{"id":"https://openalex.org/W2792776757","doi":"https://doi.org/10.1109/tits.2018.2797943","title":"Learning Driver Braking Behavior Using Smartphones, Neural Networks and the Sliding Correlation Coefficient: Road Anomaly Case Study","display_name":"Learning Driver Braking Behavior Using Smartphones, Neural Networks and the Sliding Correlation Coefficient: Road Anomaly Case Study","publication_year":2018,"publication_date":"2018-02-16","ids":{"openalex":"https://openalex.org/W2792776757","doi":"https://doi.org/10.1109/tits.2018.2797943","mag":"2792776757"},"language":"en","primary_location":{"id":"doi:10.1109/tits.2018.2797943","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tits.2018.2797943","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":true,"oa_status":"green","oa_url":"https://pureportal.coventry.ac.uk/en/publications/f924f58e-3933-47b1-bf5a-2251a7d8e747","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5010336935","display_name":"Stavros\u2010Richard G. Christopoulos","orcid":null},"institutions":[{"id":"https://openalex.org/I200777214","display_name":"National and Kapodistrian University of Athens","ror":"https://ror.org/04gnjpq42","country_code":"GR","type":"education","lineage":["https://openalex.org/I200777214"]}],"countries":["GR"],"is_corresponding":true,"raw_author_name":"Stavros-Richard G. Christopoulos","raw_affiliation_strings":["Faculty of Physics, Solid Earth Physics Institute, National and Kapodistrian University of Athens, Athens, Greece"],"raw_orcid":"https://orcid.org/0000-0002-8468-2998","affiliations":[{"raw_affiliation_string":"Faculty of Physics, Solid Earth Physics Institute, National and Kapodistrian University of Athens, Athens, Greece","institution_ids":["https://openalex.org/I200777214"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5046262492","display_name":"Stratis Kanarachos","orcid":"https://orcid.org/0000-0002-0096-4016"},"institutions":[{"id":"https://openalex.org/I73417466","display_name":"Coventry University","ror":"https://ror.org/01tgmhj36","country_code":"GB","type":"education","lineage":["https://openalex.org/I73417466"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Stratis Kanarachos","raw_affiliation_strings":["Faculty of Engineering, Environment and Computing, Coventry University, Coventry, U.K"],"raw_orcid":"https://orcid.org/0000-0002-0096-4016","affiliations":[{"raw_affiliation_string":"Faculty of Engineering, Environment and Computing, Coventry University, Coventry, U.K","institution_ids":["https://openalex.org/I73417466"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5084735644","display_name":"A. Chroneos","orcid":"https://orcid.org/0000-0002-2558-495X"},"institutions":[{"id":"https://openalex.org/I73417466","display_name":"Coventry University","ror":"https://ror.org/01tgmhj36","country_code":"GB","type":"education","lineage":["https://openalex.org/I73417466"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Alexander Chroneos","raw_affiliation_strings":["Faculty of Engineering, Environment and Computing, Coventry University, Coventry, U.K"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Faculty of Engineering, Environment and Computing, Coventry University, Coventry, U.K","institution_ids":["https://openalex.org/I73417466"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5010336935"],"corresponding_institution_ids":["https://openalex.org/I200777214"],"apc_list":null,"apc_paid":null,"fwci":3.6004,"has_fulltext":false,"cited_by_count":35,"citation_normalized_percentile":{"value":0.92681156,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":"20","issue":"1","first_page":"65","last_page":"74"},"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/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9979000091552734,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9979000091552734,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/inertial-measurement-unit","display_name":"Inertial measurement unit","score":0.662774920463562},{"id":"https://openalex.org/keywords/global-positioning-system","display_name":"Global Positioning System","score":0.6315754652023315},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.609101414680481},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5666019916534424},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5355029106140137},{"id":"https://openalex.org/keywords/advanced-driver-assistance-systems","display_name":"Advanced driver assistance