{"id":"https://openalex.org/W4416250232","doi":"https://doi.org/10.1109/ijcnn64981.2025.11229361","title":"ECAM: A Contrastive Learning Approach to Avoid Environmental Collision in Trajectory Forecasting","display_name":"ECAM: A Contrastive Learning Approach to Avoid Environmental Collision in Trajectory Forecasting","publication_year":2025,"publication_date":"2025-06-30","ids":{"openalex":"https://openalex.org/W4416250232","doi":"https://doi.org/10.1109/ijcnn64981.2025.11229361"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn64981.2025.11229361","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11229361","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":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2506.09626","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5107902222","display_name":"G. Rosin","orcid":null},"institutions":[{"id":"https://openalex.org/I149461666","display_name":"Ca' Foscari University of Venice","ror":"https://ror.org/04yzxz566","country_code":"IT","type":"education","lineage":["https://openalex.org/I149461666"]}],"countries":["IT"],"is_corresponding":true,"raw_author_name":"Giacomo Rosin","raw_affiliation_strings":["Ca&#x2019; Foscari University of Venice,Department of Environmental Sciences, Informatics and Statistics,Italy"],"affiliations":[{"raw_affiliation_string":"Ca&#x2019; Foscari University of Venice,Department of Environmental Sciences, Informatics and Statistics,Italy","institution_ids":["https://openalex.org/I149461666"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068144201","display_name":"Muhammad Rameez Ur Rahman","orcid":"https://orcid.org/0000-0003-4425-5948"},"institutions":[{"id":"https://openalex.org/I149461666","display_name":"Ca' Foscari University of Venice","ror":"https://ror.org/04yzxz566","country_code":"IT","type":"education","lineage":["https://openalex.org/I149461666"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Muhammad Rameez Ur Rahman","raw_affiliation_strings":["Ca&#x2019; Foscari University of Venice,Department of Environmental Sciences, Informatics and Statistics,Italy"],"affiliations":[{"raw_affiliation_string":"Ca&#x2019; Foscari University of Venice,Department of Environmental Sciences, Informatics and Statistics,Italy","institution_ids":["https://openalex.org/I149461666"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5055143299","display_name":"Sebastiano Vascon","orcid":"https://orcid.org/0000-0002-7855-1641"},"institutions":[{"id":"https://openalex.org/I149461666","display_name":"Ca' Foscari University of Venice","ror":"https://ror.org/04yzxz566","country_code":"IT","type":"education","lineage":["https://openalex.org/I149461666"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Sebastiano Vascon","raw_affiliation_strings":["Ca&#x2019; Foscari University of Venice,Department of Environmental Sciences, Informatics and Statistics,Italy"],"affiliations":[{"raw_affiliation_string":"Ca&#x2019; Foscari University of Venice,Department of Environmental Sciences, Informatics and Statistics,Italy","institution_ids":["https://openalex.org/I149461666"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5107902222"],"corresponding_institution_ids":["https://openalex.org/I149461666"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.36970613,"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":0.9426000118255615,"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":0.9426000118255615,"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/T10525","display_name":"Human-Automation Interaction and Safety","score":0.011900000274181366,"subfield":{"id":"https://openalex.org/subfields/3207","display_name":"Social Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10709","display_name":"Social Robot Interaction and HRI","score":0.00570000009611249,"subfield":{"id":"https://openalex.org/subfields/3207","display_name":"Social Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/collision-avoidance","display_name":"Collision avoidance","score":0.7592999935150146},{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.7484999895095825},{"id":"https://openalex.org/keywords/collision","display_name":"Collision","score":0.6504999995231628},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.5493999719619751},{"id":"https://openalex.org/keywords/collision-avoidance-system","display_name":"Collision avoidance system","score":0.40470001101493835},{"id":"https://openalex.org/keywords/robot","display_name":"Robot","score":0.3840999901294708}],"concepts":[{"id":"https://openalex.org/C2780864053","wikidata":"https://www.wikidata.org/wiki/Q5147495","display_name":"Collision avoidance","level":3,"score":0.7592999935150146},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.7484999895095825},{"id":"https://openalex.org/C121704057","wikidata":"https://www.wikidata.org/wiki/Q352070","display_name":"Collision","level":2,"score":0.6504999995231628},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6484000086784363},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6089000105857849},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.5493999719619751},{"id":"https://openalex.org/C2777016798","wikidata":"https://www.wikidata.org/wiki/Q2001988","display_name":"Collision