{"id":"https://openalex.org/W4319338512","doi":"https://doi.org/10.1109/mvt.2023.3236480","title":"Lidar Object Perception Framework for Urban Autonomous Driving: Detection and State Tracking Based on Convolutional Gated Recurrent Unit and Statistical Approach","display_name":"Lidar Object Perception Framework for Urban Autonomous Driving: Detection and State Tracking Based on Convolutional Gated Recurrent Unit and Statistical Approach","publication_year":2023,"publication_date":"2023-02-07","ids":{"openalex":"https://openalex.org/W4319338512","doi":"https://doi.org/10.1109/mvt.2023.3236480"},"language":"en","primary_location":{"id":"doi:10.1109/mvt.2023.3236480","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mvt.2023.3236480","pdf_url":null,"source":{"id":"https://openalex.org/S98855836","display_name":"IEEE Vehicular Technology Magazine","issn_l":"1556-6072","issn":["1556-6072","1556-6080"],"is_oa":false,"is_in_doaj":false,"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":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Vehicular Technology Magazine","raw_type":"journal-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/A5100427726","display_name":"Jong-Ho Kim","orcid":"https://orcid.org/0000-0001-8863-6406"},"institutions":[{"id":"https://openalex.org/I139264467","display_name":"Seoul National University","ror":"https://ror.org/04h9pn542","country_code":"KR","type":"education","lineage":["https://openalex.org/I139264467"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Jongho Kim","raw_affiliation_strings":["Mechanical Engineering, Seoul National University, Seoul, South Korea"],"raw_orcid":"https://orcid.org/0000-0001-8863-6406","affiliations":[{"raw_affiliation_string":"Mechanical Engineering, Seoul National University, Seoul, South Korea","institution_ids":["https://openalex.org/I139264467"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5023689051","display_name":"Kyongsu Yi","orcid":"https://orcid.org/0000-0002-0484-9752"},"institutions":[{"id":"https://openalex.org/I139264467","display_name":"Seoul National University","ror":"https://ror.org/04h9pn542","country_code":"KR","type":"education","lineage":["https://openalex.org/I139264467"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Kyongsu Yi","raw_affiliation_strings":["Mechanical Engineering, Seoul National University, Seoul, South Korea"],"raw_orcid":"https://orcid.org/0000-0002-0484-9752","affiliations":[{"raw_affiliation_string":"Mechanical Engineering, Seoul National University, Seoul, South Korea","institution_ids":["https://openalex.org/I139264467"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I139264467"],"apc_list":null,"apc_paid":null,"fwci":1.0687,"has_fulltext":false,"cited_by_count":11,"citation_normalized_percentile":{"value":0.74888254,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":98},"biblio":{"volume":"18","issue":"2","first_page":"60","last_page":"68"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.9991000294685364,"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":0.9991000294685364,"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/T11164","display_name":"Remote Sensing and LiDAR Applications","score":0.9987000226974487,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12153","display_name":"Advanced Optical Sensing Technologies","score":0.9977999925613403,"subfield":{"id":"https://openalex.org/subfields/3105","display_name":"Instrumentation"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/lidar","display_name":"Lidar","score":0.7240013480186462},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6743977665901184},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6385166049003601},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.6253936290740967},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.6065555810928345},{"id":"https://openalex.org/keywords/particle-filter","display_name":"Particle filter","score":0.5126785039901733},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5090853571891785},{"id":"https://openalex.org/keywords/frame","display_name":"Frame (networking)","score":0.5064481496810913},{"id":"https://openalex.org/keywords/unmanned-ground-vehicle","display_name":"Unmanned ground vehicle","score":0.4103424847126007},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.29114264249801636},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.23317667841911316}],"concepts":[{"id":"https://openalex.org/C51399673","wikidata":"https://www.wikidata.org/wiki/Q504027","display_name":"Lidar","level":2,"score":0.7240013480186462},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6743977665901184},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6385166049003601},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.6253936290740967},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6065555810928345},{"id":"https://openalex.org/C52421305","wikidata":"https://www.wikidata.org/wiki/Q1151499","display_name":"Particle