{"id":"https://openalex.org/W2081410130","doi":"https://doi.org/10.1109/ivs.2012.6232193","title":"Incorporating environmental knowledge into Bayesian filtering using attractor functions","display_name":"Incorporating environmental knowledge into Bayesian filtering using attractor functions","publication_year":2012,"publication_date":"2012-06-01","ids":{"openalex":"https://openalex.org/W2081410130","doi":"https://doi.org/10.1109/ivs.2012.6232193","mag":"2081410130"},"language":"en","primary_location":{"id":"doi:10.1109/ivs.2012.6232193","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ivs.2012.6232193","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2012 IEEE Intelligent Vehicles Symposium","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/A5001726946","display_name":"Andreas Alin","orcid":null},"institutions":[{"id":"https://openalex.org/I8087733","display_name":"University of T\u00fcbingen","ror":"https://ror.org/03a1kwz48","country_code":"DE","type":"education","lineage":["https://openalex.org/I8087733"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Andreas Alin","raw_affiliation_strings":["Department of Cognitive Modeling, University of Tubingen, Tubingen, Germany","Department of Cognitive Modeling, University of Tuebingen, Tuebingen, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Cognitive Modeling, University of Tubingen, Tubingen, Germany","institution_ids":["https://openalex.org/I8087733"]},{"raw_affiliation_string":"Department of Cognitive Modeling, University of Tuebingen, Tuebingen, Germany","institution_ids":["https://openalex.org/I8087733"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5075919503","display_name":"Martin V. Butz","orcid":"https://orcid.org/0000-0002-8120-8537"},"institutions":[{"id":"https://openalex.org/I8087733","display_name":"University of T\u00fcbingen","ror":"https://ror.org/03a1kwz48","country_code":"DE","type":"education","lineage":["https://openalex.org/I8087733"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Martin V. Butz","raw_affiliation_strings":["Department of Cognitive Modeling, University of Tubingen, Tubingen, Germany","Department of Cognitive Modeling, University of Tuebingen, Tuebingen, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Cognitive Modeling, University of Tubingen, Tubingen, Germany","institution_ids":["https://openalex.org/I8087733"]},{"raw_affiliation_string":"Department of Cognitive Modeling, University of Tuebingen, Tuebingen, Germany","institution_ids":["https://openalex.org/I8087733"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5109108780","display_name":"Jannik Fritsch","orcid":null},"institutions":[{"id":"https://openalex.org/I1283473643","display_name":"Honda (Japan)","ror":"https://ror.org/03jzay846","country_code":"JP","type":"company","lineage":["https://openalex.org/I1283473643"]},{"id":"https://openalex.org/I4210112253","display_name":"Honda (Germany)","ror":"https://ror.org/022c1xk47","country_code":"DE","type":"company","lineage":["https://openalex.org/I1283473643","https://openalex.org/I4210112253"]}],"countries":["DE","JP"],"is_corresponding":false,"raw_author_name":"Jannik Fritsch","raw_affiliation_strings":["Honda Research Institute, Offenbach am Main, Germany","Honda Res. Inst., Offenbach am Main, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Honda Research Institute, Offenbach am Main, Germany","institution_ids":["https://openalex.org/I4210112253"]},{"raw_affiliation_string":"Honda Res. Inst., Offenbach am Main, Germany","institution_ids":["https://openalex.org/I1283473643"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":10,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"476","last_page":"481"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10711","display_name":"Target Tracking and Data Fusion in Sensor Networks","score":0.9994000196456909,"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/T10711","display_name":"Target Tracking and Data Fusion in Sensor Networks","score":0.9994000196456909,"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":0.9927999973297119,"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/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.991599977016449,"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/attractor","display_name":"Attractor","score":0.7680243849754333},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7216188907623291},{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.6552711725234985},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.6364619135856628},{"id":"https://openalex.org/keywords/advanced-driver-assistance-systems","display_name":"Advanced driver assistance systems","score":0.6075833439826965},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5820106267929077},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.5124033093452454},{"id":"https://openalex.org/keywords/acceleration","display_name":"Acceleration","score":0.4574936330318451},{"id":"https://openalex.org/keywords/intelligent-transportation-system","display_name":"Intelligent transportation system","score":0.4380810260772705},{"id":"https://openalex.org/keywords/nonlinear-system","display_name":"Nonlinear system","score":0.4207741618156433},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3746546506881714},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.35414552688598633},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.13564467430114746},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.1266668736934662},{"id":"https://openalex.org/keywords/transport-engineering","display_name":"Transport