{"id":"https://openalex.org/W4400645859","doi":"https://doi.org/10.1109/iv55156.2024.10588846","title":"SSL-Interactions: Pretext Tasks for Interactive Trajectory Prediction","display_name":"SSL-Interactions: Pretext Tasks for Interactive Trajectory Prediction","publication_year":2024,"publication_date":"2024-06-02","ids":{"openalex":"https://openalex.org/W4400645859","doi":"https://doi.org/10.1109/iv55156.2024.10588846"},"language":"en","primary_location":{"id":"doi:10.1109/iv55156.2024.10588846","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/iv55156.2024.10588846","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":"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/A5057923827","display_name":"Prarthana Bhattacharyya","orcid":null},"institutions":[{"id":"https://openalex.org/I151746483","display_name":"University of Waterloo","ror":"https://ror.org/01aff2v68","country_code":"CA","type":"education","lineage":["https://openalex.org/I151746483"]}],"countries":["CA"],"is_corresponding":true,"raw_author_name":"Prarthana Bhattacharyya","raw_affiliation_strings":["University of Waterloo,Waterloo,ON,Canada"],"affiliations":[{"raw_affiliation_string":"University of Waterloo,Waterloo,ON,Canada","institution_ids":["https://openalex.org/I151746483"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5057694255","display_name":"Chengjie Huang","orcid":"https://orcid.org/0009-0003-1025-7774"},"institutions":[{"id":"https://openalex.org/I151746483","display_name":"University of Waterloo","ror":"https://ror.org/01aff2v68","country_code":"CA","type":"education","lineage":["https://openalex.org/I151746483"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Chengjie Huang","raw_affiliation_strings":["University of Waterloo,Waterloo,ON,Canada"],"affiliations":[{"raw_affiliation_string":"University of Waterloo,Waterloo,ON,Canada","institution_ids":["https://openalex.org/I151746483"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5066916130","display_name":"Krzysztof Czarnecki","orcid":"https://orcid.org/0000-0003-1642-1101"},"institutions":[{"id":"https://openalex.org/I151746483","display_name":"University of Waterloo","ror":"https://ror.org/01aff2v68","country_code":"CA","type":"education","lineage":["https://openalex.org/I151746483"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Krzysztof Czarnecki","raw_affiliation_strings":["University of Waterloo,Waterloo,ON,Canada"],"affiliations":[{"raw_affiliation_string":"University of Waterloo,Waterloo,ON,Canada","institution_ids":["https://openalex.org/I151746483"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5057923827"],"corresponding_institution_ids":["https://openalex.org/I151746483"],"apc_list":null,"apc_paid":null,"fwci":0.3637,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.64549566,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":"162","issue":null,"first_page":"1450","last_page":"1457"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10181","display_name":"Natural Language Processing Techniques","score":0.9909999966621399,"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"}},"topics":[{"id":"https://openalex.org/T10181","display_name":"Natural Language Processing Techniques","score":0.9909999966621399,"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"}},{"id":"https://openalex.org/T11106","display_name":"Data Management and Algorithms","score":0.9732999801635742,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T10215","display_name":"Semantic Web and Ontologies","score":0.9591000080108643,"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/pretext","display_name":"Pretext","score":0.888442873954773},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7420573234558105},{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.5891575813293457},{"id":"https://openalex.org/keywords/human\u2013computer-interaction","display_name":"Human\u2013computer interaction","score":0.4335120916366577},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3488065004348755}],"concepts":[{"id":"https://openalex.org/C2779627259","wikidata":"https://www.wikidata.org/wiki/Q779763","display_name":"Pretext","level":3,"score":0.888442873954773},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7420573234558105},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.5891575813293457},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.4335120916366577},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3488065004348755},{"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/C1276947","wikidata":"https://www.wikidata.org/wiki/Q333","display_name":"Astronomy","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/iv55156.2024.10588846","is_oa":false,"landing_page_url":"http://dx.doi.org/10.1109/iv55156.2024.10588846","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":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W398859631","https://openalex.org/W2167052694","https://openalex.org/W2424778531","https://openalex.org/W2955189650","https://openalex.org/W2963001155","https://openalex.org/W3108486966","https://openalex.org/W3116651890","https://openalex.org/W3205220487","https://openalex.org/W4312402163","https://openalex.org/W4313186696","https://openalex.org/W4313189526","https://openalex.org/W4394649195","https://openalex.org/W6805144376","https://openalex.org/W6842057666","https://openalex.org/W6864900745"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2748952813","https://openalex.org/W161456234","https://openalex.org/W3123043866","https://openalex.org/W2765162471","https://openalex.org/W2367130511","https://openalex.org/W4235007455","https://openalex.org/W2354300066","https://openalex.org/W2996988663","https://openalex.org/W2276802262"],"abstract_inverted_index":{"This":[0,83],"paper":[1],"addresses":[2],"motion":[3,129],"forecasting":[4,130],"in":[5,119],"multi-agent":[6],"environments,":[7],"pivotal":[8],"for":[9,41,104,140],"ensuring":[10],"safety":[11],"of":[12,54,64,69,102],"autonomous":[13],"vehicles.":[14],"Traditional":[15],"and":[16,67,99,138],"recent":[17],"data-driven":[18],"marginal":[19],"trajectory":[20,42],"prediction":[21],"methods":[22,131],"struggle":[23],"to":[24,37,50,77,95,116,135],"properly":[25],"learn":[26],"non-linear":[27],"agent-to-agent":[28],"interactions.":[29],"We":[30,44,72,108],"present":[31],"SSL-Interactions":[32,126],"that":[33],"proposes":[34],"pretext":[35,48,106],"tasks":[36,49],"enhance":[38],"interaction":[39,70,97],"modeling":[40],"prediction.":[43,71],"introduce":[45],"four":[46],"interaction-aware":[47],"encapsulate":[51],"various":[52],"aspects":[53],"agent":[55],"interactions:":[56],"range":[57],"gap":[58],"prediction,":[59,62,66],"closest":[60],"distance":[61],"direction":[63],"movement":[65],"type":[68],"further":[73],"propose":[74,110],"an":[75],"approach":[76],"curate":[78],"interaction-heavy":[79,141],"scenarios":[80],"from":[81],"datasets.":[82],"curated":[84],"data":[85],"has":[86],"two":[87],"advantages:":[88],"it":[89],"provides":[90],"a":[91],"stronger":[92],"learning":[93],"signal":[94],"the":[96],"model,":[98],"facilitates":[100],"generation":[101],"pseudo-labels":[103],"interaction-centric":[105],"tasks.":[107],"also":[109],"three":[111],"new":[112],"metrics":[113],"specifically":[114],"designed":[115],"evaluate":[117],"predictions":[118],"interactive":[120],"scenes.":[121],"Our":[122],"empirical":[123],"evaluations":[124],"indicate":[125],"outperforms":[127],"state-of-the-art":[128],"quantitatively":[132],"with":[133],"up":[134],"8%":[136],"improvement,":[137],"qualitatively,":[139],"scenarios.":[142]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2025-12-26T23:08:49.675405","created_date":"2025-10-10T00:00:00"}