{"id":"https://openalex.org/W4295678917","doi":"https://doi.org/10.1145/3563217","title":"Driving Maneuver Anomaly Detection Based on Deep Auto-Encoder and Geographical Partitioning","display_name":"Driving Maneuver Anomaly Detection Based on Deep Auto-Encoder and Geographical Partitioning","publication_year":2022,"publication_date":"2022-09-14","ids":{"openalex":"https://openalex.org/W4295678917","doi":"https://doi.org/10.1145/3563217"},"language":"en","primary_location":{"id":"doi:10.1145/3563217","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3563217","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3563217","source":{"id":"https://openalex.org/S170502224","display_name":"ACM Transactions on Sensor Networks","issn_l":"1550-4859","issn":["1550-4859","1550-4867"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Sensor Networks","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"bronze","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3563217","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100379984","display_name":"Miaomiao Liu","orcid":"https://orcid.org/0000-0001-8255-5891"},"institutions":[{"id":"https://openalex.org/I156087764","display_name":"University of California, Merced","ror":"https://ror.org/00d9ah105","country_code":"US","type":"education","lineage":["https://openalex.org/I156087764"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Miaomiao Liu","raw_affiliation_strings":["Dept. of Computer Science and Engineering, University of California, Merced, USA"],"affiliations":[{"raw_affiliation_string":"Dept. of Computer Science and Engineering, University of California, Merced, USA","institution_ids":["https://openalex.org/I156087764"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5087322305","display_name":"Kang Yang","orcid":"https://orcid.org/0000-0001-8248-4894"},"institutions":[{"id":"https://openalex.org/I156087764","display_name":"University of California, Merced","ror":"https://ror.org/00d9ah105","country_code":"US","type":"education","lineage":["https://openalex.org/I156087764"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Kang Yang","raw_affiliation_strings":["Dept. of Computer Science and Engineering, University of California, Merced, USA"],"affiliations":[{"raw_affiliation_string":"Dept. of Computer Science and Engineering, University of California, Merced, USA","institution_ids":["https://openalex.org/I156087764"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5032187620","display_name":"Yanjie Fu","orcid":"https://orcid.org/0000-0002-1767-8024"},"institutions":[{"id":"https://openalex.org/I106165777","display_name":"University of Central Florida","ror":"https://ror.org/036nfer12","country_code":"US","type":"education","lineage":["https://openalex.org/I106165777"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yanjie Fu","raw_affiliation_strings":["Dept. of Computer Science, University of Central Florida, Florida, USA"],"affiliations":[{"raw_affiliation_string":"Dept. of Computer Science, University of Central Florida, Florida, USA","institution_ids":["https://openalex.org/I106165777"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Dapeng Wu","orcid":"https://orcid.org/0000-0002-2732-6954"},"institutions":[{"id":"https://openalex.org/I168719708","display_name":"City University of Hong Kong","ror":"https://ror.org/03q8dnn23","country_code":"HK","type":"education","lineage":["https://openalex.org/I168719708"]}],"countries":["HK"],"is_corresponding":false,"raw_author_name":"Dapeng Wu","raw_affiliation_strings":["Dept. of Computer Science, City University of Hong Kong, Hong Kong, China"],"affiliations":[{"raw_affiliation_string":"Dept. of Computer Science, City University of Hong Kong, Hong Kong, China","institution_ids":["https://openalex.org/I168719708"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5107908225","display_name":"Wan Du","orcid":null},"institutions":[{"id":"https://openalex.org/I156087764","display_name":"University of California, Merced","ror":"https://ror.org/00d9ah105","country_code":"US","type":"education","lineage":["https://openalex.org/I156087764"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Wan Du","raw_affiliation_strings":["Dept. of Computer Science and Engineering, University of California, Merced, USA"],"affiliations":[{"raw_affiliation_string":"Dept. of Computer Science and Engineering, University of California, Merced, USA","institution_ids":["https://openalex.org/I156087764"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5100379984"],"corresponding_institution_ids":["https://openalex.org/I156087764"],"apc_list":null,"apc_paid":null,"fwci":1.2492,"has_fulltext":true,"cited_by_count":9,"citation_normalized_percentile":{"value":0.83101861,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":"19","issue":"2","first_page":"1","last_page":"22"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":1.0,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":1.0,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T10800","display_name":"Forensic