{"id":"https://openalex.org/W3117796851","doi":"https://doi.org/10.1109/itsc45102.2020.9294553","title":"Long-term Prediction of Vehicle Behavior using Short-term Uncertainty-aware Trajectories and High-definition Maps","display_name":"Long-term Prediction of Vehicle Behavior using Short-term Uncertainty-aware Trajectories and High-definition Maps","publication_year":2020,"publication_date":"2020-09-20","ids":{"openalex":"https://openalex.org/W3117796851","doi":"https://doi.org/10.1109/itsc45102.2020.9294553","mag":"3117796851"},"language":"en","primary_location":{"id":"doi:10.1109/itsc45102.2020.9294553","is_oa":false,"landing_page_url":"https://doi.org/10.1109/itsc45102.2020.9294553","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)","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/A5020070465","display_name":"Sai Yalamanchi","orcid":null},"institutions":[{"id":"https://openalex.org/I4210123843","display_name":"Advanced Technologies Group (United States)","ror":"https://ror.org/0359sgh16","country_code":"US","type":"company","lineage":["https://openalex.org/I4210123843"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Sai Yalamanchi","raw_affiliation_strings":["Uber Advanced Technologies Group"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Uber Advanced Technologies Group","institution_ids":["https://openalex.org/I4210123843"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102158730","display_name":"Tzu-Kuo Huang","orcid":null},"institutions":[{"id":"https://openalex.org/I4210123843","display_name":"Advanced Technologies Group (United States)","ror":"https://ror.org/0359sgh16","country_code":"US","type":"company","lineage":["https://openalex.org/I4210123843"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Tzu-Kuo Huang","raw_affiliation_strings":["Uber Advanced Technologies Group"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Uber Advanced Technologies Group","institution_ids":["https://openalex.org/I4210123843"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5109258952","display_name":"Galen Clark Haynes","orcid":null},"institutions":[{"id":"https://openalex.org/I4210123843","display_name":"Advanced Technologies Group (United States)","ror":"https://ror.org/0359sgh16","country_code":"US","type":"company","lineage":["https://openalex.org/I4210123843"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Galen Clark Haynes","raw_affiliation_strings":["Uber Advanced Technologies Group"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Uber Advanced Technologies Group","institution_ids":["https://openalex.org/I4210123843"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5082498217","display_name":"Nemanja Djuric","orcid":"https://orcid.org/0000-0002-6502-7891"},"institutions":[{"id":"https://openalex.org/I4210123843","display_name":"Advanced Technologies Group (United States)","ror":"https://ror.org/0359sgh16","country_code":"US","type":"company","lineage":["https://openalex.org/I4210123843"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Nemanja Djuric","raw_affiliation_strings":["Uber Advanced Technologies Group"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Uber Advanced Technologies Group","institution_ids":["https://openalex.org/I4210123843"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.6113,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.70784275,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.9998999834060669,"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":0.9998999834060669,"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/T12095","display_name":"Vehicle emissions and performance","score":0.9807000160217285,"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/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9779000282287598,"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/term","display_name":"Term (time)","score":0.7713931202888489},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7624843120574951},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5199305415153503},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5070021152496338},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.4997272491455078},{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.49312660098075867},{"id":"https://openalex.org/keywords/ranging","display_name":"Ranging","score":0.4429962635040283},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3386894762516022}],"concepts":[{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.7713931202888489},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7624843120574951},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5199305415153503},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5070021152496338},