{"id":"https://openalex.org/W2905552256","doi":"https://doi.org/10.1109/itsc.2018.8569418","title":"Clustering Vehicle Maneuver Trajectories Using Mixtures of Hidden Markov Models","display_name":"Clustering Vehicle Maneuver Trajectories Using Mixtures of Hidden Markov Models","publication_year":2018,"publication_date":"2018-11-01","ids":{"openalex":"https://openalex.org/W2905552256","doi":"https://doi.org/10.1109/itsc.2018.8569418","mag":"2905552256"},"language":"en","primary_location":{"id":"doi:10.1109/itsc.2018.8569418","is_oa":false,"landing_page_url":"https://doi.org/10.1109/itsc.2018.8569418","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 21st International Conference on Intelligent Transportation Systems (ITSC)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5032224130","display_name":"John Martinsson","orcid":"https://orcid.org/0000-0002-5032-4367"},"institutions":[{"id":"https://openalex.org/I66862912","display_name":"Chalmers University of Technology","ror":"https://ror.org/040wg7k59","country_code":"SE","type":"education","lineage":["https://openalex.org/I66862912"]}],"countries":["SE"],"is_corresponding":false,"raw_author_name":"John Martinsson","raw_affiliation_strings":["Department of Computer Science and Engineering, Chalmers University of Technology, Sweden"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, Chalmers University of Technology, Sweden","institution_ids":["https://openalex.org/I66862912"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5081426544","display_name":"Nasser Mohammadiha","orcid":null},"institutions":[{"id":"https://openalex.org/I4210086940","display_name":"Zenuity (Sweden)","ror":"https://ror.org/000dpyn16","country_code":"SE","type":"company","lineage":["https://openalex.org/I4210086940"]},{"id":"https://openalex.org/I66862912","display_name":"Chalmers University of Technology","ror":"https://ror.org/040wg7k59","country_code":"SE","type":"education","lineage":["https://openalex.org/I66862912"]}],"countries":["SE"],"is_corresponding":false,"raw_author_name":"Nasser Mohammadiha","raw_affiliation_strings":["Zenuity AB, Chalmers University of Technology, Sweden"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Zenuity AB, Chalmers University of Technology, Sweden","institution_ids":["https://openalex.org/I4210086940","https://openalex.org/I66862912"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5057266594","display_name":"Alexander Schliep","orcid":"https://orcid.org/0000-0002-3555-3188"},"institutions":[{"id":"https://openalex.org/I881427289","display_name":"University of Gothenburg","ror":"https://ror.org/01tm6cn81","country_code":"SE","type":"education","lineage":["https://openalex.org/I881427289"]}],"countries":["SE"],"is_corresponding":false,"raw_author_name":"Alexander Schliep","raw_affiliation_strings":["Faculty of the Department of Computer Science and Engineering, University of Gothenburg, Sweden"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Faculty of the Department of Computer Science and Engineering, University of Gothenburg, Sweden","institution_ids":["https://openalex.org/I881427289"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":14,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"3698","last_page":"3705"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9923999905586243,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9923999905586243,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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":0.9919000267982483,"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/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9882000088691711,"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/computer-science","display_name":"Computer science","score":0.7477458715438843},{"id":"https://openalex.org/keywords/hidden-markov-model","display_name":"Hidden Markov model","score":0.7476170659065247},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.6639028787612915},{"id":"https://openalex.org/keywords/markov-process","display_name":"Markov process","score":0.5326022505760193},{"id":"https://openalex.org/keywords/markov-model","display_name":"Markov model","score":0.49775055050849915},{"id":"https://openalex.org/keywords/markov-chain","display_name":"Markov chain","score":0.4960976541042328},{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.4735409617424011},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4644598662853241},{"id":"https://openalex.org/keywords/ground-truth","display_name":"Ground truth","score":0.4458315968513489},{"id":"https://openalex.org/keywords/test-data","display_name":"Test data","score":0.44115594029426575},{"id":"https://openalex.org/keywords/markov-random-field","display_name":"Markov random field","score":0.421102374792099},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.4196837246417999},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.37138307094573975},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.17828696966171265},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.08906719088554382},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.08831799030303955}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7477458715438843},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.7476170659065247},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.6639028787612915},{"id":"https://openalex.org/C159886148","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov process","level":2,"score":0.5326022505760193},{"id":"https://openalex.org/C163836022","wikidata":"https://www.wikidata.org/wiki/Q6771326","display_name":"Markov