{"id":"https://openalex.org/W4416251813","doi":"https://doi.org/10.1109/ijcnn64981.2025.11227676","title":"TrajPred-MSM: A Multi-Scale Interactive High-Definition Map Encoding Approach for Trajectory Prediction","display_name":"TrajPred-MSM: A Multi-Scale Interactive High-Definition Map Encoding Approach for Trajectory Prediction","publication_year":2025,"publication_date":"2025-06-30","ids":{"openalex":"https://openalex.org/W4416251813","doi":"https://doi.org/10.1109/ijcnn64981.2025.11227676"},"language":null,"primary_location":{"id":"doi:10.1109/ijcnn64981.2025.11227676","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11227676","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","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/A5061986097","display_name":"Zuotao Ning","orcid":"https://orcid.org/0000-0003-3114-2784"},"institutions":[{"id":"https://openalex.org/I4210134419","display_name":"Neusoft (China)","ror":"https://ror.org/02zc84r97","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210134419"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Zuotao Ning","raw_affiliation_strings":["Neusoft Reach Automotive Technology (Shenyang) Co., Ltd.,ADS Competency Center,Shenyang,China"],"affiliations":[{"raw_affiliation_string":"Neusoft Reach Automotive Technology (Shenyang) Co., Ltd.,ADS Competency Center,Shenyang,China","institution_ids":["https://openalex.org/I4210134419"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5099777578","display_name":"Haolin Xing","orcid":null},"institutions":[{"id":"https://openalex.org/I9224756","display_name":"Northeastern University","ror":"https://ror.org/03awzbc87","country_code":"CN","type":"education","lineage":["https://openalex.org/I9224756"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haolin Xing","raw_affiliation_strings":["Northeastern University,College of Information Science and Engineering,Shenyang,China"],"affiliations":[{"raw_affiliation_string":"Northeastern University,College of Information Science and Engineering,Shenyang,China","institution_ids":["https://openalex.org/I9224756"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5072381801","display_name":"Jiwei Nie","orcid":"https://orcid.org/0000-0003-3639-4729"},"institutions":[{"id":"https://openalex.org/I9224756","display_name":"Northeastern University","ror":"https://ror.org/03awzbc87","country_code":"CN","type":"education","lineage":["https://openalex.org/I9224756"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiwei Nie","raw_affiliation_strings":["Northeastern University,College of Information Science and Engineering,Shenyang,China"],"affiliations":[{"raw_affiliation_string":"Northeastern University,College of Information Science and Engineering,Shenyang,China","institution_ids":["https://openalex.org/I9224756"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5110496274","display_name":"Zhe Peng","orcid":null},"institutions":[{"id":"https://openalex.org/I9224756","display_name":"Northeastern University","ror":"https://ror.org/03awzbc87","country_code":"CN","type":"education","lineage":["https://openalex.org/I9224756"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhe Peng","raw_affiliation_strings":["Northeastern University,College of Information Science and Engineering,Shenyang,China"],"affiliations":[{"raw_affiliation_string":"Northeastern University,College of Information Science and Engineering,Shenyang,China","institution_ids":["https://openalex.org/I9224756"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101465765","display_name":"Shuai Cheng","orcid":"https://orcid.org/0009-0006-5871-6572"},"institutions":[{"id":"https://openalex.org/I4210134419","display_name":"Neusoft (China)","ror":"https://ror.org/02zc84r97","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210134419"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shuai Cheng","raw_affiliation_strings":["Neusoft Reach Automotive Technology (Shenyang) Co., Ltd.,ADS Competency Center,Shenyang,China"],"affiliations":[{"raw_affiliation_string":"Neusoft Reach Automotive Technology (Shenyang) Co., Ltd.,ADS Competency Center,Shenyang,China","institution_ids":["https://openalex.org/I4210134419"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100432026","display_name":"Wei Liu","orcid":"https://orcid.org/0009-0008-0901-7518"},"institutions":[{"id":"https://openalex.org/I9224756","display_name":"Northeastern University","ror":"https://ror.org/03awzbc87","country_code":"CN","type":"education","lineage":["https://openalex.org/I9224756"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wei Liu","raw_affiliation_strings":["Northeastern University,College of Information Science and Engineering,Shenyang,China"],"affiliations":[{"raw_affiliation_string":"Northeastern University,College of Information Science and Engineering,Shenyang,China","institution_ids":["https://openalex.org/I9224756"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5061986097"],"corresponding_institution_ids":["https://openalex.org/I4210134419"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.40129046,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.5077999830245972,"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":0.5077999830245972,"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/T13282","display_name":"Automated Road and Building Extraction","score":0.4104999899864197,"subfield":{"id":"https://openalex.org/subfields/2212","display_name":"Ocean 