{"id":"https://openalex.org/W2805334546","doi":"https://doi.org/10.1109/ivs.2018.8500529","title":"Cluster Naturalistic Driving Encounters Using Deep Unsupervised Learning","display_name":"Cluster Naturalistic Driving Encounters Using Deep Unsupervised Learning","publication_year":2018,"publication_date":"2018-06-01","ids":{"openalex":"https://openalex.org/W2805334546","doi":"https://doi.org/10.1109/ivs.2018.8500529","mag":"2805334546"},"language":"en","primary_location":{"id":"doi:10.1109/ivs.2018.8500529","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ivs.2018.8500529","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE Intelligent Vehicles Symposium (IV)","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/A5100741170","display_name":"Sisi Li","orcid":"https://orcid.org/0000-0003-4870-3840"},"institutions":[{"id":"https://openalex.org/I27837315","display_name":"University of Michigan\u2013Ann Arbor","ror":"https://ror.org/00jmfr291","country_code":"US","type":"education","lineage":["https://openalex.org/I27837315"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Sisi Li","raw_affiliation_strings":["Robotics Institute, University of Michigan, Ann Arbor, MI"],"affiliations":[{"raw_affiliation_string":"Robotics Institute, University of Michigan, Ann Arbor, MI","institution_ids":["https://openalex.org/I27837315"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5055099598","display_name":"Wenshuo Wang","orcid":"https://orcid.org/0000-0002-1860-8351"},"institutions":[{"id":"https://openalex.org/I27837315","display_name":"University of Michigan\u2013Ann Arbor","ror":"https://ror.org/00jmfr291","country_code":"US","type":"education","lineage":["https://openalex.org/I27837315"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Wenshuo Wang","raw_affiliation_strings":["Department of Mechanical Engineering, University of Michigan, Ann Arbor"],"affiliations":[{"raw_affiliation_string":"Department of Mechanical Engineering, University of Michigan, Ann Arbor","institution_ids":["https://openalex.org/I27837315"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5019056538","display_name":"Zhaobin Mo","orcid":"https://orcid.org/0000-0002-0465-8550"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhaobin Mo","raw_affiliation_strings":["Automotive Engineering at the Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Automotive Engineering at the Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5037644321","display_name":"Ding Zhao","orcid":"https://orcid.org/0000-0002-9400-8446"},"institutions":[{"id":"https://openalex.org/I27837315","display_name":"University of Michigan\u2013Ann Arbor","ror":"https://ror.org/00jmfr291","country_code":"US","type":"education","lineage":["https://openalex.org/I27837315"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ding Zhao","raw_affiliation_strings":["Department of Mechanical Engineering, University of Michigan, Ann Arbor"],"affiliations":[{"raw_affiliation_string":"Department of Mechanical Engineering, University of Michigan, Ann Arbor","institution_ids":["https://openalex.org/I27837315"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5100741170"],"corresponding_institution_ids":["https://openalex.org/I27837315"],"apc_list":null,"apc_paid":null,"fwci":1.4624,"has_fulltext":false,"cited_by_count":31,"citation_normalized_percentile":{"value":0.86733389,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1354","last_page":"1359"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9959999918937683,"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":0.9959999918937683,"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/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.9947999715805054,"subfield":{"id":"https://openalex.org/subfields/3313","display_name":"Transportation"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9941999912261963,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.8415806293487549},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6389918923377991},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5967400074005127},{"id":"https://openalex.org/keywords/unsupervised-learning","display_name":"Unsupervised learning","score":0.586786687374115},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5696649551391602},{"id":"https://openalex.org/keywords/cluster","display_name":"Cluster (spacecraft)","score":0.5407028794288635},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4719901382923126},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.44353240728378296},{"id":"https://openalex.org/keywords/naturalistic-observation","display_name":"Naturalistic observation","score":0.4296633005142212},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.42523202300071716},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.38786783814430237},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.08684107661247253},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.07608366012573242}],"concepts":[{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.8415806293487549},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6389918923377991},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5967400074005127},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.586786687374115},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5696649551391602},{"id":"https://openalex.org/C164866538","wikidata":"https://www.wikidata.org/wiki/Q367351","display_name":"Cluster (spacecraft)","level":2,"score":0.5407028794288635},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4719901382923126},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.44353240728378296},{"id":"https://openalex.org/C167699689","wikidata":"https://www.wikidata.org/wiki/Q1521337","display_name":"Naturalistic observation","level":2,"score":0.4296633005142212},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.42523202300071716},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.38786783814430237},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.08684107661247253},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.07608366012573242},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","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":1,"locations":[{"id":"doi:10.1109/ivs.2018.8500529","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ivs.2018.8500529","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE Intelligent Vehicles Symposium (IV)","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":29,"referenced_works":["https://openalex.org/W1527702126","https://openalex.org/W2011430131","https://openalex.org/W2039582020","https://openalex.org/W2145094598","https://openalex.org/W2438413413","https://openalex.org/W2468121020","https://openalex.org/W2511072509","https://openalex.org/W2517236235","https://openalex.org/W2528043655","https://openalex.org/W2537623947","https://openalex.org/W2573941966","https://openalex.org/W2578339457","https://openalex.org/W2584539651","https://openalex.org/W2586457790","https://openalex.org/W2679723396","https://openalex.org/W2733549015","https://openalex.org/W2741086815","https://openalex.org/W2745090846","https://openalex.org/W2746721413","https://openalex.org/W2755725656","https://openalex.org/W2963898834","https://openalex.org/W2964110963","https://openalex.org/W2997574889","https://openalex.org/W3100672372","https://openalex.org/W6681096077","https://openalex.org/W6725417818","https://openalex.org/W6725477328","https://openalex.org/W6733010505","https://openalex.org/W6742915796"],"related_works":["https://openalex.org/W3013693939","https://openalex.org/W2159052453","https://openalex.org/W2566616303","https://openalex.org/W3131327266","https://openalex.org/W2734887215","https://openalex.org/W4321789545","https://openalex.org/W2806873178","https://openalex.org/W2965146396","https://openalex.org/W2770818364","https://openalex.org/W4312416532"],"abstract_inverted_index":{"Learning":[0],"knowledge":[1],"from":[2],"driving":[3,13,30,55],"encounters":[4,31,56],"could":[5],"help":[6],"self-driving":[7],"cars":[8],"make":[9],"appropriate":[10],"decisions":[11],"when":[12],"in":[14,67],"complex":[15],"settings":[16],"with":[17,39,77],"nearby":[18],"vehicles":[19],"engaged.":[20],"This":[21],"paper":[22],"develops":[23],"an":[24,37],"unsupervised":[25],"classifier":[26],"to":[27],"group":[28],"naturalistic":[29,54],"into":[32],"distinguishable":[33],"clusters":[34],"by":[35,60],"combining":[36],"auto-encoder":[38],"k-means":[40,79,93],"clustering":[41,80,94],"(AE-kMC).":[42],"The":[43],"effectiveness":[44],"of":[45,52,63],"AE-kMC":[46,88],"was":[47],"validated":[48],"using":[49],"the":[50,61,68,78,87,91],"data":[51],"10,000":[53],"which":[57],"were":[58],"collected":[59],"University":[62],"Michigan,":[64],"Ann":[65],"Arbor":[66],"past":[69],"five":[70],"years.":[71],"We":[72],"compare":[73],"our":[74],"developed":[75],"method":[76,89],"methods":[81],"and":[82],"experimental":[83],"results":[84],"demonstrate":[85],"that":[86],"outperforms":[90],"original":[92],"method.":[95]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":5},{"year":2021,"cited_by_count":8},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":3},{"year":2018,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