systems","score":0.4942300617694855},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.47888487577438354},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.44456690549850464},{"id":"https://openalex.org/keywords/accelerometer","display_name":"Accelerometer","score":0.4389181137084961},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.416811466217041},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.3489321768283844},{"id":"https://openalex.org/keywords/simulation","display_name":"Simulation","score":0.34850698709487915}],"concepts":[{"id":"https://openalex.org/C79061980","wikidata":"https://www.wikidata.org/wiki/Q941680","display_name":"Inertial measurement unit","level":2,"score":0.662774920463562},{"id":"https://openalex.org/C60229501","wikidata":"https://www.wikidata.org/wiki/Q18822","display_name":"Global Positioning System","level":2,"score":0.6315754652023315},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.609101414680481},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5666019916534424},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5355029106140137},{"id":"https://openalex.org/C87833898","wikidata":"https://www.wikidata.org/wiki/Q1060280","display_name":"Advanced driver assistance systems","level":2,"score":0.4942300617694855},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.47888487577438354},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.44456690549850464},{"id":"https://openalex.org/C89805583","wikidata":"https://www.wikidata.org/wiki/Q192940","display_name":"Accelerometer","level":2,"score":0.4389181137084961},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.416811466217041},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.3489321768283844},{"id":"https://openalex.org/C44154836","wikidata":"https://www.wikidata.org/wiki/Q45045","display_name":"Simulation","level":1,"score":0.34850698709487915},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","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/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/tits.2018.2797943","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tits.2018.2797943","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"},{"id":"pmh:oai:pure.atira.dk:openaire/f924f58e-3933-47b1-bf5a-2251a7d8e747","is_oa":true,"landing_page_url":"https://pureportal.coventry.ac.uk/en/publications/f924f58e-3933-47b1-bf5a-2251a7d8e747","pdf_url":null,"source":{"id":"https://openalex.org/S4306402411","display_name":"Pure (Coventry University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I73417466","host_organization_name":"Coventry University","host_organization_lineage":["https://openalex.org/I73417466"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Christopoulos, S, Kanarachos, S & Chroneos, A 2019, 'Learning Driver Braking Behavior using Smartphones, Neural Networks and the Sliding Correlation Coefficient : Road Anomaly Case Study', IEEE Transactions on Intelligent Transportation Systems, vol. 20, no. 1, pp. 65-74. https://doi.org/10.1109/TITS.2018.2797943","raw_type":"info:eu-repo/semantics/publishedVersion"},{"id":"pmh:oai:pure.atira.dk:publications/f924f58e-3933-47b1-bf5a-2251a7d8e747","is_oa":true,"landing_page_url":"https://pureportal.coventry.ac.uk/en/publications/learning-driver-braking-behavior-using-smartphones-neural-networks-and-the-sliding-correlation-coefficient(f924f58e-3933-47b1-bf5a-2251a7d8e747).html","pdf_url":null,"source":{"id":"https://openalex.org/S4306402411","display_name":"Pure (Coventry University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I73417466","host_organization_name":"Coventry University","host_organization_lineage":["https://openalex.org/I73417466"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Christopoulos , S , Kanarachos , S & Chroneos , A 2019 , ' Learning Driver Braking Behavior using Smartphones, Neural Networks and the Sliding Correlation Coefficient : Road Anomaly Case Study ' , IEEE Transactions on Intelligent Transportation Systems , vol. 20 , no. 1 , pp. 65-74 . https://doi.org/10.1109/TITS.2018.2797943","raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:pure.atira.dk:openaire/f924f58e-3933-47b1-bf5a-2251a7d8e747","is_oa":true,"landing_page_url":"https://pureportal.coventry.ac.uk/en/publications/f924f58e-3933-47b1-bf5a-2251a7d8e747","pdf_url":null,"source":{"id":"https://openalex.org/S4306402411","display_name":"Pure (Coventry University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I73417466","host_organization_name":"Coventry