile":{"value":0.89827788,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1444","last_page":"1451"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9988999962806702,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9869999885559082,"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/computer-science","display_name":"Computer science","score":0.8458468317985535},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6913020014762878},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6873632073402405},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6517610549926758},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.6508301496505737},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.6208304166793823},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5733364224433899},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.5726460218429565},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.46761593222618103},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.45536795258522034},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.4547322690486908},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3419560194015503}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8458468317985535},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6913020014762878},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6873632073402405},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6517610549926758},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.6508301496505737},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.6208304166793823},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5733364224433899},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.5726460218429565},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.46761593222618103},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.45536795258522034},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.4547322690486908},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3419560194015503},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ivs.2018.8500598","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ivs.2018.8500598","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE Intelligent Vehicles Symposium (IV)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.41999998688697815,"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":34,"referenced_works":["https://openalex.org/W1556899625","https://openalex.org/W1745334888","https://openalex.org/W1836465849","https://openalex.org/W1903029394","https://openalex.org/W1910657905","https://openalex.org/W2010522557","https://openalex.org/W2015255902","https://openalex.org/W2022202433","https://openalex.org/W2047030012","https://openalex.org/W2070724998","https://openalex.org/W2106976646","https://openalex.org/W2118545852","https://openalex.org/W2140623477","https://openalex.org/W2149933564","https://openalex.org/W2150322733","https://openalex.org/W2159132531","https://openalex.org/W2171943915","https://openalex.org/W2277132981","https://openalex.org/W2340897893","https://openalex.org/W2419448466","https://openalex.org/W2478820856","https://openalex.org/W2519059004","https://openalex.org/W2560323025","https://openalex.org/W2605617837","https://openalex.org/W2738638456","https://openalex.org/W2963881378","https://openalex.org/W3100916768","https://openalex.org/W4293406525","https://openalex.org/W4299518610","https://openalex.org/W6638667902","https://openalex.org/W6639780620","https://openalex.org/W6682007123","https://openalex.org/W6685336329","https://openalex.org/W6717372056"],"related_works":["https://openalex.org/W4390516098","https://openalex.org/W2085033728","https://openalex.org/W4285411112","https://openalex.org/W2171299904","https://openalex.org/W2181948922","https://openalex.org/W2384362569","https://openalex.org/W1647606319","https://openalex.org/W4390494008","https://openalex.org/W2922442631","https://openalex.org/W2053596378"],"abstract_inverted_index":{"Multilane":[0],"detection":[1,30,116],"system":[2],"is":[3,47],"a":[4,36,42,48,82,86,113],"vital":[5],"prerequisite":[6],"for":[7,27,180,208],"realizing":[8],"higher":[9],"ADAS":[10],"functionality":[11],"of":[12,31,61,74,93,135,163,233],"autonomous":[13],"navigation.":[14],"In":[15],"this":[16],"work,":[17],"we":[18,80,152,170],"present":[19,124],"an":[20,206,240],"efficient":[21],"convolutional":[22],"neural":[23],"network":[24,40,54,63,220,238],"(CNN)":[25],"architecture":[26,45],"real":[28],"time":[29],"multiple":[32],"lane":[33,58,83,168,174,189,211],"boundaries":[34,175,212],"using":[35],"camera":[37],"sensor.":[38],"Our":[39,145,237],"has":[41,252],"simple":[43],"encoder-decoder":[44],"and":[46,128,187,202,223,225,235,250],"special":[49],"two":[50,66],"class":[51,88,101,137],"semantic":[52,94],"segmentation":[53,121,133],"designed":[55],"to":[56,130,197],"segment":[57],"boundaries.":[59],"Efficacy":[60],"our":[62,76,159,219,256],"stems":[64],"from":[65],"key":[67],"insights":[68],"which":[69,103,139],"are":[70],"at":[71],"the":[72,91,99,109,119,132,150,154,166,173,177,194,200],"foundation":[73],"all":[75],"design":[77,126],"decisions.":[78],"Firstly,":[79],"term":[81],"boundary":[84],"as":[85],"weak":[87,100,136],"object":[89],"in":[90,108,140,149,158,176,205,213,231,259],"context":[92,204],"segmentation.":[95],"We":[96,123,191,217],"show":[97],"that":[98],"objects":[102],"occupy":[104],"relatively":[105,114],"few":[106],"pixels":[107],"scene,":[110],"also":[111],"have":[112],"low":[115,185],"accuracy":[117,134],"among":[118],"know":[120],"methods.":[122],"novel":[125],"choices":[127],"intuitions":[129],"improve":[131],"objects,":[138],"turn":[141],"reduces":[142],"computation":[143],"time.":[144],"second":[146],"insight":[147],"lies":[148],"manner":[151],"depict":[153],"ground":[155,178],"truth":[156,179],"information":[157],"derived":[160],"dataset.":[161],"Instead":[162],"annotating":[164],"just":[165],"visible":[167],"markers,":[169],"accurately":[171,209],"delineate":[172],"challenging":[181,215],"scenarios":[182],"like":[183],"occlusions,":[184],"light":[186],"degraded":[188],"markings.":[190],"then":[192],"leverage":[193],"CNN's":[195],"ability":[196],"concisely":[198],"summarize":[199],"global":[201],"local":[203],"image,":[207],"inferring":[210],"these":[214],"cases.":[216],"evaluate":[218],"against":[221],"ENet":[222],"FCN-8,":[224],"found":[226],"it":[227,251],"performing":[228],"notably":[229],"better":[230],"terms":[232],"speed":[234],"accuracy.":[236],"achieves":[239],"encouraging":[241],"46":[242],"FPS":[243],"performance":[244],"on":[245,255],"NVIDIA":[246],"Drive":[247],"PX2":[248],"platform":[249],"been":[253],"validated":[254],"test":[257],"vehicle":[258],"highway":[260],"driving":[261],"conditions.":[262]},"counts_by_year":[{"year":2025,"cited_by_count":7},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":5},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
