{"id":"https://openalex.org/W2890520809","doi":"https://doi.org/10.1109/tits.2018.2857510","title":"Dynamic Vehicle Detection With Sparse Point Clouds Based on PE-CPD","display_name":"Dynamic Vehicle Detection With Sparse Point Clouds Based on PE-CPD","publication_year":2018,"publication_date":"2018-09-18","ids":{"openalex":"https://openalex.org/W2890520809","doi":"https://doi.org/10.1109/tits.2018.2857510","mag":"2890520809"},"language":"en","primary_location":{"id":"doi:10.1109/tits.2018.2857510","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tits.2018.2857510","pdf_url":null,"source":{"id":"https://openalex.org/S144771191","display_name":"IEEE Transactions on Intelligent Transportation Systems","issn_l":"1524-9050","issn":["1524-9050","1558-0016"],"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 Transactions on Intelligent Transportation Systems","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/A5101586165","display_name":"Kaiqi Liu","orcid":"https://orcid.org/0000-0003-2063-5997"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Kaiqi Liu","raw_affiliation_strings":["School of Electronic and Information Engineering, Beihang University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Electronic and Information Engineering, Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100634524","display_name":"Wenguang Wang","orcid":"https://orcid.org/0000-0002-6012-7084"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenguang Wang","raw_affiliation_strings":["School of Electronic and Information Engineering, Beihang University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Electronic and Information Engineering, Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5083431484","display_name":"Ratnasingham Tharmarasa","orcid":"https://orcid.org/0000-0003-3439-4135"},"institutions":[{"id":"https://openalex.org/I98251732","display_name":"McMaster University","ror":"https://ror.org/02fa3aq29","country_code":"CA","type":"education","lineage":["https://openalex.org/I98251732"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Ratnasingham Tharmarasa","raw_affiliation_strings":["Department of Electrical and Computer Engineering, McMaster University, Hamilton, ON, Canada"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, McMaster University, Hamilton, ON, Canada","institution_ids":["https://openalex.org/I98251732"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100669839","display_name":"Jun Wang","orcid":"https://orcid.org/0000-0002-7684-0191"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jun Wang","raw_affiliation_strings":["School of Electronic and Information Engineering, Beihang University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Electronic and Information Engineering, Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5101586165"],"corresponding_institution_ids":["https://openalex.org/I82880672"],"apc_list":null,"apc_paid":null,"fwci":1.9432,"has_fulltext":false,"cited_by_count":41,"citation_normalized_percentile":{"value":0.86802488,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":97,"max":99},"biblio":{"volume":"20","issue":"5","first_page":"1964","last_page":"1977"},"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/T10191","display_name":"Robotics and Sensor-Based Localization","score":0.9951000213623047,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace 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/point-cloud","display_name":"Point cloud","score":0.7132855653762817},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5528417229652405},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5522999167442322},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5158376097679138},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.45043304562568665},{"id":"https://openalex.org/keywords/lidar","display_name":"Lidar","score":0.42496258020401},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3203859329223633},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.25316184759140015},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.20346200466156006},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.09175178408622742}],"concepts":[{"id":"https://openalex.org/C131979681","wikidata":"https://www.wikidata.org/wiki/Q1899648","display_name":"Point cloud","level":2,"score":0.7132855653762817},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5528417229652405},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5522999167442322},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5158376097679138},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.45043304562568665},{"id":"https://openalex.org/C51399673","wikidata":"https://www.wikidata.org/wiki/Q504027","display_name":"Lidar","level":2,"score":0.42496258020401},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3203859329223633},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.25316184759140015},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.20346200466156006},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.09175178408622742}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tits.2018.2857510","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tits.2018.2857510","pdf_url":null,"source":{"id":"https://openalex.org/S144771191","display_name":"IEEE