{"id":"https://openalex.org/W4400644820","doi":"https://doi.org/10.1109/iv55156.2024.10588821","title":"A Review on Trajectory Datasets on Advanced Driver Assistance System Equipped-vehicles","display_name":"A Review on Trajectory Datasets on Advanced Driver Assistance System Equipped-vehicles","publication_year":2024,"publication_date":"2024-06-02","ids":{"openalex":"https://openalex.org/W4400644820","doi":"https://doi.org/10.1109/iv55156.2024.10588821"},"language":"en","primary_location":{"id":"doi:10.1109/iv55156.2024.10588821","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iv55156.2024.10588821","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE Intelligent Vehicles Symposium (IV)","raw_type":"proceedings-article"},"type":"review","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/A5069597439","display_name":"Hang Zhou","orcid":"https://orcid.org/0000-0002-0472-9515"},"institutions":[{"id":"https://openalex.org/I135310074","display_name":"University of Wisconsin\u2013Madison","ror":"https://ror.org/01y2jtd41","country_code":"US","type":"education","lineage":["https://openalex.org/I135310074"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Hang Zhou","raw_affiliation_strings":["Univ. of Wisconsin-Madison,Department of Civil and Environmental Engineering,Madison,WI,USA"],"affiliations":[{"raw_affiliation_string":"Univ. of Wisconsin-Madison,Department of Civil and Environmental Engineering,Madison,WI,USA","institution_ids":["https://openalex.org/I135310074"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5061414045","display_name":"Ke Ma","orcid":"https://orcid.org/0000-0003-2779-7964"},"institutions":[{"id":"https://openalex.org/I135310074","display_name":"University of Wisconsin\u2013Madison","ror":"https://ror.org/01y2jtd41","country_code":"US","type":"education","lineage":["https://openalex.org/I135310074"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ke Ma","raw_affiliation_strings":["Univ. of Wisconsin-Madison,Department of Civil and Environmental Engineering,Madison,WI,USA"],"affiliations":[{"raw_affiliation_string":"Univ. of Wisconsin-Madison,Department of Civil and Environmental Engineering,Madison,WI,USA","institution_ids":["https://openalex.org/I135310074"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100355083","display_name":"Xiaopeng Li","orcid":"https://orcid.org/0000-0002-5264-3775"},"institutions":[{"id":"https://openalex.org/I135310074","display_name":"University of Wisconsin\u2013Madison","ror":"https://ror.org/01y2jtd41","country_code":"US","type":"education","lineage":["https://openalex.org/I135310074"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xiaopeng Li","raw_affiliation_strings":["Univ. of Wisconsin-Madison,Department of Civil and Environmental Engineering,Madison,WI,USA"],"affiliations":[{"raw_affiliation_string":"Univ. of Wisconsin-Madison,Department of Civil and Environmental Engineering,Madison,WI,USA","institution_ids":["https://openalex.org/I135310074"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5069597439"],"corresponding_institution_ids":["https://openalex.org/I135310074"],"apc_list":null,"apc_paid":null,"fwci":1.0293,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.74308113,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1947","last_page":"1952"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9648000001907349,"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"}},"topics":[{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9648000001907349,"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"}},{"id":"https://openalex.org/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.944100022315979,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.8041940927505493},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6608543395996094},{"id":"https://openalex.org/keywords/advanced-driver-assistance-systems","display_name":"Advanced driver assistance systems","score":0.5031155943870544},{"id":"https://openalex.org/keywords/aeronautics","display_name":"Aeronautics","score":0.3379100561141968},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.32651257514953613},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.2339141070842743},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.17217865586280823}],"concepts":[{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.8041940927505493},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6608543395996094},{"id":"https://openalex.org/C87833898","wikidata":"https://www.wikidata.org/wiki/Q1060280","display_name":"Advanced driver assistance systems","level":2,"score":0.5031155943870544},{"id":"https://openalex.org/C178802073","wikidata":"https://www.wikidata.org/wiki/Q8421","display_name":"Aeronautics","level":1,"score":0.3379100561141968},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.32651257514953613},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2339141070842743},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.17217865586280823},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C1276947","wikidata":"https://www.wikidata.org/wiki/Q333","display_name":"Astronomy","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iv55156.2024.10588821","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iv55156.2024.10588821","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE Intelligent Vehicles Symposium (IV)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":12,"referenced_works":["https://openalex.org/W1965455100","https://openalex.org/W2955189650","https://openalex.org/W2983435156","https://openalex.org/W3017204849","https://openalex.org/W3035172746","https://openalex.org/W3035564946","https://openalex.org/W3157274478","https://openalex.org/W4200170951","https://openalex.org/W4205515105","https://openalex.org/W4312650415","https://openalex.org/W4313484219","https://openalex.org/W4396214375"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W1941703695","https://openalex.org/W4248382324","https://openalex.org/W4396696052"],"abstract_inverted_index":{"This":[0,29],"paper":[1,111],"presents":[2],"a":[3,100,118,143,155],"comprehensive":[4],"review":[5],"of":[6,20,25,34,40,93,102,114,132,139,158,174],"trajectory":[7,35],"datasets":[8,113,141],"from":[9],"vehicles":[10],"equipped":[11],"with":[12,17],"Advanced":[13],"Driver":[14],"Assistance":[15],"Systems,":[16],"the":[18,23,32,38,53,56,65,69,74,137,172],"aim":[19],"precisely":[21],"modeling":[22,126],"behavior":[24],"Autonomous":[26,59],"Vehicles":[27],"(AVs).":[28],"study":[30,134],"emphasizes":[31],"importance":[33],"data":[36,94,103,178],"in":[37,44,91],"development":[39],"AV":[41,84,127],"models,":[42],"especially":[43],"car-following":[45,128,168],"scenarios.":[46],"We":[47],"introduce":[48],"and":[49,73,97,107,165],"evaluate":[50],"several":[51],"datasets:":[52],"OpenACC":[54],"Dataset,":[55,64,68,72],"Connected":[57],"&":[58],"Transportation":[60],"Systems":[61],"Laboratory":[62],"Open":[63,76],"Vanderbilt":[66],"ACC":[67],"Central":[70],"Ohio":[71],"Waymo":[75],"Dataset.":[77],"Each":[78],"dataset":[79],"offers":[80],"unique":[81],"insights":[82],"into":[83,117,142],"behaviors,":[85],"yet":[86],"they":[87],"share":[88],"common":[89],"challenges":[90],"terms":[92],"availability,":[95],"processing,":[96],"standardization.":[98],"After":[99],"series":[101],"cleaning,":[104],"outlier":[105],"removal,":[106],"statistical":[108],"analysis,":[109],"this":[110,133],"transforms":[112],"varied":[115],"formats":[116],"uniform":[119],"standard,":[120],"thereby":[121],"improving":[122],"their":[123,148,162],"applicability":[124],"for":[125,150,167,176],"behavior.":[129],"Key":[130],"contributions":[131],"include:":[135],"1.":[136],"transformation":[138],"all":[140],"unified":[144],"standard":[145],"format,":[146],"enhancing":[147],"utility":[149],"broad":[151],"research":[152],"applications;":[153],"2.":[154],"comparative":[156],"analysis":[157],"these":[159],"datasets,":[160],"highlighting":[161],"distinct":[163],"characteristics":[164],"implications":[166],"model":[169],"development;":[170],"3.":[171],"provision":[173],"guidelines":[175],"future":[177],"collection":[179],"projects.":[180]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2}],"updated_date":"2026-04-16T08:26:57.006410","created_date":"2024-07-16T00:00:00"}
