{"id":"https://openalex.org/W3210289792","doi":"https://doi.org/10.1109/iv48863.2021.9575253","title":"Intention-Driven Trajectory Prediction for Autonomous Driving","display_name":"Intention-Driven Trajectory Prediction for Autonomous Driving","publication_year":2021,"publication_date":"2021-07-11","ids":{"openalex":"https://openalex.org/W3210289792","doi":"https://doi.org/10.1109/iv48863.2021.9575253","mag":"3210289792"},"language":"en","primary_location":{"id":"doi:10.1109/iv48863.2021.9575253","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iv48863.2021.9575253","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE Intelligent Vehicles Symposium (IV)","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/A5059726116","display_name":"Shiwei Fan","orcid":null},"institutions":[{"id":"https://openalex.org/I4210159102","display_name":"Huawei Technologies (Sweden)","ror":"https://ror.org/0500fyd17","country_code":"SE","type":"company","lineage":["https://openalex.org/I2250955327","https://openalex.org/I4210159102"]}],"countries":["SE"],"is_corresponding":true,"raw_author_name":"Shiwei Fan","raw_affiliation_strings":["Noah's Ark Lab,Huawei"],"affiliations":[{"raw_affiliation_string":"Noah's Ark Lab,Huawei","institution_ids":["https://openalex.org/I4210159102"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5018337478","display_name":"Xiangxu Li","orcid":"https://orcid.org/0009-0006-8971-6815"},"institutions":[{"id":"https://openalex.org/I4210159102","display_name":"Huawei Technologies (Sweden)","ror":"https://ror.org/0500fyd17","country_code":"SE","type":"company","lineage":["https://openalex.org/I2250955327","https://openalex.org/I4210159102"]}],"countries":["SE"],"is_corresponding":false,"raw_author_name":"Xiangxu Li","raw_affiliation_strings":["Noah's Ark Lab,Huawei"],"affiliations":[{"raw_affiliation_string":"Noah's Ark Lab,Huawei","institution_ids":["https://openalex.org/I4210159102"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100325818","display_name":"Fei Li","orcid":"https://orcid.org/0000-0002-3861-461X"},"institutions":[{"id":"https://openalex.org/I4210159102","display_name":"Huawei Technologies (Sweden)","ror":"https://ror.org/0500fyd17","country_code":"SE","type":"company","lineage":["https://openalex.org/I2250955327","https://openalex.org/I4210159102"]}],"countries":["SE"],"is_corresponding":false,"raw_author_name":"Fei Li","raw_affiliation_strings":["Noah's Ark Lab,Huawei"],"affiliations":[{"raw_affiliation_string":"Noah's Ark Lab,Huawei","institution_ids":["https://openalex.org/I4210159102"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5059726116"],"corresponding_institution_ids":["https://openalex.org/I4210159102"],"apc_list":null,"apc_paid":null,"fwci":0.2749,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.5570375,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"107","last_page":"113"},"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9864000082015991,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.9779999852180481,"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/trajectory","display_name":"Trajectory","score":0.8442702293395996},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7708443403244019},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.7060932517051697},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.6343400478363037},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5918245911598206},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5900524854660034},{"id":"https://openalex.org/keywords/modal","display_name":"Modal","score":0.543496310710907},{"id":"https://openalex.org/keywords/term","display_name":"Term (time)","score":0.5362704396247864},{"id":"https://openalex.org/keywords/variety","display_name":"Variety (cybernetics)","score":0.5340515375137329},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5256109237670898},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.474631667137146},{"id":"https://openalex.org/keywords/context-model","display_name":"Context model","score":0.4372543692588806},{"id":"https://openalex.org/keywords/state","display_name":"State (computer science)","score":0.4151134788990021},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.39967548847198486},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.11344236135482788},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.0840868353843689}],"concepts":[{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.8442702293395996},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7708443403244019},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.7060932517051697},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.6343400478363037},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5918245911598206},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5900524854660034},{"id":"https://openalex.org/C71139939","wikidata":"https://www.wikidata.org/wiki/Q910194","display_name":"Modal","level":2,"score":0.543496310710907},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.5362704396247864},{"id":"https://openalex.org/C136197465","wikidata":"https://www.wikidata.org/wiki/Q1729295","display_name":"Variety (cybernetics)","level":2,"score":0.5340515375137329},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5256109237670898},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.474631667137146},{"id":"https://openalex.org/C183322885","wikidata":"https://www.wikidata.org/wiki/Q17007702","display_name":"Context model","level":3,"score":0.4372543692588806},{"id":"https://openalex.org/C48103436","wikidata":"https://www.wikidata.org/wiki/Q599031","display_name":"State (computer science)","level":2,"score":0.4151134788990021},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.39967548847198486},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.11344236135482788},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.0840868353843689},{"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/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"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/C1276947","wikidata":"https://www.wikidata.org/wiki/Q333","display_name":"Astronomy","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/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","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/C188027245","wikidata":"https://www.wikidata.org/wiki/Q750446","display_name":"Polymer chemistry","level":1,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iv48863.2021.9575253","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iv48863.2021.9575253","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE Intelligent Vehicles Symposium (IV)","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":20,"referenced_works":["https://openalex.org/W1959608418","https://openalex.org/W2424778531","https://openalex.org/W2607296803","https://openalex.org/W2886617718","https://openalex.org/W2905173465","https://openalex.org/W2950891689","https://openalex.org/W2963001155","https://openalex.org/W2963403868","https://openalex.org/W2963906196","https://openalex.org/W2967835402","https://openalex.org/W2968984153","https://openalex.org/W2970116586","https://openalex.org/W2980160556","https://openalex.org/W2991000214","https://openalex.org/W3003906095","https://openalex.org/W3015656235","https://openalex.org/W3034722190","https://openalex.org/W3035054225","https://openalex.org/W3039953768","https://openalex.org/W3062588417"],"related_works":["https://openalex.org/W2378211422","https://openalex.org/W4321353415","https://openalex.org/W2745001401","https://openalex.org/W2130974462","https://openalex.org/W2028665553","https://openalex.org/W2086519370","https://openalex.org/W972276598","https://openalex.org/W2087343574","https://openalex.org/W4246352526","https://openalex.org/W2121910908"],"abstract_inverted_index":{"Trajectory":[0],"prediction":[1,140],"has":[2],"received":[3],"much":[4],"attention":[5],"recently,":[6],"especially":[7],"in":[8],"autonomous":[9],"driving.":[10],"Many":[11],"Proposed":[12],"models":[13],"generate":[14],"multi-modal":[15],"trajectories":[16,44,64,111],"using":[17],"a":[18,53],"wide":[19],"variety":[20],"of":[21,37,79,87,117,127,134],"frameworks":[22],"for":[23],"context":[24,95,115],"representation":[25],"and":[26,41,60,81,97,120],"dynamic":[27],"interaction":[28,126],"modeling.":[29],"But":[30],"they":[31],"can":[32],"not":[33,46],"estimate":[34,56],"the":[35,38,42,57,62,67,76,85,99,114,125,132,138,144],"intention":[36,59,74,83,119],"target":[39],"vehicle,":[40],"predicted":[43],"are":[45],"explicable.":[47],"Towards":[48],"this":[49],"end,":[50],"we":[51],"propose":[52],"model":[54,136],"to":[55,66,72,93],"vehicle":[58,80],"predict":[61],"possible":[63],"corresponding":[65],"intention.":[68],"We":[69,89,130],"separate":[70],"intentions":[71,101],"long-term":[73,100],"indicating":[75,84],"future":[77],"path":[78],"short-term":[82],"behavior":[86],"vehicle.":[88],"use":[90],"rasterized":[91],"map":[92],"represent":[94],"information":[96],"differentiate":[98],"into":[102],"different":[103],"channels.":[104],"A":[105],"multi":[106],"encoder-decoder":[107],"module":[108],"generates":[109],"forecasting":[110],"based":[112],"on":[113,137],"feature":[116],"specific":[118],"learns":[121],"various":[122],"behaviors":[123],"considering":[124],"surrounding":[128],"obstacles.":[129],"demonstrate":[131],"performance":[133],"our":[135],"nuScenes":[139],"dataset,":[141],"which":[142],"outperforms":[143],"state-of-the-art":[145],"methods.":[146]},"counts_by_year":[{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}