{"id":"https://openalex.org/W3007391509","doi":"https://doi.org/10.1109/bigdata47090.2019.9005982","title":"Classification of Driving Behavior Events Utilizing Kinematic Classification and Machine Learning for Down Sampled Time Series Data","display_name":"Classification of Driving Behavior Events Utilizing Kinematic Classification and Machine Learning for Down Sampled Time Series Data","publication_year":2019,"publication_date":"2019-12-01","ids":{"openalex":"https://openalex.org/W3007391509","doi":"https://doi.org/10.1109/bigdata47090.2019.9005982","mag":"3007391509"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata47090.2019.9005982","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata47090.2019.9005982","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-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/A5068804090","display_name":"Vikram Krishnamurthy","orcid":"https://orcid.org/0000-0002-4170-6056"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Vikram Krishnamurthy","raw_affiliation_strings":["ACV Analytics Alliance Innovation Lab Silicon Valley, Santa Clara, USA"],"affiliations":[{"raw_affiliation_string":"ACV Analytics Alliance Innovation Lab Silicon Valley, Santa Clara, USA","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5009060010","display_name":"Kusha Nezafati","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kusha Nezafati","raw_affiliation_strings":["ACV Analytics Alliance Innovation Lab Silicon Valley, Santa Clara, USA"],"affiliations":[{"raw_affiliation_string":"ACV Analytics Alliance Innovation Lab Silicon Valley, Santa Clara, USA","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100780771","display_name":"J. Alexander Bae","orcid":"https://orcid.org/0000-0002-4681-6342"},"institutions":[{"id":"https://openalex.org/I130701444","display_name":"Georgia Institute of Technology","ror":"https://ror.org/01zkghx44","country_code":"US","type":"education","lineage":["https://openalex.org/I130701444"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Juhyun Bae","raw_affiliation_strings":["College of Computing, Georgia Institute of Technology, Atlanta, USA"],"affiliations":[{"raw_affiliation_string":"College of Computing, Georgia Institute of Technology, Atlanta, USA","institution_ids":["https://openalex.org/I130701444"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006858624","display_name":"Emre G\u00fcrsoy","orcid":null},"institutions":[{"id":"https://openalex.org/I130701444","display_name":"Georgia Institute of Technology","ror":"https://ror.org/01zkghx44","country_code":"US","type":"education","lineage":["https://openalex.org/I130701444"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Emre Gursoy","raw_affiliation_strings":["College of Computing, Georgia Institute of Technology, Atlanta, USA"],"affiliations":[{"raw_affiliation_string":"College of Computing, Georgia Institute of Technology, Atlanta, USA","institution_ids":["https://openalex.org/I130701444"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100633981","display_name":"Mian Zhong","orcid":"https://orcid.org/0009-0009-4546-7214"},"institutions":[{"id":"https://openalex.org/I95457486","display_name":"University of California, Berkeley","ror":"https://ror.org/01an7q238","country_code":"US","type":"education","lineage":["https://openalex.org/I95457486"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Mian Zhong","raw_affiliation_strings":["Dept. of Mathematics, University of California, Berkeley, Berkeley, USA"],"affiliations":[{"raw_affiliation_string":"Dept. of Mathematics, University of California, Berkeley, Berkeley, USA","institution_ids":["https://openalex.org/I95457486"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101866881","display_name":"Vikrant Singh","orcid":"https://orcid.org/0000-0003-1625-1309"},"institutions":[{"id":"https://openalex.org/I95457486","display_name":"University of California, Berkeley","ror":"https://ror.org/01an7q238","country_code":"US","type":"education","lineage":["https://openalex.org/I95457486"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Vikrant Singh","raw_affiliation_strings":["Institute of Transportation Studies University of California, Berkeley, Berkeley, USA"],"affiliations":[{"raw_affiliation_string":"Institute of Transportation Studies University of California, Berkeley, Berkeley, USA","institution_ids":["https://openalex.org/I95457486"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5068804090"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.19765823,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"3","issue":null,"first_page":"3789","last_page":"3796"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9983999729156494,"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":0.9983999729156494,"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/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.9979000091552734,"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9977999925613403,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/computer-science","display_name":"Computer science","score":0.7066383957862854},{"id":"https://openalex.org/keywords/timestamp","display_name":"Timestamp","score":0.6647065281867981},{"id":"https://openalex.org/keywords/kinematics","display_name":"Kinematics","score":0.6261197328567505},{"id":"https://openalex.org/keywords/data-set","display_name":"Data set","score":0.5208837985992432},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.49076804518699646},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.4896135926246643},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4287329614162445},{"id":"https://openalex.org/keywords/precision-and-recall","display_name":"Precision