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":0.015799999237060547,"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/trajectory","display_name":"Trajectory","score":0.7652000188827515},{"id":"https://openalex.org/keywords/encoding","display_name":"Encoding (memory)","score":0.7193999886512756},{"id":"https://openalex.org/keywords/encode","display_name":"ENCODE","score":0.6622999906539917},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.551800012588501},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5515999794006348},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5396000146865845},{"id":"https://openalex.org/keywords/face","display_name":"Face (sociological concept)","score":0.4973999857902527}],"concepts":[{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.7652000188827515},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.7193999886512756},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6902999877929688},{"id":"https://openalex.org/C66746571","wikidata":"https://www.wikidata.org/wiki/Q1134833","display_name":"ENCODE","level":3,"score":0.6622999906539917},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5752999782562256},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.551800012588501},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5515999794006348},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5396000146865845},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.4973999857902527},{"id":"https://openalex.org/C2164484","wikidata":"https://www.wikidata.org/wiki/Q5170150","display_name":"Core (optical fiber)","level":2,"score":0.42899999022483826},{"id":"https://openalex.org/C13355873","wikidata":"https://www.wikidata.org/wiki/Q2920850","display_name":"Connection (principal bundle)","level":2,"score":0.40290001034736633},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.39750000834465027},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3774000108242035},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3686999976634979},{"id":"https://openalex.org/C2779188883","wikidata":"https://www.wikidata.org/wiki/Q82446","display_name":"Global Map","level":3,"score":0.3174000084400177},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2915000021457672},{"id":"https://openalex.org/C148043351","wikidata":"https://www.wikidata.org/wiki/Q4456944","display_name":"Current (fluid)","level":2,"score":0.2874000072479248},{"id":"https://openalex.org/C159620131","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Spatial analysis","level":2,"score":0.2865000069141388},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.2858000099658966}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn64981.2025.11227676","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11227676","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","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":22,"referenced_works":["https://openalex.org/W1689711448","https://openalex.org/W2112796928","https://openalex.org/W2116341502","https://openalex.org/W2160419726","https://openalex.org/W2565639579","https://openalex.org/W2955189650","https://openalex.org/W2963945905","https://openalex.org/W2967177252","https://openalex.org/W3010072020","https://openalex.org/W3034722190","https://openalex.org/W3035671534","https://openalex.org/W3107552218","https://openalex.org/W3108486966","https://openalex.org/W3121034396","https://openalex.org/W3139491754","https://openalex.org/W3209837334","https://openalex.org/W4287009164","https://openalex.org/W4312339440","https://openalex.org/W4312731878","https://openalex.org/W4383109074","https://openalex.org/W4385221361","https://openalex.org/W4401207414"],"related_works":[],"abstract_inverted_index":{"Current":[0],"research":[1],"on":[2,119],"autonomous":[3],"driving":[4],"trajectory":[5,62,133],"prediction":[6,63,134],"algorithms":[7],"continues":[8],"to":[9,24,80],"face":[10],"challenges.":[11],"One":[12],"key":[13],"issue":[14],"is":[15,79],"that":[16,124],"the":[17,34,48,68,120],"encoding":[18],"of":[19,37,50,76],"road":[20],"topology":[21],"may":[22],"fail":[23],"simultaneously":[25],"capture":[26],"both":[27],"local":[28,89,107],"detail":[29],"and":[30,39,91],"global":[31,95,115],"representation.":[32,96],"Secondly,":[33],"separate":[35],"processing":[36],"spatial":[38],"temporal":[40,52],"information":[41],"for":[42,88,94],"traffic":[43],"participants":[44],"can":[45],"result":[46],"in":[47,106],"loss":[49],"critical":[51],"feature":[53],"information.":[54],"To":[55],"address":[56],"these":[57],"issues,":[58],"we":[59],"propose":[60],"a":[61,114],"algorithm":[64,78],"named":[65],"TrajPred-MSM":[66],"using":[67],"high-definition":[69,82],"map":[70,129],"by":[71,126],"multi-scale.":[72],"The":[73,97],"core":[74],"idea":[75],"this":[77],"encode":[81],"maps":[83],"at":[84],"two":[85],"scales:":[86],"nodes":[87],"representation":[90],"lane":[92],"segments":[93],"designed":[98],"dual-scale":[99],"approach":[100],"captures":[101],"fine-grained":[102],"microscopic":[103],"connection":[104],"relationships":[105],"areas":[108],"while":[109],"preserving":[110],"macroscopic":[111],"connections":[112],"from":[113],"perspective.":[116],"Experimental":[117],"results":[118],"Argoverse":[121],"dataset":[122],"demonstrate":[123],"TrajPred-MSM,":[125],"effectively":[127],"extracting":[128],"information,":[130],"outperforms":[131],"traditional":[132],"algorithms.":[135]},"counts_by_year":[],"updated_date":"2026-03-07T16:01:11.037858","created_date":"2025-11-14T00:00:00"}