{"id":"https://openalex.org/W4416429118","doi":"https://doi.org/10.1109/access.2025.3635121","title":"RoadFormer: Local-Global Feature Fusion for Road Surface Classification in Autonomous Driving","display_name":"RoadFormer: Local-Global Feature Fusion for Road Surface Classification in Autonomous Driving","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4416429118","doi":"https://doi.org/10.1109/access.2025.3635121"},"language":"en","primary_location":{"id":"doi:10.1109/access.2025.3635121","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3635121","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1109/access.2025.3635121","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5042995182","display_name":"Tianze Wang","orcid":"https://orcid.org/0009-0000-6630-1832"},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Tianze Wang","raw_affiliation_strings":["National Engineering Laboratory for Electric Vehicles, School of Mechanical Engineering, Beijing Institute of Technology, Beijing, China","School of Mechanical Engineering, National Engineering Laboratory for Electric Vehicles, Beijing Institute of Technology, Beijing, China"],"raw_orcid":"https://orcid.org/0009-0000-6630-1832","affiliations":[{"raw_affiliation_string":"National Engineering Laboratory for Electric Vehicles, School of Mechanical Engineering, Beijing Institute of Technology, Beijing, China","institution_ids":["https://openalex.org/I125839683"]},{"raw_affiliation_string":"School of Mechanical Engineering, National Engineering Laboratory for Electric Vehicles, Beijing Institute of Technology, Beijing, China","institution_ids":["https://openalex.org/I125839683"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100599312","display_name":"Zhang Zhang","orcid":"https://orcid.org/0000-0002-3510-4585"},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhang Zhang","raw_affiliation_strings":["Shenzhen Automotive Research Institute, Beijing Institute of Technology, Shenzhen, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shenzhen Automotive Research Institute, Beijing Institute of Technology, Shenzhen, China","institution_ids":["https://openalex.org/I125839683"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Chao Yue","orcid":"https://orcid.org/0009-0009-9601-4178"},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chao Yue","raw_affiliation_strings":["Shenzhen Automotive Research Institute, Beijing Institute of Technology, Shenzhen, China"],"raw_orcid":"https://orcid.org/0009-0009-9601-4178","affiliations":[{"raw_affiliation_string":"Shenzhen Automotive Research Institute, Beijing Institute of Technology, Shenzhen, China","institution_ids":["https://openalex.org/I125839683"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100308133","display_name":"Yueran Zhao","orcid":"https://orcid.org/0009-0008-2067-7840"},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yueran Zhao","raw_affiliation_strings":["National Engineering Laboratory for Electric Vehicles, School of Mechanical Engineering, Beijing Institute of Technology, Beijing, China","School of Mechanical Engineering, National Engineering Laboratory for Electric Vehicles, Beijing Institute of Technology, Beijing, China"],"raw_orcid":"https://orcid.org/0009-0008-2067-7840","affiliations":[{"raw_affiliation_string":"National Engineering Laboratory for Electric Vehicles, School of Mechanical Engineering, Beijing Institute of Technology, Beijing, China","institution_ids":["https://openalex.org/I125839683"]},{"raw_affiliation_string":"School of Mechanical Engineering, National Engineering Laboratory for Electric Vehicles, Beijing Institute of Technology, Beijing, China","institution_ids":["https://openalex.org/I125839683"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5087977761","display_name":"Chao Sun","orcid":"https://orcid.org/0000-0002-9324-0892"},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chao Sun","raw_affiliation_strings":["Shenzhen Automotive Research Institute, Beijing Institute of Technology, Shenzhen, China"],"raw_orcid":"https://orcid.org/0000-0002-9324-0892","affiliations":[{"raw_affiliation_string":"Shenzhen Automotive Research Institute, Beijing Institute of Technology, Shenzhen, China","institution_ids":["https://openalex.org/I125839683"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5042995182"],"corresponding_institution_ids":["https://openalex.org/I125839683"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":0.8126,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.77870006,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":98},"biblio":{"volume":"13","issue":null,"first_page":"201875","last_page":"201888"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11606","display_name":"Infrastructure Maintenance and Monitoring","score":0.9320999979972839,"subfield":{"id":"https://openalex.org/subfields/2205","display_name":"Civil and Structural 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/T11606","display_name":"Infrastructure Maintenance and Monitoring","score":0.9320999979972839,"subfield":{"id":"https://openalex.org/subfields/2205","display_name":"Civil and Structural 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":0.03739999979734421,"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/T10264","display_name":"Asphalt Pavement Performance Evaluation","score":0.004999999888241291,"subfield":{"id":"https://openalex.org/subfields/2205","display_name":"Civil and Structural 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/feature-extraction","display_name":"Feature extraction","score":0.6518999934196472},{"id":"https://openalex.org/keywords/road-surface","display_name":"Road surface","score":0.6176000237464905},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.49570000171661377},{"id":"https://openalex.org/keywords/stacking","display_name":"Stacking","score":0.4401000142097473},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4287000000476837},{"id":"https://openalex.org/keywords/safer","display_name":"SAFER","score":0.4097999930381775},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3562000095844269},{"id":"https://openalex.org/keywords/reliability","display_name":"Reliability (semiconductor)","score":0.3273000121116638}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7354000210762024},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.6518999934196472},{"id":"https://openalex.org/C2780042925","wikidata":"https://www.wikidata.org/wiki/Q1049667","display_name":"Road surface","level":2,"score":0.6176000237464905},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5523999929428101},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.49570000171661377},{"id":"https://openalex.org/C33347731","wikidata":"https://www.wikidata.org/wiki/Q285210","display_name":"Stacking","level":2,"score":0.4401000142097473},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4287000000476837},{"id":"https://openalex.org/C2776654903","wikidata":"https://www.wikidata.org/wiki/Q2601463","display_name":"SAFER","level":2,"score":0.4097999930381775},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3562000095844269},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3440999984741211},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3393999934196472},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.3273000121116638},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.30889999866485596},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.2922999858856201},{"id":"https://openalex.org/C110083411","wikidata":"https://www.wikidata.org/wiki/Q1744628","display_name":"Statistical classification","level":2,"score":0.2865999937057495},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.2777999937534332},{"id":"https://openalex.org/C26760741","wikidata":"https://www.wikidata.org/wiki/Q160402","display_name":"Perception","level":2,"score":0.2696000039577484},{"id":"https://openalex.org/C87833898","wikidata":"https://www.wikidata.org/wiki/Q1060280","display_name":"Advanced driver assistance systems","level":2,"score":0.2696000039577484},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.2547999918460846},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.25049999356269836}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2025.3635121","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3635121","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:28579be8d42e450ab30a7a43ba3fd233","is_oa":true,"landing_page_url":"https://doaj.org/article/28579be8d42e450ab30a7a43ba3fd233","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 13, Pp 201875-201888 (2025)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2025.3635121","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2025.3635121","pdf_url":null,"source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G3241258132","display_name":null,"funder_award_id":"2023B0909040001","funder_id":"https://openalex.org/F4320329801","funder_display_name":"Shenzhen Research and Development Program"},{"id":"https://openalex.org/G7666324119","display_name":null,"funder_award_id":"KJZD20230923114815032","funder_id":"https://openalex.org/F4320336736","funder_display_name":"Chengdu Science and Technology Program"}],"funders":[{"id":"https://openalex.org/F4320329801","display_name":"Shenzhen Research and Development Program","ror":null},{"id":"https://openalex.org/F4320336736","display_name":"Chengdu Science and Technology Program","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"The":[0],"classification":[1,86,94,189,215],"of":[2,5,25,92,95,128,139,162,184,217,249],"the":[3,16,26,59,90,126,136,148,160,185,188,247],"type":[4],"road":[6,27,36,60],"surface":[7],"(RSC)":[8],"aims":[9],"to":[10,14,30,33,56,146,225,228],"utilize":[11],"pavement":[12,96,101,199,250],"features":[13,183],"identify":[15],"roughness,":[17],"wet":[18],"and":[19,22,38,65,69,121,130,141,167,205,219],"dry":[20],"conditions,":[21],"material":[23],"information":[24,124],"surface.":[28],"Due":[29],"its":[31],"ability":[32,190],"effectively":[34,179],"enhance":[35],"safety":[37],"traffic":[39],"management,":[40],"it":[41],"has":[42,80],"received":[43],"widespread":[44],"attention":[45],"in":[46,239,245,252],"recent":[47],"years.":[48],"In":[49,103,153],"autonomous":[50,115,253],"driving,":[51],"accurate":[52],"RSC":[53,79,112,240],"allows":[54],"vehicles":[55],"better":[57],"understand":[58],"environment,":[61],"adjust":[62],"driving":[63,72,116,254],"strategies,":[64],"ensure":[66],"a":[67,75,108,174,197,206],"safer":[68],"more":[70],"efficient":[71],"experience.":[73],"For":[74],"long":[76],"time,":[77],"vision-based":[78,110],"been":[81],"favored.":[82],"However,":[83],"existing":[84,237],"visual":[85],"methods":[87,238],"have":[88],"overlooked":[89],"exploration":[91],"fine-grained":[93,111,156,181],"types":[97],"(such":[98],"as":[99],"similar":[100],"textures).":[102],"this":[104,211],"work,":[105],"we":[106,172],"propose":[107,173],"pure":[109],"method":[113],"for":[114,191],"scenarios,":[117],"which":[118],"fuses":[119],"local":[120,140],"global":[122,142],"feature":[123,143,150],"through":[125],"stacking":[127,137],"convolutional":[129],"transformer":[131],"modules.":[132],"We":[133],"further":[134],"explore":[135],"strategies":[138],"extraction":[144,151],"modules":[145],"find":[147],"optimal":[149],"strategy.":[152],"addition,":[154],"since":[155],"tasks":[157],"also":[158],"face":[159],"challenge":[161],"relatively":[163,168],"large":[164],"intra-class":[165],"differences":[166],"small":[169],"inter-class":[170],"differences,":[171],"Foreground-Background":[175],"Module":[176],"(FBM)":[177],"that":[178,234],"extracts":[180],"context":[182],"pavement,":[186],"enhancing":[187],"complex":[192],"pavements.":[193],"Experiments":[194],"conducted":[195],"on":[196],"large-scale":[198],"dataset":[200,208,212],"containing":[201],"one":[202],"million":[203],"samples":[204],"simplified":[207],"reorganized":[209],"from":[210],"achieved":[213],"Top-1":[214],"accuracies":[216],"92.52%":[218],"96.50%,":[220],"respectively,":[221],"improving":[222,246],"by":[223],"5.69%":[224],"12.84%":[226],"compared":[227],"SOTA":[229],"methods.":[230],"These":[231],"results":[232],"demonstrate":[233],"RoadFormer":[235],"outperforms":[236],"tasks,":[241],"providing":[242],"significant":[243],"progress":[244],"reliability":[248],"perception":[251],"systems.":[255]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-05-06T08:25:59.206177","created_date":"2025-11-20T00:00:00"}