model","level":3,"score":0.49775055050849915},{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.4960976541042328},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.4735409617424011},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4644598662853241},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.4458315968513489},{"id":"https://openalex.org/C16910744","wikidata":"https://www.wikidata.org/wiki/Q7705759","display_name":"Test data","level":2,"score":0.44115594029426575},{"id":"https://openalex.org/C2778045648","wikidata":"https://www.wikidata.org/wiki/Q176827","display_name":"Markov random field","level":4,"score":0.421102374792099},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.4196837246417999},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.37138307094573975},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.17828696966171265},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.08906719088554382},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.08831799030303955},{"id":"https://openalex.org/C1276947","wikidata":"https://www.wikidata.org/wiki/Q333","display_name":"Astronomy","level":1,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","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/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"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":2,"locations":[{"id":"doi:10.1109/itsc.2018.8569418","is_oa":false,"landing_page_url":"https://doi.org/10.1109/itsc.2018.8569418","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 21st International Conference on Intelligent Transportation Systems (ITSC)","raw_type":"proceedings-article"},{"id":"pmh:oai:research.chalmers.se:508459","is_oa":false,"landing_page_url":"https://research.chalmers.se/en/publication/508459","pdf_url":null,"source":{"id":"https://openalex.org/S4306402469","display_name":"Chalmers Research (Chalmers University of Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I66862912","host_organization_name":"Chalmers University of Technology","host_organization_lineage":["https://openalex.org/I66862912"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.6399999856948853,"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320321030","display_name":"VINNOVA","ror":"https://ror.org/01kd5m353"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W1480276090","https://openalex.org/W1515257115","https://openalex.org/W1985486010","https://openalex.org/W2047555270","https://openalex.org/W2080972498","https://openalex.org/W2109328551","https://openalex.org/W2109820980","https://openalex.org/W2124660252","https://openalex.org/W2125838338","https://openalex.org/W2132875213","https://openalex.org/W2162833336","https://openalex.org/W2163811214","https://openalex.org/W2168175751","https://openalex.org/W2170098929","https://openalex.org/W2523958427","https://openalex.org/W2525936901","https://openalex.org/W2565390496","https://openalex.org/W2884253562","https://openalex.org/W2904997753","https://openalex.org/W2963632944","https://openalex.org/W4297806271","https://openalex.org/W6628701064","https://openalex.org/W6679651670","https://openalex.org/W6684050148","https://openalex.org/W6745344099","https://openalex.org/W6752999189","https://openalex.org/W6757415282"],"related_works":["https://openalex.org/W2350115929","https://openalex.org/W2118728396","https://openalex.org/W2963414935","https://openalex.org/W2039473210","https://openalex.org/W2901259486","https://openalex.org/W4289285260","https://openalex.org/W2111983527","https://openalex.org/W987019958","https://openalex.org/W1511076251","https://openalex.org/W1823779199"],"abstract_inverted_index":{"The":[0,99,120],"safety":[1],"of":[2,33,79,106,132,148,165],"autonomous":[3,19,47],"vehicles":[4,20],"needs":[5],"to":[6,17,43,83,93,143,172],"be":[7,68,91,153,170],"verified":[8],"and":[9,24,39,63,70,105,134,182],"validated":[10],"by":[11],"rigorous":[12],"testing.":[13],"It":[14],"is":[15,122,141],"expensive":[16],"test":[18,45,129],"in":[21,49,86,176],"the":[22,46,96,118,149],"field,":[23],"therefore":[25],"virtual":[26,51],"testing":[27],"methods":[28],"are":[29,41,102],"needed.":[30],"Generative":[31],"models":[32,56,82,113,168],"maneuvers":[34,157],"such":[35,55,65],"as":[36,155],"cut-ins,":[37],"overtakes,":[38],"lane-keeping":[40],"needed":[42],"thoroughly":[44],"vehicle":[48],"a":[50,77,114,128],"environment.":[52],"To":[53],"train":[54],"we":[57,75],"need":[58],"ground":[59],"truth":[60],"maneuver":[61,87,100,178],"labels":[62,66],"obtaining":[64],"can":[67,90,152,169],"time-consuming":[69],"costly.":[71],"In":[72],"this":[73],"work,":[74],"use":[76],"mixture":[78],"hidden":[80,111,166],"Markov":[81,112,167],"find":[84,173],"clusters":[85,150],"trajectories,":[88],"which":[89,109],"used":[92,171],"speed":[94],"up":[95],"labeling":[97],"process.":[98],"trajectories":[101],"noisy,":[103],"asynchronous":[104],"uneven":[107],"length,":[108],"make":[110],"good":[115],"fit":[116],"for":[117],"data.":[119],"method":[121],"evaluated":[123],"on":[124],"labeled":[125],"data":[126,145,179],"from":[127,180],"track":[130],"consisting":[131],"cut-ins":[133],"overtakes":[135],"with":[136],"favorable":[137],"results.":[138],"Further,":[139],"it":[140],"applied":[142],"natural":[144],"where":[146],"many":[147],"found":[151],"interpreted":[154],"driver":[156,177],"under":[158],"reasonable":[159],"assumptions.":[160],"We":[161],"show":[162],"that":[163],"mixtures":[164],"motion":[174],"patterns":[175],"highways":[181],"country":[183],"roads.":[184]},"counts_by_year":[{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":5},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":2}],"updated_date":"2026-07-15T18:14:33.161393","created_date":"2025-10-10T00:00:00"}