{"id":"https://openalex.org/W3089418799","doi":"https://doi.org/10.1109/icra40945.2020.9196738","title":"Multimodal Trajectory Predictions for Urban Environments Using Geometric Relationships between a Vehicle and Lanes","display_name":"Multimodal Trajectory Predictions for Urban Environments Using Geometric Relationships between a Vehicle and Lanes","publication_year":2020,"publication_date":"2020-05-01","ids":{"openalex":"https://openalex.org/W3089418799","doi":"https://doi.org/10.1109/icra40945.2020.9196738","mag":"3089418799"},"language":"en","primary_location":{"id":"doi:10.1109/icra40945.2020.9196738","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icra40945.2020.9196738","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Conference on Robotics and Automation (ICRA)","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/A5067683481","display_name":"Atsushi Kawasaki","orcid":null},"institutions":[{"id":"https://openalex.org/I1292669757","display_name":"Toshiba (Japan)","ror":"https://ror.org/0326v3z14","country_code":"JP","type":"company","lineage":["https://openalex.org/I1292669757"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Atsushi Kawasaki","raw_affiliation_strings":["Corporate R&D Center, Toshiba Corporation, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Corporate R&D Center, Toshiba Corporation, Japan","institution_ids":["https://openalex.org/I1292669757"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5113687718","display_name":"Akihito Seki","orcid":null},"institutions":[{"id":"https://openalex.org/I1292669757","display_name":"Toshiba (Japan)","ror":"https://ror.org/0326v3z14","country_code":"JP","type":"company","lineage":["https://openalex.org/I1292669757"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Akihito Seki","raw_affiliation_strings":["Corporate R&D Center, Toshiba Corporation, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Corporate R&D Center, Toshiba Corporation, Japan","institution_ids":["https://openalex.org/I1292669757"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I1292669757"],"apc_list":null,"apc_paid":null,"fwci":2.6092,"has_fulltext":false,"cited_by_count":20,"citation_normalized_percentile":{"value":0.91717382,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":93,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"9203","last_page":"9209"},"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/T10370","display_name":"Traffic and Road Safety","score":0.9811999797821045,"subfield":{"id":"https://openalex.org/subfields/2213","display_name":"Safety, Risk, Reliability and Quality"},"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":0.9796000123023987,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.8042798042297363},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7472149729728699},{"id":"https://openalex.org/keywords/constraint","display_name":"Constraint (computer-aided design)","score":0.7312108278274536},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.6563904881477356},{"id":"https://openalex.org/keywords/state","display_name":"State (computer science)","score":0.46884122490882874},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4657488763332367},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.34664541482925415},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.32127201557159424},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.2870662212371826},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.14145216345787048}],"concepts":[{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.8042798042297363},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7472149729728699},{"id":"https://openalex.org/C2776036281","wikidata":"https://www.wikidata.org/wiki/Q48769818","display_name":"Constraint (computer-aided design)","level":2,"score":0.7312108278274536},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.6563904881477356},{"id":"https://openalex.org/C48103436","wikidata":"https://www.wikidata.org/wiki/Q599031","display_name":"State (computer science)","level":2,"score":0.46884122490882874},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4657488763332367},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.34664541482925415},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.32127201557159424},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2870662212371826},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.14145216345787048},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","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/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icra40945.2020.9196738","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icra40945.2020.9196738","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Conference