Transactions on Intelligent Transportation Systems","issn_l":"1524-9050","issn":["1524-9050","1558-0016"],"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 Transactions on Intelligent Transportation Systems","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2297807074","display_name":null,"funder_award_id":"61671035","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6042256963","display_name":null,"funder_award_id":"61771028","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":43,"referenced_works":["https://openalex.org/W1541900454","https://openalex.org/W1556563111","https://openalex.org/W1976306417","https://openalex.org/W1986237681","https://openalex.org/W1994114209","https://openalex.org/W2025299767","https://openalex.org/W2044164936","https://openalex.org/W2050428689","https://openalex.org/W2082179954","https://openalex.org/W2112714380","https://openalex.org/W2115579991","https://openalex.org/W2134236847","https://openalex.org/W2135199739","https://openalex.org/W2152845613","https://openalex.org/W2161249246","https://openalex.org/W2166774169","https://openalex.org/W2184393491","https://openalex.org/W2296035016","https://openalex.org/W2296120052","https://openalex.org/W2316306491","https://openalex.org/W2327148878","https://openalex.org/W2347073743","https://openalex.org/W2512933954","https://openalex.org/W2547693818","https://openalex.org/W2548401522","https://openalex.org/W2555618208","https://openalex.org/W2563267921","https://openalex.org/W2586273564","https://openalex.org/W2589014543","https://openalex.org/W2613947156","https://openalex.org/W2677389480","https://openalex.org/W2734383012","https://openalex.org/W2736059325","https://openalex.org/W2739512016","https://openalex.org/W2739781348","https://openalex.org/W2741050579","https://openalex.org/W2741151098","https://openalex.org/W2742562184","https://openalex.org/W2742844481","https://openalex.org/W2743302375","https://openalex.org/W6686211706","https://openalex.org/W6725333127","https://openalex.org/W6730670121"],"related_works":["https://openalex.org/W4319317934","https://openalex.org/W2901265155","https://openalex.org/W2956374172","https://openalex.org/W4281783339","https://openalex.org/W4319837668","https://openalex.org/W4308071650","https://openalex.org/W3188333020","https://openalex.org/W1964041166","https://openalex.org/W4293094720","https://openalex.org/W2739701376"],"abstract_inverted_index":{"Detecting":[0],"dynamic":[1,78,97,101,125],"vehicles":[2,25,46],"is":[3,40,104,120,134,151,155,196],"of":[4,10,54,96,165,192],"great":[5,42],"significance":[6],"in":[7,64,106,189],"the":[8,14,30,33,51,94,114,124,138,159,179,183,187,190,203],"field":[9],"autonomous":[11],"vehicles.":[12],"In":[13],"literature,":[15],"a":[16,41,65,77,147],"few":[17],"vehicle":[18,79,102,139,160,166],"detection":[19,71,80,99,167,184,188],"methods":[20],"are":[21,36,48,173],"proposed":[22,105,135,180],"to":[23,44,122,136],"detect":[24,45,123],"within":[26],"50":[27],"m":[28],"from":[29,50],"Lidar,":[31],"where":[32],"point":[34,56,60,89],"clouds":[35,61],"relatively":[37],"dense.":[38],"It":[39],"challenge":[43],"that":[47,150,178],"far":[49],"Lidar":[52],"because":[53],"sparse":[55],"clouds.":[57],"Fewer":[58],"returned":[59],"will":[62],"result":[63],"larger":[66],"fitting":[67],"randomness":[68],"and":[69,100,116],"lower":[70],"rate.":[72],"To":[73],"tackle":[74],"this":[75,107],"issue,":[76],"method":[81,181,204],"based":[82,112,130,168,205],"on":[83,113,131,169,206],"likelihood-field-based":[84],"model":[85],"combined":[86],"with":[87,146,202,209],"coherent":[88],"drift":[90],"(CPD),":[91],"which":[92,195],"includes":[93],"steps":[95],"object":[98],"confirmation,":[103],"paper.":[108],"An":[109],"adaptive":[110],"threshold":[111],"distance":[115],"grid":[117],"angular":[118],"resolution":[119],"applied":[121],"objects.":[126],"The":[127,141,175],"pose":[128,207],"estimation":[129,208],"CPD":[132],"(PE-CPD)":[133],"estimate":[137],"pose.":[140],"scaling":[142,211],"series":[143],"algorithm":[144],"coupled":[145],"Bayesian":[148],"filter":[149],"improved":[152],"by":[153],"PE-CPD":[154],"utilized":[156],"for":[157],"updating":[158],"states.":[161],"Finally,":[162],"comparative":[163],"experiments":[164],"KITTI":[170],"data":[171],"sets":[172],"conducted.":[174],"results":[176],"show":[177],"improves":[182],"rate,":[185],"especially":[186],"radius":[191],"40-80":[193],"m,":[194],"termed":[197],"as":[198],"distant":[199],"area,":[200],"compared":[201],"modified":[210],"series.":[212]},"counts_by_year":[{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":8},{"year":2022,"cited_by_count":9},{"year":2021,"cited_by_count":4},{"year":2020,"cited_by_count":4},{"year":2019,"cited_by_count":5}],"updated_date":"2026-03-18T14:38:29.013473","created_date":"2025-10-10T00:00:00"}