Toxicology and Drug Analysis","score":0.9912999868392944,"subfield":{"id":"https://openalex.org/subfields/3005","display_name":"Toxicology"},"field":{"id":"https://openalex.org/fields/30","display_name":"Pharmacology, Toxicology and Pharmaceutics"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.9883999824523926,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7870743274688721},{"id":"https://openalex.org/keywords/dependency","display_name":"Dependency (UML)","score":0.7718706727027893},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.7563594579696655},{"id":"https://openalex.org/keywords/global-positioning-system","display_name":"Global Positioning System","score":0.6513780355453491},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.5556968450546265},{"id":"https://openalex.org/keywords/baseline","display_name":"Baseline (sea)","score":0.515240490436554},{"id":"https://openalex.org/keywords/acceleration","display_name":"Acceleration","score":0.49028876423835754},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.4653955399990082},{"id":"https://openalex.org/keywords/anomaly","display_name":"Anomaly (physics)","score":0.45725956559181213},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.43925681710243225},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.4367592930793762},{"id":"https://openalex.org/keywords/partition","display_name":"Partition (number theory)","score":0.4245503842830658},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.38699260354042053},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.2703993320465088}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7870743274688721},{"id":"https://openalex.org/C19768560","wikidata":"https://www.wikidata.org/wiki/Q320727","display_name":"Dependency (UML)","level":2,"score":0.7718706727027893},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.7563594579696655},{"id":"https://openalex.org/C60229501","wikidata":"https://www.wikidata.org/wiki/Q18822","display_name":"Global Positioning System","level":2,"score":0.6513780355453491},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.5556968450546265},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.515240490436554},{"id":"https://openalex.org/C117896860","wikidata":"https://www.wikidata.org/wiki/Q11376","display_name":"Acceleration","level":2,"score":0.49028876423835754},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.4653955399990082},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.45725956559181213},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.43925681710243225},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.4367592930793762},{"id":"https://openalex.org/C42812","wikidata":"https://www.wikidata.org/wiki/Q1082910","display_name":"Partition (number theory)","level":2,"score":0.4245503842830658},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.38699260354042053},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2703993320465088},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.0},{"id":"https://openalex.org/C26873012","wikidata":"https://www.wikidata.org/wiki/Q214781","display_name":"Condensed matter physics","level":1,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C111368507","wikidata":"https://www.wikidata.org/wiki/Q43518","display_name":"Oceanography","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C74650414","wikidata":"https://www.wikidata.org/wiki/Q11397","display_name":"Classical mechanics","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},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3563217","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3563217","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3563217","source":{"id":"https://openalex.org/S170502224","display_name":"ACM Transactions on Sensor Networks","issn_l":"1550-4859","issn":["1550-4859","1550-4867"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Sensor Networks","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1145/3563217","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3563217","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3563217","source":{"id":"https://openalex.org/S170502224","display_name":"ACM Transactions on Sensor Networks","issn_l":"1550-4859","issn":["1550-4859","1550-4867"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319798","host_organization_name":"Association for Computing Machinery","host_organization_lineage":["https://openalex.org/P4310319798"],"host_organization_lineage_names":["Association for Computing Machinery"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ACM Transactions on Sensor