{"id":"https://openalex.org/W7101758895","doi":"https://doi.org/10.48550/arxiv.2510.22236","title":"DiffusionLane: Diffusion Model for Lane Detection","display_name":"DiffusionLane: Diffusion Model for Lane Detection","publication_year":2025,"publication_date":"2025-10-25","ids":{"openalex":"https://openalex.org/W7101758895","doi":"https://doi.org/10.48550/arxiv.2510.22236"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2510.22236","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2510.22236","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2510.22236","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Zhou, Kunyang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhou, Kunyang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":null,"display_name":"Shao, Yeqin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shao, Yeqin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":2,"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":true,"primary_topic":{"id":"https://openalex.org/T13124","display_name":"Chemical synthesis and alkaloids","score":0.11949999630451202,"subfield":{"id":"https://openalex.org/subfields/1605","display_name":"Organic Chemistry"},"field":{"id":"https://openalex.org/fields/16","display_name":"Chemistry"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T13124","display_name":"Chemical synthesis and alkaloids","score":0.11949999630451202,"subfield":{"id":"https://openalex.org/subfields/1605","display_name":"Organic Chemistry"},"field":{"id":"https://openalex.org/fields/16","display_name":"Chemistry"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T13130","display_name":"Biological and pharmacological studies of plants","score":0.09120000153779984,"subfield":{"id":"https://openalex.org/subfields/2736","display_name":"Pharmacology"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T13422","display_name":"Phytochemistry and biological activities of Ficus species","score":0.021700000390410423,"subfield":{"id":"https://openalex.org/subfields/1110","display_name":"Plant Science"},"field":{"id":"https://openalex.org/fields/11","display_name":"Agricultural and Biological Sciences"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.6337000131607056},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.6061000227928162},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5742999911308289},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.5620999932289124},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.49799999594688416},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.475600004196167},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.4404999911785126},{"id":"https://openalex.org/keywords/decoding-methods","display_name":"Decoding methods","score":0.43619999289512634},{"id":"https://openalex.org/keywords/point","display_name":"Point (geometry)","score":0.4269999861717224}],"concepts":[{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.6337000131607056},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6305999755859375},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.6061000227928162},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5742999911308289},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.5620999932289124},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5437999963760376},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.49799999594688416},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.475600004196167},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.4404999911785126},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.43619999289512634},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.4269999861717224},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.41499999165534973},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.41429999470710754},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.40540000796318054},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.4047999978065491},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.4011000096797943},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.37070000171661377},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.31929999589920044},{"id":"https://openalex.org/C2778572836","wikidata":"https://www.wikidata.org/wiki/Q380933","display_name":"Space (punctuation)","level":2,"score":0.3019999861717224},{"id":"https://openalex.org/C4199805","wikidata":"https://www.wikidata.org/wiki/Q2725903","display_name":"Gaussian noise","level":2,"score":0.2906000018119812},{"id":"https://openalex.org/C69357855","wikidata":"https://www.wikidata.org/wiki/Q163214","display_name":"Diffusion","level":2,"score":0.2797999978065491},{"id":"https://openalex.org/C2778067643","wikidata":"https://www.wikidata.org/wiki/Q166507","display_name":"Interval (graph theory)","level":2,"score":0.272599995136261},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.27219998836517334},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.271699994802475},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.26669999957084656},{"id":"https://openalex.org/C2776434776","wikidata":"https://www.wikidata.org/wiki/Q19246213","display_name":"Domain adaptation","level":3,"score":0.26420000195503235},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.2639999985694885},{"id":"https://openalex.org/C12713177","wikidata":"https://www.wikidata.org/wiki/Q1900281","display_name":"Perspective (graphical)","level":2,"score":0.25949999690055847},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.25679999589920044}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2510.22236","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2510.22236","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2510.22236","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2510.22236","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"sustainable_development_goals":[{"display_name":"No poverty","score":0.5016440749168396,"id":"https://metadata.un.org/sdg/1"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"In":[0],"this":[1],"paper,":[2],"we":[3,33,76,98,123],"present":[4],"a":[5,21,67,78,100,159],"novel":[6],"diffusion-based":[7],"model":[8,58],"for":[9,110,138],"lane":[10,17,54,64,95,112,136],"detection,":[11],"called":[12],"DiffusionLane,":[13],"which":[14],"treats":[15],"the":[16,26,30,35,39,45,57,62,72,84,89,93,117,121,129,134,140,143,169,179,188],"detection":[18,165],"task":[19],"as":[20],"denoising":[22],"diffusion":[23,102],"process":[24],"in":[25,66,128],"parameter":[27],"space":[28],"of":[29,47,88,120],"lane.":[31],"Firstly,":[32],"add":[34],"Gaussian":[36],"noise":[37],"to":[38,51,60,70,82,104,115,132,168],"parameters":[40],"(the":[41],"starting":[42],"point":[43],"and":[44,56,107,153,163,209],"angle)":[46],"ground":[48],"truth":[49],"lanes":[50],"obtain":[52,71],"noisy":[53,63,94],"anchors,":[55],"learns":[59],"refine":[61],"anchors":[65,137],"progressive":[68],"way":[69],"target":[73],"lanes.":[74],"Secondly,":[75],"propose":[77],"hybrid":[79,101],"decoding":[80],"strategy":[81],"address":[83],"poor":[85],"feature":[86,118],"representation":[87,119],"encoder,":[90,122],"resulting":[91],"from":[92],"anchors.":[96,113],"Specifically,":[97],"design":[99],"decoder":[103],"combine":[105],"global-level":[106],"local-level":[108],"decoders":[109],"high-quality":[111],"Then,":[114],"improve":[116],"employ":[124],"an":[125],"auxiliary":[126],"head":[127],"training":[130],"stage":[131],"adopt":[133],"learnable":[135],"enriching":[139],"supervision":[141],"on":[142,147,187,201,205,213],"encoder.":[144],"Experimental":[145],"results":[146],"four":[148],"benchmarks,":[149],"Carlane,":[150],"Tusimple,":[151],"CULane,":[152,202],"LLAMAS,":[154],"show":[155],"that":[156],"DiffusionLane":[157,175,194],"possesses":[158],"strong":[160],"generalization":[161],"ability":[162],"promising":[164],"performance":[166],"compared":[167],"previous":[170],"state-of-the-art":[171],"methods.":[172],"For":[173],"example,":[174],"with":[176,195,207,215],"ResNet18":[177],"surpasses":[178],"existing":[180],"methods":[181],"by":[182],"at":[183,221],"least":[184],"1\\%":[185],"accuracy":[186,204],"domain":[189],"adaptation":[190],"dataset":[191],"Carlane.":[192],"Besides,":[193],"MobileNetV4":[196],"gets":[197],"81.32\\%":[198],"F1":[199,211],"score":[200,212],"96.89\\%":[203],"Tusimple":[206],"ResNet34,":[208],"97.59\\%":[210],"LLAMAS":[214],"ResNet101.":[216],"Code":[217],"will":[218],"be":[219],"available":[220],"https://github.com/zkyntu/UnLanedet.":[222]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-29T00:00:00"}