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.4997272491455078},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.49312660098075867},{"id":"https://openalex.org/C115051666","wikidata":"https://www.wikidata.org/wiki/Q6522493","display_name":"Ranging","level":2,"score":0.4429962635040283},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3386894762516022},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","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/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/itsc45102.2020.9294553","is_oa":false,"landing_page_url":"https://doi.org/10.1109/itsc45102.2020.9294553","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11","score":0.6600000262260437}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":36,"referenced_works":["https://openalex.org/W1986864914","https://openalex.org/W2012849708","https://openalex.org/W2032924574","https://openalex.org/W2096366269","https://openalex.org/W2104806250","https://openalex.org/W2105934661","https://openalex.org/W2120179083","https://openalex.org/W2124817418","https://openalex.org/W2139391802","https://openalex.org/W2342840547","https://openalex.org/W2343568200","https://openalex.org/W2402106645","https://openalex.org/W2582443945","https://openalex.org/W2734463498","https://openalex.org/W2784715585","https://openalex.org/W2798930779","https://openalex.org/W2803184913","https://openalex.org/W2883602772","https://openalex.org/W2898900571","https://openalex.org/W2905173465","https://openalex.org/W2962901581","https://openalex.org/W2967177252","https://openalex.org/W3010072020","https://openalex.org/W3029177463","https://openalex.org/W3047375952","https://openalex.org/W3091179814","https://openalex.org/W3104946437","https://openalex.org/W3105115779","https://openalex.org/W3106257603","https://openalex.org/W3114753236","https://openalex.org/W6704559304","https://openalex.org/W6755864109","https://openalex.org/W6756871163","https://openalex.org/W6758283773","https://openalex.org/W6769377150","https://openalex.org/W6785849211"],"related_works":["https://openalex.org/W2961085424","https://openalex.org/W4306674287","https://openalex.org/W4224009465","https://openalex.org/W4286629047","https://openalex.org/W4306321456","https://openalex.org/W3046775127","https://openalex.org/W4205958290","https://openalex.org/W3209574120","https://openalex.org/W3170094116","https://openalex.org/W3107602296"],"abstract_inverted_index":{"Motion":[0],"prediction":[1,69,143],"of":[2,7,39,159],"surrounding":[3],"vehicles":[4],"is":[5],"one":[6],"the":[8,23,32,77,81,84,91,105,118,160,166,173],"most":[9],"important":[10,51],"tasks":[11],"handled":[12],"by":[13],"a":[14,19,37,114,122,137,180],"self-driving":[15,181],"vehicle,":[16],"and":[17,44,112,148],"represents":[18],"critical":[20],"step":[21],"in":[22,136,141],"autonomous":[24],"system":[25],"necessary":[26],"to":[27,60,64,89,103],"ensure":[28],"safety":[29],"for":[30,74],"all":[31],"involved":[33],"traffic":[34],"actors.":[35],"Recently":[36],"number":[38],"researchers":[40],"from":[41,56],"both":[42,146],"academic":[43],"industrial":[45],"communities":[46],"have":[47,87],"focused":[48],"on":[49,152],"this":[50,98],"problem,":[52],"proposing":[53],"ideas":[54],"ranging":[55],"engineered,":[57],"rule-based":[58],"methods":[59,79,86],"learned":[61,85],"approaches,":[62,83],"shown":[63],"perform":[65],"well":[66],"at":[67,94,145],"different":[68],"horizons.":[70,96,150],"In":[71,97],"particular,":[72],"while":[73],"longer-term":[75,149],"trajectories":[76,132],"engineered":[78],"outperform":[80],"competing":[82],"proven":[88],"be":[90],"best":[92],"choice":[93],"short-term":[95],"work":[99],"we":[100],"describe":[101],"how":[102],"overcome":[104],"discrepancy":[106],"between":[107],"these":[108],"two":[109],"research":[110],"directions,":[111],"propose":[113],"method":[115,175],"that":[116],"combines":[117],"disparate":[119],"approaches":[120],"under":[121],"single":[123],"unifying":[124],"framework.":[125],"The":[126],"resulting":[127,140],"algorithm":[128],"fuses":[129],"learned,":[130],"uncertainty-aware":[131],"with":[133],"lane-based":[134],"paths":[135],"principled":[138],"manner,":[139],"improved":[142],"accuracy":[144],"shorter-":[147],"Experiments":[151],"real-world,":[153],"large-scale":[154],"data":[155],"strongly":[156],"suggest":[157],"benefits":[158],"proposed":[161,174],"unified":[162],"method,":[163],"which":[164],"outperformed":[165],"existing":[167],"state-of-the-art.":[168],"Moreover,":[169],"following":[170],"offline":[171],"evaluation":[172],"was":[176],"successfully":[177],"tested":[178],"onboard":[179],"vehicle.":[182]},"counts_by_year":[{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":2}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