on Robotics and Automation (ICRA)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.8399999737739563,"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":47,"referenced_works":["https://openalex.org/W1485009520","https://openalex.org/W1522301498","https://openalex.org/W1579853615","https://openalex.org/W1810943226","https://openalex.org/W1959608418","https://openalex.org/W1966554111","https://openalex.org/W2038705758","https://openalex.org/W2055556996","https://openalex.org/W2061106795","https://openalex.org/W2083651391","https://openalex.org/W2091281530","https://openalex.org/W2108501770","https://openalex.org/W2152239535","https://openalex.org/W2157331557","https://openalex.org/W2424778531","https://openalex.org/W2607296803","https://openalex.org/W2803184913","https://openalex.org/W2809078955","https://openalex.org/W2883602772","https://openalex.org/W2886483202","https://openalex.org/W2897638129","https://openalex.org/W2948479456","https://openalex.org/W2949416428","https://openalex.org/W2955189650","https://openalex.org/W2962719371","https://openalex.org/W2963203809","https://openalex.org/W2963343478","https://openalex.org/W2963759562","https://openalex.org/W2963906196","https://openalex.org/W2963945905","https://openalex.org/W2964121744","https://openalex.org/W2964193755","https://openalex.org/W2967177252","https://openalex.org/W2968337686","https://openalex.org/W2971215036","https://openalex.org/W3105115779","https://openalex.org/W3106257603","https://openalex.org/W6628877408","https://openalex.org/W6631190155","https://openalex.org/W6638273328","https://openalex.org/W6640963894","https://openalex.org/W6675944832","https://openalex.org/W6738303097","https://openalex.org/W6752231694","https://openalex.org/W6753653501","https://openalex.org/W6754899213","https://openalex.org/W6785849211"],"related_works":["https://openalex.org/W4323768008","https://openalex.org/W1941703695","https://openalex.org/W3131574667","https://openalex.org/W4360995134","https://openalex.org/W4248382324","https://openalex.org/W3023605104","https://openalex.org/W2039473718","https://openalex.org/W2387529410","https://openalex.org/W2383578611","https://openalex.org/W2987583674"],"abstract_inverted_index":{"Implementation":[0],"of":[1,11,15,58,73,160],"safe":[2],"and":[3,56,61,70,88,105,115],"efficient":[4],"autonomous":[5],"driving":[6],"systems":[7],"requires":[8],"accurate":[9],"prediction":[10,48,137,145,150],"the":[12,64,71,85,97,102,113,141,149],"long-term":[13],"trajectories":[14,28],"surrounding":[16],"vehicles.":[17],"High":[18],"uncertainty":[19],"in":[20,29],"traffic":[21,59,162],"behavior":[22],"makes":[23],"it":[24],"difficult":[25],"to":[26,83,95,118,133],"predict":[27,62],"urban":[30],"environments,":[31],"which":[32,51,155],"have":[33],"various":[34],"road":[35],"geometries.":[36],"To":[37],"over-come":[38],"this":[39,110],"problem,":[40],"we":[41,126],"propose":[42],"a":[43,89,128,157],"method":[44,138],"called":[45],"lane-based":[46],"multimodal":[47],"network":[49,108],"(LAMP-Net),":[50],"can":[52],"handle":[53],"arbitrary":[54],"shapes":[55],"numbers":[57],"lanes":[60],"both":[63],"future":[65],"trajectory":[66],"along":[67],"each":[68,74],"lane":[69,75,86,91],"probability":[72],"being":[76],"selected.":[77],"A":[78],"vector":[79],"map":[80],"is":[81,93,116],"used":[82],"define":[84],"geometry":[87],"novel":[90],"feature":[92,111],"introduced":[94],"represent":[96],"generalized":[98],"geometric":[99],"relationships":[100],"between":[101],"vehicle":[103,129],"state":[104],"lanes.":[106,124],"Our":[107,136],"takes":[109],"as":[112],"input":[114],"trained":[117],"be":[119],"versatile":[120],"for":[121],"arbitrarily":[122],"shaped":[123],"Moreover,":[125],"introduce":[127],"motion":[130],"model":[131],"constraint":[132,142],"our":[134,168],"network.":[135],"combined":[139],"with":[140],"significantly":[143],"enhances":[144],"accuracy.":[146],"We":[147],"evaluate":[148],"performance":[151],"on":[152],"two":[153],"datasets":[154],"contain":[156],"wide":[158],"variety":[159],"real-world":[161],"scenarios.":[163],"Experimental":[164],"results":[165],"show":[166],"that":[167],"proposed":[169],"LAMP-Net":[170],"outperforms":[171],"state-of-the-art":[172],"methods.":[173]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":5},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":2}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
