{"id":"https://openalex.org/W2740072693","doi":"https://doi.org/10.1109/ivs.2017.7995927","title":"Exact inference and learning in hybrid Bayesian Networks for lane change intention classification","display_name":"Exact inference and learning in hybrid Bayesian Networks for lane change intention classification","publication_year":2017,"publication_date":"2017-06-01","ids":{"openalex":"https://openalex.org/W2740072693","doi":"https://doi.org/10.1109/ivs.2017.7995927","mag":"2740072693"},"language":"en","primary_location":{"id":"doi:10.1109/ivs.2017.7995927","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ivs.2017.7995927","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 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/A5030763116","display_name":"Alexander Koenig","orcid":"https://orcid.org/0000-0001-5828-6409"},"institutions":[{"id":"https://openalex.org/I4210156768","display_name":"BMW Group (Germany)","ror":"https://ror.org/044kkbh92","country_code":"DE","type":"company","lineage":["https://openalex.org/I4210156768"]},{"id":"https://openalex.org/I1283382300","display_name":"BMW (Germany)","ror":"https://ror.org/05vs9tj88","country_code":"DE","type":"company","lineage":["https://openalex.org/I1283382300","https://openalex.org/I4210156768"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Alexander Koenig","raw_affiliation_strings":["Department of Highly Automated Driving, BMW Group, Munich, Germany"],"affiliations":[{"raw_affiliation_string":"Department of Highly Automated Driving, BMW Group, Munich, Germany","institution_ids":["https://openalex.org/I1283382300","https://openalex.org/I4210156768"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5033474421","display_name":"Tobias Rehder","orcid":"https://orcid.org/0000-0003-1494-9383"},"institutions":[{"id":"https://openalex.org/I4210156768","display_name":"BMW Group (Germany)","ror":"https://ror.org/044kkbh92","country_code":"DE","type":"company","lineage":["https://openalex.org/I4210156768"]},{"id":"https://openalex.org/I1283382300","display_name":"BMW (Germany)","ror":"https://ror.org/05vs9tj88","country_code":"DE","type":"company","lineage":["https://openalex.org/I1283382300","https://openalex.org/I4210156768"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Tobias Rehder","raw_affiliation_strings":["Department of Highly Automated Driving, BMW Group, Munich, Germany"],"affiliations":[{"raw_affiliation_string":"Department of Highly Automated Driving, BMW Group, Munich, Germany","institution_ids":["https://openalex.org/I1283382300","https://openalex.org/I4210156768"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5091569424","display_name":"Soeren Hohmann","orcid":null},"institutions":[{"id":"https://openalex.org/I102335020","display_name":"Karlsruhe Institute of Technology","ror":"https://ror.org/04t3en479","country_code":"DE","type":"education","lineage":["https://openalex.org/I102335020","https://openalex.org/I1305996414"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Soeren Hohmann","raw_affiliation_strings":["Head of the Institute for Control Systems, KIT, Karlsruhe, Germany"],"affiliations":[{"raw_affiliation_string":"Head of the Institute for Control Systems, KIT, Karlsruhe, Germany","institution_ids":["https://openalex.org/I102335020"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5030763116"],"corresponding_institution_ids":["https://openalex.org/I1283382300","https://openalex.org/I4210156768"],"apc_list":null,"apc_paid":null,"fwci":0.702,"has_fulltext":false,"cited_by_count":12,"citation_normalized_percentile":{"value":0.74863492,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"1535","last_page":"1540"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.9983000159263611,"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":0.9983000159263611,"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/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.9973000288009644,"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/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9886000156402588,"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/toolbox","display_name":"Toolbox","score":0.7202720046043396},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6978169083595276},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.6888568997383118},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6539994478225708},{"id":"https://openalex.org/keywords/bayesian-network","display_name":"Bayesian