Networks","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","score":0.7400000095367432,"id":"https://metadata.un.org/sdg/11"}],"awards":[{"id":"https://openalex.org/G3555515704","display_name":"EAGER: Collaborative Research: Substructure-aware Spatiotemporal Representation Learning","funder_award_id":"2040950","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G5921281487","display_name":null,"funder_award_id":"number","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G6406716698","display_name":null,"funder_award_id":"2007210","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G7847671534","display_name":null,"funder_award_id":"2008837","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G848032724","display_name":null,"funder_award_id":"Science","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4295678917.pdf","grobid_xml":"https://content.openalex.org/works/W4295678917.grobid-xml"},"referenced_works_count":34,"referenced_works":["https://openalex.org/W98895690","https://openalex.org/W1924770834","https://openalex.org/W1973749534","https://openalex.org/W2011039300","https://openalex.org/W2086662759","https://openalex.org/W2089343068","https://openalex.org/W2121947440","https://openalex.org/W2127735975","https://openalex.org/W2132914434","https://openalex.org/W2151666054","https://openalex.org/W2172717914","https://openalex.org/W2176240313","https://openalex.org/W2272158670","https://openalex.org/W2341058432","https://openalex.org/W2590652386","https://openalex.org/W2622390887","https://openalex.org/W2903883820","https://openalex.org/W2910068345","https://openalex.org/W2948401540","https://openalex.org/W2954713061","https://openalex.org/W2959716684","https://openalex.org/W2963061824","https://openalex.org/W2987228832","https://openalex.org/W2988035443","https://openalex.org/W3047056580","https://openalex.org/W3088951242","https://openalex.org/W3109235893","https://openalex.org/W3110046508","https://openalex.org/W3123432870","https://openalex.org/W3126615635","https://openalex.org/W3130911179","https://openalex.org/W3165069917","https://openalex.org/W4286432616","https://openalex.org/W6758101687"],"related_works":["https://openalex.org/W3186512740","https://openalex.org/W3017266184","https://openalex.org/W2918377632","https://openalex.org/W3194885736","https://openalex.org/W3046391934","https://openalex.org/W4363671829","https://openalex.org/W2806741695","https://openalex.org/W3210364259","https://openalex.org/W4290647774","https://openalex.org/W3189286258"],"abstract_inverted_index":{"This":[0],"paper":[1],"presents":[2],"GeoDMA":[3,169,187],",":[4],"which":[5],"processes":[6],"the":[7,42,52,56,63,72,95,102,110,121,127,136,196],"GPS":[8,45,180],"data":[9,46,58],"from":[10,41],"multiple":[11],"vehicles":[12],"to":[13,34,70,86,93,105,125,190],"detect":[14],"anomalous":[15],"driving":[16,57,96,150],"maneuvers,":[17],"such":[18],"as":[19],"rapid":[20,25],"acceleration,":[21],"sudden":[22],"braking,":[23],"and":[24,62,108,134,157,172],"swerving.":[26],"First,":[27],"an":[28,115],"unsupervised":[29],"deep":[30],"auto-encoder":[31],"is":[32,124],"designed":[33],"learn":[35],"a":[36,82,88,148,177],"set":[37],"of":[38,47,55,75,120,129,138,161],"unique":[39],"features":[40],"normal":[43],"historical":[44],"all":[48],"drivers.":[49,68],"We":[50,167],"consider":[51],"temporal":[53],"dependency":[54,74,104,107,128,137],"for":[59,154],"individual":[60],"drivers":[61,76],"spatial":[64],"correlation":[65],"among":[66],"different":[67,143],"Second,":[69],"incorporate":[71],"peer":[73],"in":[77,170],"local":[78],"regions,":[79],"we":[80,100,146],"develop":[81],"geographical":[83,111],"partitioning":[84,112],"algorithm":[85],"partition":[87],"city":[89],"into":[90,114],"several":[91],"sub-regions":[92],"do":[94],"anomaly":[97,151],"detection.":[98],"Specifically,":[99],"extend":[101],"vehicle-vehicle":[103],"road-road":[106],"formulate":[109],"problem":[113,123],"optimization":[116,122],"problem.":[117],"The":[118,182],"objective":[119],"maximize":[126],"roads":[130,139],"within":[131],"each":[132,155],"sub-region":[133,156],"minimize":[135],"between":[140],"any":[141],"two":[142],"sub-regions.":[144],"Finally,":[145],"train":[147],"specific":[149],"detection":[152,193],"model":[153],"perform":[158],"in-situ":[159],"updating":[160],"these":[162],"models":[163],"by":[164],"incremental":[165],"training.":[166],"implement":[168],"Pytorch":[171],"evaluate":[173],"its":[174],"performance":[175],"using":[176],"large":[178],"real-world":[179],"trajectories.":[181],"experiment":[183],"results":[184],"demonstrate":[185],"that":[186],"achieves":[188],"up":[189],"8.5%":[191],"higher":[192],"accuracy":[194],"than":[195],"baseline":[197],"methods.":[198]},"counts_by_year":[{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":3},{"year":2022,"cited_by_count":1}],"updated_date":"2026-04-10T15:06:20.359241","created_date":"2025-10-10T00:00:00"}