{"id":"https://openalex.org/W7138171656","doi":"https://doi.org/10.1609/aaai.v40i13.38101","title":"Fine-Grained Representation for Lane Topology Reasoning","display_name":"Fine-Grained Representation for Lane Topology Reasoning","publication_year":2026,"publication_date":"2026-03-14","ids":{"openalex":"https://openalex.org/W7138171656","doi":"https://doi.org/10.1609/aaai.v40i13.38101"},"language":null,"primary_location":{"id":"doi:10.1609/aaai.v40i13.38101","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v40i13.38101","pdf_url":null,"source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://doi.org/10.1609/aaai.v40i13.38101","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101967307","display_name":"Guoqing Xu","orcid":"https://orcid.org/0000-0003-4737-2146"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Guoqing Xu","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129743057","display_name":"Yiheng Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yiheng Li","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5129741161","display_name":"Yang Yang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang Yang","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5101967307"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.45454545,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"40","issue":"13","first_page":"11214","last_page":"11222"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.5848000049591064,"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":0.5848000049591064,"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":0.06880000233650208,"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/T12536","display_name":"Topological and Geometric Data Analysis","score":0.0551999993622303,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"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/topology","display_name":"Topology (electrical circuits)","score":0.7143999934196472},{"id":"https://openalex.org/keywords/prior-probability","display_name":"Prior probability","score":0.616599977016449},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.5764999985694885},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5418000221252441},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.5123000144958496},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5015000104904175},{"id":"https://openalex.org/keywords/network-topology","display_name":"Network topology","score":0.3181999921798706}],"concepts":[{"id":"https://openalex.org/C184720557","wikidata":"https://www.wikidata.org/wiki/Q7825049","display_name":"Topology (electrical circuits)","level":2,"score":0.7143999934196472},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.65829998254776},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.616599977016449},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.5764999985694885},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5418000221252441},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.5123000144958496},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5015000104904175},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3578000068664551},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.32359999418258667},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.32330000400543213},{"id":"https://openalex.org/C199845137","wikidata":"https://www.wikidata.org/wiki/Q145490","display_name":"Network topology","level":2,"score":0.3181999921798706},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2930000126361847},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.29249998927116394},{"id":"https://openalex.org/C31170391","wikidata":"https://www.wikidata.org/wiki/Q188619","display_name":"Hierarchy","level":2,"score":0.2913999855518341},{"id":"https://openalex.org/C144986985","wikidata":"https://www.wikidata.org/wiki/Q871236","display_name":"Hierarchical database model","level":2,"score":0.2856999933719635},{"id":"https://openalex.org/C24858836","wikidata":"https://www.wikidata.org/wiki/Q844718","display_name":"Theory of computation","level":2,"score":0.2734000086784363},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.263700008392334},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2581999897956848},{"id":"https://openalex.org/C181576044","wikidata":"https://www.wikidata.org/wiki/Q4129926","display_name":"Computational topology","level":3,"score":0.2540000081062317}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1609/aaai.v40i13.38101","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v40i13.38101","pdf_url":null,"source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1609/aaai.v40i13.38101","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v40i13.38101","pdf_url":null,"source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Precise":[0],"modeling":[1],"of":[2,42,142,156,225],"lane":[3,23,37,49,63,161,202],"topology":[4,54,64,77,195,207],"is":[5],"essential":[6],"for":[7],"autonomous":[8],"driving,":[9],"as":[10],"it":[11],"directly":[12],"impacts":[13],"navigation":[14],"and":[15,28,93,109,131,145,168,182,188,204,229],"control":[16],"decisions.":[17],"Existing":[18],"methods":[19],"typically":[20],"represent":[21],"each":[22,157],"with":[24,148,222],"a":[25,61,171,190],"single":[26],"query":[27,121,154,166],"infer":[29],"topological":[30,172],"connectivity":[31,162],"based":[32,163],"on":[33,164,211,227,231],"the":[34,70,106,129,143,153,212],"similarity":[35],"between":[36],"queries.":[38],"However,":[39],"this":[40,57],"kind":[41],"design":[43],"struggles":[44],"to":[45,52,76,117,151,175,193],"accurately":[46],"model":[47],"complex":[48,201],"structures,":[50],"leading":[51],"unreliable":[53],"prediction.":[55],"In":[56],"view,":[58],"we":[59],"propose":[60],"Fine-Grained":[62],"reasoning":[65],"framework":[66],"(TopoFG).":[67],"It":[68,134],"divides":[69],"procedure":[71],"from":[72,105,113],"bird\u2019s-eye-view":[73],"(BEV)":[74],"features":[75,150,167],"prediction":[78],"via":[79],"fine-grained":[80,120,125,186],"queries":[81,126,187],"into":[82,185],"three":[83],"phases,":[84],"i.e.,":[85],"Hierarchical":[86],"Prior":[87],"Extractor":[88],"(HPE),":[89],"Region-Focused":[90],"Decoder":[91],"(RFD),":[92],"Robust":[94],"Boundary-Point":[95],"Topology":[96],"Reasoning":[97],"(RBTR).":[98],"Specifically,":[99],"HPE":[100],"extracts":[101],"global":[102],"spatial":[103,130,181],"priors":[104,112,184],"BEV":[107,149],"mask":[108,144],"local":[110],"sequential":[111,132,183],"in-lane":[114],"keypoint":[115],"sequences":[116],"guide":[118],"subsequent":[119],"modeling.":[122],"RFD":[123],"constructs":[124],"by":[127],"integrating":[128,180],"priors.":[133],"then":[135],"samples":[136],"reference":[137],"points":[138],"in":[139],"RoI":[140],"regions":[141],"applies":[146],"cross-attention":[147],"refine":[152],"representations":[155],"lane.":[158],"RBTR":[159],"models":[160,200],"boundary-point":[165,194],"further":[169],"employs":[170],"denoising":[173,191],"strategy":[174,192],"reduce":[176],"matching":[177],"ambiguity.":[178],"By":[179],"applying":[189],"reasoning,":[196],"our":[197],"method":[198],"precisely":[199],"structures":[203],"delivers":[205],"trustworthy":[206],"predictions.":[208],"Extensive":[209],"experiments":[210],"OpenLane-V2":[213],"benchmark":[214],"demonstrate":[215],"that":[216],"TopoFG":[217],"achieves":[218],"new":[219],"state-of-the-art":[220],"performance,":[221],"an":[223],"OLS":[224],"48.0%":[226],"subset_A":[228],"45.4%":[230],"subset_B.":[232]},"counts_by_year":[],"updated_date":"2026-03-18T06:31:55.123368","created_date":"2026-03-18T00:00:00"}