network","score":0.628661036491394},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6172031164169312},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5579090118408203},{"id":"https://openalex.org/keywords/bayes-theorem","display_name":"Bayes' theorem","score":0.5575700402259827},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5524718165397644},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.5287045240402222},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.47848671674728394},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.46849164366722107},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.42413967847824097}],"concepts":[{"id":"https://openalex.org/C2777655017","wikidata":"https://www.wikidata.org/wiki/Q1501161","display_name":"Toolbox","level":2,"score":0.7202720046043396},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6978169083595276},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6888568997383118},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6539994478225708},{"id":"https://openalex.org/C33724603","wikidata":"https://www.wikidata.org/wiki/Q812540","display_name":"Bayesian network","level":2,"score":0.628661036491394},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6172031164169312},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5579090118408203},{"id":"https://openalex.org/C207201462","wikidata":"https://www.wikidata.org/wiki/Q182505","display_name":"Bayes' theorem","level":3,"score":0.5575700402259827},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5524718165397644},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.5287045240402222},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.47848671674728394},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.46849164366722107},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.42413967847824097},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"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/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ivs.2017.7995927","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ivs.2017.7995927","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE Intelligent Vehicles Symposium (IV)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W113954766","https://openalex.org/W1511986666","https://openalex.org/W1522301498","https://openalex.org/W1533861849","https://openalex.org/W1561218590","https://openalex.org/W1577436411","https://openalex.org/W1598269389","https://openalex.org/W2046584898","https://openalex.org/W2049633694","https://openalex.org/W2083651391","https://openalex.org/W2142258168","https://openalex.org/W2271840356","https://openalex.org/W2285793995","https://openalex.org/W2582970877","https://openalex.org/W2979006918","https://openalex.org/W3129711340","https://openalex.org/W4246718066","https://openalex.org/W6634556777","https://openalex.org/W6695817743","https://openalex.org/W6768650569","https://openalex.org/W7048738093"],"related_works":["https://openalex.org/W4205140848","https://openalex.org/W2068663075","https://openalex.org/W2978678743","https://openalex.org/W2797837731","https://openalex.org/W4393677513","https://openalex.org/W4390832911","https://openalex.org/W829257147","https://openalex.org/W4385302116","https://openalex.org/W3081389670","https://openalex.org/W2150344375"],"abstract_inverted_index":{"Determining":[0],"the":[1,81,89,106],"current":[2],"intentions":[3,60],"of":[4,44],"other":[5],"drivers":[6],"is":[7],"essential":[8],"for":[9,31,38,77,109],"correctly":[10],"predicting":[11],"or":[12,26],"simulating":[13],"their":[14],"future":[15],"actions.":[16],"Especially":[17],"unpredicted":[18],"lane":[19,110],"changes":[20,111],"can":[21],"result":[22],"in":[23,65],"very":[24],"uncomfortable":[25],"even":[27],"dangerous":[28],"braking":[29],"maneuvers":[30],"succeeding":[32],"vehicles.":[33],"Bayesian":[34],"Networks":[35],"(BN)":[36],"allow":[37],"a":[39,92,97,113],"physically":[40],"motivated":[41],"probabilistic":[42],"representation":[43],"features":[45,50],"influencing":[46],"driver":[47],"intentions.":[48],"While":[49],"often":[51],"take":[52],"continuous":[53],"values,":[54],"e.g.":[55],"velocity":[56],"and":[57,70],"distance,":[58],"maneuver":[59],"are":[61],"discrete,":[62],"which":[63],"results":[64],"hybrid":[66,78],"BN.":[67],"For":[68],"efficient":[69],"exact":[71],"inference,":[72],"we":[73,87,104],"implement":[74],"an":[75],"approach":[76,90],"nets":[79],"into":[80],"original":[82],"Bayes":[83],"Net":[84],"Toolbox.":[85],"Furthermore,":[86],"extend":[88],"with":[91,99,112],"learning":[93],"component":[94],"to":[95],"train":[96],"BN":[98],"simulated":[100],"traffic":[101],"data.":[102],"Finally,":[103],"compare":[105],"classification":[107],"performance":[108],"Deep":[114],"Neural":[115],"Network":[116],"(DNN)":[117],"classifier.":[118]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":1},{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}