{"id":"https://openalex.org/W2165609373","doi":"https://doi.org/10.1109/robot.2009.5152838","title":"Probabilistic situation recognition for vehicular traffic scenarios","display_name":"Probabilistic situation recognition for vehicular traffic scenarios","publication_year":2009,"publication_date":"2009-05-01","ids":{"openalex":"https://openalex.org/W2165609373","doi":"https://doi.org/10.1109/robot.2009.5152838","mag":"2165609373"},"language":"en","primary_location":{"id":"doi:10.1109/robot.2009.5152838","is_oa":false,"landing_page_url":"https://doi.org/10.1109/robot.2009.5152838","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2009 IEEE International Conference on Robotics and Automation","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/A5074074068","display_name":"Daniel Meyer-Delius","orcid":null},"institutions":[{"id":"https://openalex.org/I161046081","display_name":"University of Freiburg","ror":"https://ror.org/0245cg223","country_code":"DE","type":"education","lineage":["https://openalex.org/I161046081"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"D. Meyer-Delius","raw_affiliation_strings":["Department of Computer Science, University of Freiburg, Freiburg im Breisgau, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science, University of Freiburg, Freiburg im Breisgau, Germany","institution_ids":["https://openalex.org/I161046081"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5084071835","display_name":"Christian Plagemann","orcid":null},"institutions":[{"id":"https://openalex.org/I161046081","display_name":"University of Freiburg","ror":"https://ror.org/0245cg223","country_code":"DE","type":"education","lineage":["https://openalex.org/I161046081"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"C. Plagemann","raw_affiliation_strings":["Department of Computer Science, University of Freiburg, Freiburg im Breisgau, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science, University of Freiburg, Freiburg im Breisgau, Germany","institution_ids":["https://openalex.org/I161046081"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5084499878","display_name":"Wolfram Burgard","orcid":"https://orcid.org/0000-0002-5680-6500"},"institutions":[{"id":"https://openalex.org/I161046081","display_name":"University of Freiburg","ror":"https://ror.org/0245cg223","country_code":"DE","type":"education","lineage":["https://openalex.org/I161046081"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"W. Burgard","raw_affiliation_strings":["Department of Computer Science, University of Freiburg, Freiburg im Breisgau, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science, University of Freiburg, Freiburg im Breisgau, Germany","institution_ids":["https://openalex.org/I161046081"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I161046081"],"apc_list":null,"apc_paid":null,"fwci":6.6982,"has_fulltext":false,"cited_by_count":73,"citation_normalized_percentile":{"value":0.97450215,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"459","last_page":"464"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11106","display_name":"Data Management and Algorithms","score":0.9987000226974487,"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"}},"topics":[{"id":"https://openalex.org/T11106","display_name":"Data Management and Algorithms","score":0.9987000226974487,"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"}},{"id":"https://openalex.org/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.998199999332428,"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9966999888420105,"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":0.8065764904022217},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.7431912422180176},{"id":"https://openalex.org/keywords/hidden-markov-model","display_name":"Hidden Markov model","score":0.6221997737884521},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5894202589988708},{"id":"https://openalex.org/keywords/position","display_name":"Position (finance)","score":0.4750345051288605},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4738198518753052},{"id":"https://openalex.org/keywords/markov-process","display_name":"Markov process","score":0.46886521577835083},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4540889263153076},{"id":"https://openalex.org/keywords/track","display_name":"Track (disk drive)","score":0.44974732398986816},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4205531179904938},{"id":"https://openalex.org/keywords/interpretation","display_name":"Interpretation (philosophy)","score":0.4111461937427521}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8065764904022217},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.7431912422180176},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.6221997737884521},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5894202589988708},{"id":"https://openalex.org/C198082294","wikidata":"https://www.wikidata.org/wiki/Q3399648","display_name":"Position (finance)","level":2,"score":0.4750345051288605},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4738198518753052},{"id":"https://openalex.org/C159886148","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov process","level":2,"score":0.46886521577835083},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4540889263153076},{"id":"https://openalex.org/C89992363","wikidata":"https://www.wikidata.org/wiki/Q5961558","display_name":"Track (disk drive)","level":2,"score":0.44974732398986816},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4205531179904938},{"id":"https://openalex.org/C527412718","wikidata":"https://www.wikidata.org/wiki/Q855395","display_name":"Interpretation (philosophy)","level":2,"score":0.4111461937427521},{"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},{"id":"https://openalex.org/C10138342","wikidata":"https://www.wikidata.org/wiki/Q43015","display_name":"Finance","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/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1109/robot.2009.5152838","is_oa":false,"landing_page_url":"https://doi.org/10.1109/robot.2009.5152838","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2009 IEEE International Conference on Robotics and Automation","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.337.1030","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.337.1030","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.stanford.edu/~plagem/bib/meyerdelius09icra.pdf","raw_type":"text"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.421.2906","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.421.2906","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://ais.informatik.uni-freiburg.de/publications/papers/meyerdelius09icra.pdf","raw_type":"text"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.522.9044","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.522.9044","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.informatik.uni-freiburg.de/~meyerdel/publications/meyer-delius09icra.pdf","raw_type":"text"}],"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/W852874","https://openalex.org/W64504817","https://openalex.org/W158032981","https://openalex.org/W186516023","https://openalex.org/W1480040460","https://openalex.org/W1506588809","https://openalex.org/W1518565144","https://openalex.org/W1528056001","https://openalex.org/W1537435730","https://openalex.org/W1539708367","https://openalex.org/W1594913267","https://openalex.org/W2020294948","https://openalex.org/W2049633694","https://openalex.org/W2122410182","https://openalex.org/W2125838338","https://openalex.org/W2149429918","https://openalex.org/W2152239535","https://openalex.org/W2161484642","https://openalex.org/W2623293810","https://openalex.org/W2899987233","https://openalex.org/W6602599886","https://openalex.org/W6632101533"],"related_works":["https://openalex.org/W2364370872","https://openalex.org/W2053269318","https://openalex.org/W2025614924","https://openalex.org/W2294335174","https://openalex.org/W2097963413","https://openalex.org/W1591475660","https://openalex.org/W2001275470","https://openalex.org/W2073996508","https://openalex.org/W3145575561","https://openalex.org/W830459867"],"abstract_inverted_index":{"To":[0],"act":[1],"intelligently":[2],"in":[3,16,113,137],"dynamic":[4],"environments,":[5],"a":[6,44,54,57],"system":[7,127],"must":[8],"understand":[9],"the":[10,80,157,160],"current":[11],"situation":[12,55,69,135],"it":[13],"is":[14,70,128],"involved":[15],"at":[17],"any":[18],"given":[19],"time.":[20],"This":[21],"requires":[22],"dealing":[23],"with":[24],"temporal":[25],"context,":[26],"handling":[27],"multiple":[28,134],"and":[29,32,49,90,100,119,132,139],"ambiguous":[30],"interpretations,":[31],"accounting":[33],"for":[34,155],"various":[35],"sources":[36],"of":[37,61,159],"uncertainty.":[38],"In":[39,83],"this":[40],"paper":[41],"we":[42,85,147],"propose":[43],"probabilistic":[45],"approach":[46,109],"to":[47,98,130],"modeling":[48],"recognizing":[50],"situations.":[51],"We":[52],"define":[53],"as":[56],"distribution":[58],"over":[59],"sequences":[60],"states":[62],"that":[63,78,125,149],"have":[64],"some":[65],"meaningful":[66],"interpretation.":[67],"Each":[68],"characterized":[71],"by":[72],"an":[73],"individual":[74],"hidden":[75],"Markov":[76],"model":[77,99],"describes":[79],"corresponding":[81],"distribution.":[82],"particular,":[84],"consider":[86],"typical":[87],"traffic":[88,115],"scenarios":[89,116],"describe":[91],"how":[92],"our":[93,126,150],"framework":[94],"can":[95,152],"be":[96,153],"used":[97,154],"track":[101,133],"different":[102],"situations":[103],"while":[104],"they":[105],"are":[106],"evolving.":[107],"The":[108,122],"was":[110],"evaluated":[111],"experimentally":[112],"vehicular":[114],"using":[117],"real":[118],"simulated":[120],"data.":[121],"results":[123],"show":[124,148],"able":[129],"recognize":[131],"instances":[136],"parallel":[138],"make":[140],"sensible":[141],"decisions":[142],"between":[143],"competing":[144],"hypotheses.":[145],"Additionally,":[146],"models":[151],"predicting":[156],"position":[158],"tracked":[161],"vehicles.":[162]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":5},{"year":2020,"cited_by_count":4},{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":5},{"year":2017,"cited_by_count":5},{"year":2016,"cited_by_count":4},{"year":2015,"cited_by_count":6},{"year":2014,"cited_by_count":5},{"year":2013,"cited_by_count":12},{"year":2012,"cited_by_count":7}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}