engineering","score":0.07739165425300598}],"concepts":[{"id":"https://openalex.org/C164380108","wikidata":"https://www.wikidata.org/wiki/Q507187","display_name":"Attractor","level":2,"score":0.7680243849754333},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7216188907623291},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.6552711725234985},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.6364619135856628},{"id":"https://openalex.org/C87833898","wikidata":"https://www.wikidata.org/wiki/Q1060280","display_name":"Advanced driver assistance systems","level":2,"score":0.6075833439826965},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5820106267929077},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.5124033093452454},{"id":"https://openalex.org/C117896860","wikidata":"https://www.wikidata.org/wiki/Q11376","display_name":"Acceleration","level":2,"score":0.4574936330318451},{"id":"https://openalex.org/C47796450","wikidata":"https://www.wikidata.org/wiki/Q508378","display_name":"Intelligent transportation system","level":2,"score":0.4380810260772705},{"id":"https://openalex.org/C158622935","wikidata":"https://www.wikidata.org/wiki/Q660848","display_name":"Nonlinear system","level":2,"score":0.4207741618156433},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3746546506881714},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.35414552688598633},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.13564467430114746},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.1266668736934662},{"id":"https://openalex.org/C22212356","wikidata":"https://www.wikidata.org/wiki/Q775325","display_name":"Transport engineering","level":1,"score":0.07739165425300598},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C1276947","wikidata":"https://www.wikidata.org/wiki/Q333","display_name":"Astronomy","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/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","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/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ivs.2012.6232193","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ivs.2012.6232193","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2012 IEEE Intelligent Vehicles Symposium","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":10,"referenced_works":["https://openalex.org/W1536542822","https://openalex.org/W2006251970","https://openalex.org/W2058570191","https://openalex.org/W2076369695","https://openalex.org/W2099698956","https://openalex.org/W2118933465","https://openalex.org/W2143071398","https://openalex.org/W2153523409","https://openalex.org/W2336416123","https://openalex.org/W6632378633"],"related_works":["https://openalex.org/W3049714198","https://openalex.org/W2960434112","https://openalex.org/W2036805113","https://openalex.org/W2592279130","https://openalex.org/W2049158118","https://openalex.org/W2123827981","https://openalex.org/W4205994410","https://openalex.org/W1499880030","https://openalex.org/W34461864","https://openalex.org/W2181936729"],"abstract_inverted_index":{"Many":[0],"automotive":[1],"systems":[2],"use":[3],"linear":[4,20],"approaches":[5],"to":[6,50,65,145],"track":[7],"and":[8,54,134],"predict":[9],"other":[10,57,158],"traffic":[11,58,93,104,136,159],"participants.":[12,59],"While":[13],"this":[14,114],"may":[15,161],"be":[16,162],"appropriate":[17],"on":[18,26,177],"highways,":[19],"predictions":[21],"do":[22],"not":[23],"work":[24],"properly":[25],"curved":[27],"roads":[28],"or":[29,106],"lane":[30,46,102],"crossings.":[31],"This":[32],"contribution":[33],"introduces":[34],"a":[35,75],"generic":[36],"way":[37],"for":[38],"including":[39],"environmental":[40,98],"knowledge":[41,62,99],"-":[42,49],"such":[43,100],"as":[44,101],"the":[45,83,111,118,138,155],"trajectory":[47],"ahead":[48],"anticipate":[51],"yaw":[52],"rate":[53],"acceleration":[55],"of":[56,86,113,157],"The":[60,89],"anticipatory":[61],"is":[63,72,141],"used":[64],"improve":[66],"prediction":[67],"in":[68,74,124],"filtering":[69],"tasks.":[70],"It":[71],"embedded":[73],"Bayesian":[76],"framework":[77],"by":[78,116,164],"introducing":[79],"attractors,":[80],"which":[81,173],"modify":[82],"probabilistic":[84],"propagation":[85],"state":[87],"estimations.":[88],"attractors":[90],"model":[91],"how":[92],"participants":[94,160],"typically":[95],"behave,":[96],"given":[97,130],"information,":[103],"lights,":[105],"indicator":[107],"lights.":[108],"We":[109,127],"demonstrate":[110],"potential":[112],"approach":[115],"modeling":[117],"fact":[119],"that":[120,129,154],"vehicles":[121],"usually":[122],"stay":[123],"their":[125],"lane.":[126],"show":[128,153],"correct":[131],"context":[132],"information":[133],"nonlinear":[135],"situations,":[137],"tracking":[139,147],"error":[140],"considerably":[142],"lower":[143],"compared":[144],"conventional":[146],"methods.":[148],"In":[149],"addition,":[150],"we":[151],"also":[152],"intentions":[156],"inferred":[163],"comparing":[165],"actual":[166],"sensory":[167],"data":[168],"with":[169],"anticipated":[170],"probability":[171],"distributions,":[172],"were":[174],"generated":[175],"dependent":[176],"alternative":[178],"attractors.":[179]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":1},{"year":2015,"cited_by_count":3},{"year":2013,"cited_by_count":3}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
