{"id":"https://openalex.org/W2736781985","doi":"https://doi.org/10.1109/icra.2017.7989029","title":"Vehicle tracking using extended object methods: An approach for fusing radar and laser","display_name":"Vehicle tracking using extended object methods: An approach for fusing radar and laser","publication_year":2017,"publication_date":"2017-05-01","ids":{"openalex":"https://openalex.org/W2736781985","doi":"https://doi.org/10.1109/icra.2017.7989029","mag":"2736781985"},"language":"en","primary_location":{"id":"doi:10.1109/icra.2017.7989029","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icra.2017.7989029","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Conference on Robotics and Automation (ICRA)","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/A5082675746","display_name":"Alexander Scheel","orcid":"https://orcid.org/0000-0002-5014-2651"},"institutions":[{"id":"https://openalex.org/I196349391","display_name":"Universit\u00e4t Ulm","ror":"https://ror.org/032000t02","country_code":"DE","type":"education","lineage":["https://openalex.org/I196349391"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Alexander Scheel","raw_affiliation_strings":["Institute of Measurement, Control, and Microtechnology, Ulm University, Ulm, Germany"],"affiliations":[{"raw_affiliation_string":"Institute of Measurement, Control, and Microtechnology, Ulm University, Ulm, Germany","institution_ids":["https://openalex.org/I196349391"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5014956076","display_name":"Stephan Reuter","orcid":"https://orcid.org/0000-0002-4858-1081"},"institutions":[{"id":"https://openalex.org/I196349391","display_name":"Universit\u00e4t Ulm","ror":"https://ror.org/032000t02","country_code":"DE","type":"education","lineage":["https://openalex.org/I196349391"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Stephan Reuter","raw_affiliation_strings":["Institute of Measurement, Control, and Microtechnology, Ulm University, Ulm, Germany"],"affiliations":[{"raw_affiliation_string":"Institute of Measurement, Control, and Microtechnology, Ulm University, Ulm, Germany","institution_ids":["https://openalex.org/I196349391"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5085054529","display_name":"Klaus Dietmayer","orcid":"https://orcid.org/0000-0002-1651-014X"},"institutions":[{"id":"https://openalex.org/I196349391","display_name":"Universit\u00e4t Ulm","ror":"https://ror.org/032000t02","country_code":"DE","type":"education","lineage":["https://openalex.org/I196349391"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Klaus Dietmayer","raw_affiliation_strings":["Institute of Measurement, Control, and Microtechnology, Ulm University, Ulm, Germany"],"affiliations":[{"raw_affiliation_string":"Institute of Measurement, Control, and Microtechnology, Ulm University, Ulm, Germany","institution_ids":["https://openalex.org/I196349391"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5082675746"],"corresponding_institution_ids":["https://openalex.org/I196349391"],"apc_list":null,"apc_paid":null,"fwci":1.5602,"has_fulltext":false,"cited_by_count":16,"citation_normalized_percentile":{"value":0.87242927,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"231","last_page":"238"},"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":0.9998999834060669,"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":0.9998999834060669,"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/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.9939000010490417,"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/T12879","display_name":"Distributed Sensor Networks and Detection Algorithms","score":0.9926999807357788,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"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":0.7077072262763977},{"id":"https://openalex.org/keywords/sensor-fusion","display_name":"Sensor fusion","score":0.69547438621521},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.6310442686080933},{"id":"https://openalex.org/keywords/video-tracking","display_name":"Video tracking","score":0.6191872954368591},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5697436928749084},{"id":"https://openalex.org/keywords/redundancy","display_name":"Redundancy (engineering)","score":0.5202540159225464},{"id":"https://openalex.org/keywords/radar-tracker","display_name":"Radar tracker","score":0.476001113653183},{"id":"https://openalex.org/keywords/preprocessor","display_name":"Preprocessor","score":0.4518125653266907},{"id":"https://openalex.org/keywords/tracking-system","display_name":"Tracking system","score":0.4413350820541382},{"id":"https://openalex.org/keywords/radar","display_name":"Radar","score":0.4374270737171173},{"id":"https://openalex.org/keywords/raw-data","display_name":"Raw data","score":0.4372541308403015},{"id":"https://openalex.org/keywords/tracking","display_name":"Tracking (education)","score":0.41495996713638306},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.4001252055168152},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.39141276478767395}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7077072262763977},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.69547438621521},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6310442686080933},{"id":"https://openalex.org/C202474056","wikidata":"https://www.wikidata.org/wiki/Q1931635","display_name":"Video tracking","level":3,"score":0.6191872954368591},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5697436928749084},{"id":"https://openalex.org/C152124472","wikidata":"https://www.wikidata.org/wiki/Q1204361","display_name":"Redundancy (engineering)","level":2,"score":0.5202540159225464},{"id":"https://openalex.org/C32283439","wikidata":"https://www.wikidata.org/wiki/Q1407014","display_name":"Radar tracker","level":3,"score":0.476001113653183},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.4518125653266907},{"id":"https://openalex.org/C154586513","wikidata":"https://www.wikidata.org/wiki/Q4420972","display_name":"Tracking system","level":3,"score":0.4413350820541382},{"id":"https://openalex.org/C554190296","wikidata":"https://www.wikidata.org/wiki/Q47528","display_name":"Radar","level":2,"score":0.4374270737171173},{"id":"https://openalex.org/C132964779","wikidata":"https://www.wikidata.org/wiki/Q2110223","display_name":"Raw data","level":2,"score":0.4372541308403015},{"id":"https://openalex.org/C2775936607","wikidata":"https://www.wikidata.org/wiki/Q466845","display_name":"Tracking (education)","level":2,"score":0.41495996713638306},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.4001252055168152},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.39141276478767395},{"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/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.0},{"id":"https://openalex.org/C19417346","wikidata":"https://www.wikidata.org/wiki/Q7922","display_name":"Pedagogy","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/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icra.2017.7989029","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icra.2017.7989029","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Conference on Robotics and Automation (ICRA)","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":17,"referenced_works":["https://openalex.org/W1507290626","https://openalex.org/W1909771825","https://openalex.org/W1959279866","https://openalex.org/W2031031248","https://openalex.org/W2035753090","https://openalex.org/W2044164936","https://openalex.org/W2049244691","https://openalex.org/W2080655929","https://openalex.org/W2099144460","https://openalex.org/W2123378338","https://openalex.org/W2130213433","https://openalex.org/W2159021170","https://openalex.org/W2511727584","https://openalex.org/W2512933954","https://openalex.org/W2513434274","https://openalex.org/W2516938316","https://openalex.org/W6725333127"],"related_works":["https://openalex.org/W2318603563","https://openalex.org/W2990606766","https://openalex.org/W887692824","https://openalex.org/W3151342408","https://openalex.org/W2354419434","https://openalex.org/W1938087941","https://openalex.org/W1582120664","https://openalex.org/W191136024","https://openalex.org/W2021121433","https://openalex.org/W4285271403"],"abstract_inverted_index":{"Combining":[0],"data":[1,77,106],"from":[2,107],"heterogeneous":[3],"sensors":[4],"allows":[5],"to":[6],"enhance":[7],"tracking":[8,33,47],"systems":[9],"by":[10,22],"increasing":[11],"the":[12,20,56,74,79,86],"field":[13],"of":[14,55],"view,":[15],"incorporating":[16],"redundancy,":[17],"and":[18,42,95],"improving":[19],"performance":[21],"exploiting":[23],"complementary":[24],"sensor":[25,76],"characteristics.":[26],"This":[27],"paper":[28],"proposes":[29],"a":[30,51,108],"new":[31],"vehicle":[32,36],"approach":[34],"for":[35,88],"environment":[37],"perception":[38],"that":[39,71],"fuses":[40],"radar":[41],"laser":[43],"data.":[44],"A":[45],"Random-Finite-Set-based":[46],"filter,":[48],"which":[49],"permits":[50],"clear":[52],"mathematical":[53],"formulation":[54],"multi-object":[57],"problem,":[58],"is":[59],"used":[60],"as":[61],"fusion":[62],"center.":[63],"In":[64],"combination":[65],"with":[66],"extended":[67],"object":[68,93],"measurement":[69],"models":[70],"work":[72],"on":[73],"raw":[75],"directly,":[78],"filter":[80],"uses":[81],"all":[82],"available":[83],"information":[84],"without":[85],"need":[87],"further":[89],"preprocessing":[90],"routines,":[91],"considers":[92],"interdependencies,":[94],"works":[96],"in":[97],"ambiguous":[98],"situations.":[99],"The":[100],"results":[101],"are":[102],"evaluated":[103],"using":[104],"experimental":[105],"test":[109],"vehicle.":[110]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":3},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":3},{"year":2018,"cited_by_count":4}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