University","host_organization_lineage":["https://openalex.org/I73417466"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Christopoulos, S, Kanarachos, S & Chroneos, A 2019, 'Learning Driver Braking Behavior using Smartphones, Neural Networks and the Sliding Correlation Coefficient : Road Anomaly Case Study', IEEE Transactions on Intelligent Transportation Systems, vol. 20, no. 1, pp. 65-74. https://doi.org/10.1109/TITS.2018.2797943","raw_type":"info:eu-repo/semantics/publishedVersion"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":29,"referenced_works":["https://openalex.org/W756021388","https://openalex.org/W944070795","https://openalex.org/W1970678286","https://openalex.org/W1976092068","https://openalex.org/W1980774423","https://openalex.org/W1991801742","https://openalex.org/W2018690964","https://openalex.org/W2026430219","https://openalex.org/W2030833325","https://openalex.org/W2032585166","https://openalex.org/W2042294266","https://openalex.org/W2047249607","https://openalex.org/W2047258886","https://openalex.org/W2062417860","https://openalex.org/W2078483536","https://openalex.org/W2098458112","https://openalex.org/W2104107943","https://openalex.org/W2144169341","https://openalex.org/W2152259176","https://openalex.org/W2158698691","https://openalex.org/W2170581891","https://openalex.org/W2241968865","https://openalex.org/W2266143063","https://openalex.org/W2492460224","https://openalex.org/W2600728771","https://openalex.org/W2608847424","https://openalex.org/W2623684345","https://openalex.org/W2963441279","https://openalex.org/W6693501026"],"related_works":["https://openalex.org/W2181490969","https://openalex.org/W1493687195","https://openalex.org/W2554034262","https://openalex.org/W2124707714","https://openalex.org/W2532618464","https://openalex.org/W2274697715","https://openalex.org/W2002467261","https://openalex.org/W2186630593","https://openalex.org/W2164012680","https://openalex.org/W1946547943"],"abstract_inverted_index":{"This":[0],"paper":[1],"focuses":[2],"on":[3,70,86,109],"the":[4,12,38,47,60,68,99,114,121,173],"automated":[5],"learning":[6],"of":[7,14,41,59,82,124],"driver":[8,22,43,128,191],"braking":[9,75],"\u201csignature\u201d":[10],"in":[11],"presence":[13],"road":[15,100,125],"anomalies.":[16],"Our":[17],"motivation":[18],"is":[19,51,118,140],"to":[20,33,53,67,182],"improve":[21],"experience":[23],"using":[24,77,152,172],"preview":[25],"information":[26],"from":[27],"navigation":[28],"maps.":[29],"Smartphones":[30],"facilitate,":[31],"due":[32],"their":[34],"unprecedented":[35],"market":[36],"penetration,":[37],"large-scale":[39],"deployment":[40],"advanced":[42],"assistance":[44],"systems.":[45],"On":[46],"other":[48],"hand,":[49],"it":[50],"challenging":[52],"exploit":[54],"smartphone":[55],"sensor":[56],"data":[57,156],"because":[58],"fewer":[61],"and":[62,113,127,147,154,164,186],"lower":[63],"quality":[64],"signals,":[65],"compared":[66],"ones":[69],"board.":[71],"Methods":[72],"for":[73,120,189],"detecting":[74],"behavior":[76,97],"smartphones":[78,153],"exist,":[79],"however,":[80],"most":[81],"them":[83],"focus":[84],"only":[85,90],"harsh":[87],"events.":[88],"Additionally,":[89],"a":[91,105,155,184],"few":[92],"studies":[93],"correlate":[94],"longitudinal":[95],"driving":[96],"with":[98],"condition.":[101],"In":[102],"this":[103],"paper,":[104],"new":[106],"method,":[107],"based":[108],"deep":[110,132],"neural":[111,133],"networks":[112],"sliding":[115],"correlation":[116,123],"coefficient,":[117],"proposed":[119,168],"spatio-temporal":[122],"anomalies":[126],"behavior.":[129,192],"A":[130],"unique":[131],"network":[134],"structure,":[135],"that":[136],"requires":[137],"minimum":[138],"tuning,":[139],"proposed.":[141],"Extensive":[142],"field":[143],"trials":[144],"were":[145],"conducted":[146],"vehicle":[148],"motion":[149],"was":[150,170],"recorded":[151],"acquisition":[157],"system,":[158],"comprising":[159],"an":[160],"inertial":[161],"measurement":[162],"unit":[163],"differential":[165],"GPS.":[166],"The":[167,179],"method":[169,180],"validated":[171],"probabilistic":[174],"Receiver":[175],"Operating":[176],"Characteristics":[177],"method.":[178],"proves":[181],"be":[183],"robust":[185],"flexible":[187],"tool":[188],"self-learning":[190]},"counts_by_year":[{"year":2023,"cited_by_count":4},{"year":2022,"cited_by_count":7},{"year":2021,"cited_by_count":7},{"year":2020,"cited_by_count":6},{"year":2019,"cited_by_count":10},{"year":2018,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}