filter","level":3,"score":0.5126785039901733},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5090853571891785},{"id":"https://openalex.org/C126042441","wikidata":"https://www.wikidata.org/wiki/Q1324888","display_name":"Frame (networking)","level":2,"score":0.5064481496810913},{"id":"https://openalex.org/C2776548393","wikidata":"https://www.wikidata.org/wiki/Q2031473","display_name":"Unmanned ground vehicle","level":2,"score":0.4103424847126007},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.29114264249801636},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.23317667841911316},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.0},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/mvt.2023.3236480","is_oa":false,"landing_page_url":"https://doi.org/10.1109/mvt.2023.3236480","pdf_url":null,"source":{"id":"https://openalex.org/S98855836","display_name":"IEEE Vehicular Technology Magazine","issn_l":"1556-6072","issn":["1556-6072","1556-6080"],"is_oa":false,"is_in_doaj":false,"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":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Vehicular Technology Magazine","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities","score":0.8399999737739563}],"awards":[{"id":"https://openalex.org/G568056181","display_name":null,"funder_award_id":"20220583","funder_id":"https://openalex.org/F4320322011","funder_display_name":"Ministry of Oceans and Fisheries"}],"funders":[{"id":"https://openalex.org/F4320322011","display_name":"Ministry of Oceans and Fisheries","ror":"https://ror.org/00c5s4a53"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W2115579991","https://openalex.org/W2625297812","https://openalex.org/W2963149042","https://openalex.org/W2963727135","https://openalex.org/W2968296999","https://openalex.org/W3013060085","https://openalex.org/W3018757597","https://openalex.org/W3034314779","https://openalex.org/W3035172746","https://openalex.org/W3114753236","https://openalex.org/W3117901461","https://openalex.org/W3159447300","https://openalex.org/W3167095230","https://openalex.org/W3182636718","https://openalex.org/W6685670348","https://openalex.org/W6763422710","https://openalex.org/W6777046832"],"related_works":["https://openalex.org/W4319317934","https://openalex.org/W4406302447","https://openalex.org/W2901265155","https://openalex.org/W2956374172","https://openalex.org/W4319837668","https://openalex.org/W4308071650","https://openalex.org/W3188333020","https://openalex.org/W4281783339","https://openalex.org/W2969228573","https://openalex.org/W2963690996"],"abstract_inverted_index":{"This":[0],"article":[1],"describes":[2],"the":[3,15,19,51,88,113,117,142,176,184],"development":[4],"and":[5,41,101,168,180,188],"implementation":[6],"of":[7,18,30,109,145],"a":[8,38,42,71,120],"3D":[9,65],"lidar":[10],"perception":[11],"framework":[12,28,149,177],"to":[13,63,104,127,140,158],"guarantee":[14],"precise":[16],"cognition":[17],"surrounding":[20,185],"environment":[21],"for":[22,85],"urban":[23,193],"autonomous":[24,162,194],"driving.":[25,195],"The":[26,47,60,91,147,171],"proposed":[27],"consists":[29],"two":[31],"different":[32],"detection":[33,74,100,187],"modules":[34],"operating":[35],"in":[36,87,156,161,192],"parallel:":[37],"deep":[39],"learning-based":[40],"geometric":[43,95],"model-free":[44,96],"cluster-based":[45],"method.":[46],"first":[48,89],"module":[49,61,93],"utilizes":[50,94],"convolutional":[52],"gated":[53],"recurrent":[54],"unit":[55],"(ConvGRU)-based":[56],"residual":[57],"network":[58],"(CGRN).":[59],"aims":[62],"repredict":[64],"objects":[66,111],"based":[67,79],"on":[68,80],"results":[69,173],"from":[70,112],"continuous":[72],"single-frame":[73],"network.":[75],"A":[76,131],"vision-fusion":[77],"methodology":[78],"2D":[81],"projection":[82],"is":[83,102,125,137],"adopted":[84],"postprocessing":[86],"module.":[90,115],"second":[92,118],"area":[97],"(GMFA)":[98],"cluster":[99,121],"designed":[103],"cope":[105],"with":[106,153,165,178],"false-negative":[107],"cases":[108],"unclassified":[110],"prior":[114],"For":[116],"module,":[119],"variance-based":[122],"ground":[123],"removal":[124],"conducted":[126],"prevent":[128],"false-positive":[129],"cases.":[130],"kinematic":[132],"model-based":[133],"particle":[134],"filter":[135],"(PF)":[136],"then":[138],"applied":[139],"estimate":[141],"dynamic":[143],"states":[144],"detection.":[146],"suggested":[148],"has":[150],"been":[151],"developed":[152],"real-time":[154],"operation":[155],"mind,":[157],"be":[159],"implemented":[160],"vehicles":[163],"equipped":[164],"automotive":[166],"lidars":[167],"low-cost":[169],"cameras.":[170],"test":[172],"show":[174],"that":[175],"CGRN":[179],"GMFA":[181],"successfully":[182],"improved":[183],"object":[186],"state":[189],"estimation":[190],"accuracy":[191]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