and recall","score":0.4266960024833679},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4233250916004181},{"id":"https://openalex.org/keywords/vehicle-dynamics","display_name":"Vehicle dynamics","score":0.41700994968414307},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.41665804386138916},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.41219276189804077},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.19690266251564026}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7066383957862854},{"id":"https://openalex.org/C113954288","wikidata":"https://www.wikidata.org/wiki/Q186885","display_name":"Timestamp","level":2,"score":0.6647065281867981},{"id":"https://openalex.org/C39920418","wikidata":"https://www.wikidata.org/wiki/Q11476","display_name":"Kinematics","level":2,"score":0.6261197328567505},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.5208837985992432},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.49076804518699646},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.4896135926246643},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4287329614162445},{"id":"https://openalex.org/C81669768","wikidata":"https://www.wikidata.org/wiki/Q2359161","display_name":"Precision and recall","level":2,"score":0.4266960024833679},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4233250916004181},{"id":"https://openalex.org/C79487989","wikidata":"https://www.wikidata.org/wiki/Q934680","display_name":"Vehicle dynamics","level":2,"score":0.41700994968414307},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.41665804386138916},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.41219276189804077},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.19690266251564026},{"id":"https://openalex.org/C171146098","wikidata":"https://www.wikidata.org/wiki/Q124192","display_name":"Automotive engineering","level":1,"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/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata47090.2019.9005982","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata47090.2019.9005982","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W1557919099","https://openalex.org/W2039869565","https://openalex.org/W2040820346","https://openalex.org/W2045064676","https://openalex.org/W2073537988","https://openalex.org/W2086234878","https://openalex.org/W2097747115","https://openalex.org/W2148143831","https://openalex.org/W2155105886","https://openalex.org/W2164330572","https://openalex.org/W2553910756","https://openalex.org/W2584176441","https://openalex.org/W2752788177","https://openalex.org/W2768960721","https://openalex.org/W2800482383","https://openalex.org/W2911303748","https://openalex.org/W2957967900","https://openalex.org/W2963351448","https://openalex.org/W4293580161","https://openalex.org/W6729763630","https://openalex.org/W6750949408","https://openalex.org/W6765889037"],"related_works":["https://openalex.org/W2060561905","https://openalex.org/W1417711376","https://openalex.org/W2032260263","https://openalex.org/W1986883493","https://openalex.org/W2469862403","https://openalex.org/W2166378262","https://openalex.org/W2035891203","https://openalex.org/W4379524643","https://openalex.org/W2011027677","https://openalex.org/W4287824979"],"abstract_inverted_index":{"The":[0,131],"proliferation":[1],"of":[2,12,36,56,62,71,102,124,135,142,196,206],"connected":[3,37,79,213],"cars":[4],"globally":[5],"has":[6,53],"the":[7,57,67,78,82,100],"potential":[8],"to":[9,81,113,155,167],"produce":[10],"torrents":[11],"Big":[13],"Data":[14],"that":[15],"will":[16],"enable":[17],"improvements":[18,26],"in":[19,27,128,177],"driver":[20],"safety,":[21],"new":[22],"location":[23],"based":[24],"services,":[25],"vehicle":[28,32,118,144,147],"quality,":[29],"and":[30,44,69,90,146,199,204,208],"optimized":[31],"designs.":[33],"One":[34],"aspect":[35],"car":[38,80,214],"data":[39,43,76,88,119,140,160,187,215],"involves":[40],"driving":[41,63,74,125,158,170],"behavior":[42,64,75,126,159],"its":[45],"use":[46],"for":[47,121,172,210],"Usage":[48],"Based":[49],"Insurance":[50],"(UBI).":[51],"UBI":[52,129],"become":[54],"one":[55],"most":[58],"widely":[59],"used":[60,166],"applications":[61],"data.":[65,221],"Currently,":[66],"transmission":[68],"processing":[70],"high":[72],"frequency":[73],"from":[77],"cloud":[83],"is":[84,149,165],"limited":[85,194],"by":[86,190],"wireless":[87],"costs":[89],"in-vehicle":[91],"hardware":[92],"complexity.":[93],"To":[94],"alleviate":[95],"these":[96],"issues,":[97],"we":[98],"detail":[99],"development":[101],"a":[103,108,193],"machine":[104,163,183],"learning":[105,164,184],"framework":[106],"utilizing":[107],"kinematic":[109,153,179],"classification":[110],"methodology":[111,185],"applied":[112],"down":[114],"sampled":[115],"time":[116],"series":[117],"sets":[120,216],"accurate":[122],"imputation":[123],"events":[127,171],"applications.":[130],"down-sampled":[132,220],"data,":[133],"consisting":[134],"5":[136,174],"second":[137,175],"frames":[138],"with":[139,217],"fields":[141],"timestamp,":[143],"speed,":[145],"acceleration":[148],"classified":[150],"into":[151],"unique":[152],"clusters":[154],"standardize":[156],"any":[157],"distributions.":[161],"Subsequently,":[162],"impute":[168],"harsh":[169],"each":[173],"frame":[176],"select":[178],"clusters.":[180],"This":[181],"novel":[182],"reduced":[186],"set":[188,195],"sizes":[189],"75%,":[191],"utilized":[192],"five":[197],"attributes,":[198],"achieved":[200],"an":[201],"average":[202],"precision":[203],"recall":[205],"84.5%":[207],"63.5%":[209],"two":[211],"distinct":[212],"1/5":[218],"Hz":[219]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}