avoidance system","level":4,"score":0.40470001101493835},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.39820000529289246},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.3840999901294708},{"id":"https://openalex.org/C34413123","wikidata":"https://www.wikidata.org/wiki/Q170978","display_name":"Robotics","level":3,"score":0.3199999928474426},{"id":"https://openalex.org/C2780791683","wikidata":"https://www.wikidata.org/wiki/Q846785","display_name":"Action (physics)","level":2,"score":0.3163999915122986},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.311599999666214},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2766999900341034},{"id":"https://openalex.org/C44154836","wikidata":"https://www.wikidata.org/wiki/Q45045","display_name":"Simulation","level":1,"score":0.2615000009536743}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1109/ijcnn64981.2025.11229361","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11229361","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"},{"id":"pmh:oai:arXiv.org:2506.09626","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2506.09626","pdf_url":"https://arxiv.org/pdf/2506.09626","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"pmh:oai:iris.unive.it:10278/5097771","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2506.09626v1","pdf_url":"https://arxiv.org/pdf/2506.09626","source":{"id":"https://openalex.org/S4306402336","display_name":"ARCA (Universit\u00e0 Ca' Foscari Venezia)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I149461666","host_organization_name":"Ca' Foscari University of Venice","host_organization_lineage":["https://openalex.org/I149461666"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"info:eu-repo/semantics/conferenceObject"},{"id":"pmh:doi:10.48550/arxiv.2506.09626","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2506.09626","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2506.09626","pdf_url":"https://arxiv.org/pdf/2506.09626","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":37,"referenced_works":["https://openalex.org/W1970206276","https://openalex.org/W2142943472","https://openalex.org/W2167052694","https://openalex.org/W2424778531","https://openalex.org/W2476548250","https://openalex.org/W2532516272","https://openalex.org/W2607296803","https://openalex.org/W2750823073","https://openalex.org/W2801667201","https://openalex.org/W2962687116","https://openalex.org/W2963001155","https://openalex.org/W2963353290","https://openalex.org/W2963610939","https://openalex.org/W3003906095","https://openalex.org/W3035285524","https://openalex.org/W3108908812","https://openalex.org/W3115517346","https://openalex.org/W3116651890","https://openalex.org/W3139491754","https://openalex.org/W3160050461","https://openalex.org/W3169575318","https://openalex.org/W3177765762","https://openalex.org/W3188998254","https://openalex.org/W3194018559","https://openalex.org/W3204345677","https://openalex.org/W4210457203","https://openalex.org/W4214593147","https://openalex.org/W4312305613","https://openalex.org/W4313054679","https://openalex.org/W4313131073","https://openalex.org/W4318976340","https://openalex.org/W4385245566","https://openalex.org/W4386066131","https://openalex.org/W4388543829","https://openalex.org/W4390872831","https://openalex.org/W4402754122","https://openalex.org/W4404892945"],"related_works":[],"abstract_inverted_index":{"Human":[0],"trajectory":[1,81],"forecasting":[2,15,82],"is":[3,128],"crucial":[4],"in":[5],"applications":[6],"such":[7],"as":[8],"autonomous":[9],"driving,":[10],"robotics":[11],"and":[12,29,99,101],"surveillance.":[13],"Accurate":[14],"requires":[16],"models":[17],"to":[18,49,65,87],"consider":[19],"various":[20],"factors,":[21,38],"including":[22],"social":[23],"interactions,":[24],"multi-modal":[25],"predictions,":[26],"pedestrian":[27],"intention":[28],"environmental":[30],"context.":[31],"While":[32],"existing":[33,80],"methods":[34,113],"account":[35],"for":[36],"these":[37],"they":[39],"often":[40],"overlook":[41],"the":[42,45,71,96,117,123],"impact":[43],"of":[44],"environment,":[46],"which":[47],"leads":[48],"collisions":[50],"with":[51,70,122],"obstacles.":[52],"This":[53],"paper":[54],"introduces":[55],"ECAM":[56],"(Environmental":[57],"Collision":[58],"Avoidance":[59],"Module),":[60],"a":[61],"contrastive":[62],"learning-based":[63],"module":[64,75],"enhance":[66],"collision":[67,105,118],"avoidance":[68,106],"ability":[69,86],"environment.":[72],"The":[73,126],"proposed":[74,124],"can":[76],"be":[77],"integrated":[78,121],"into":[79],"models,":[83],"improving":[84],"their":[85],"generate":[88],"collision-free":[89],"predictions.":[90],"We":[91],"evaluate":[92],"our":[93],"method":[94],"on":[95],"ETH/UCY":[97],"dataset":[98],"quantitatively":[100],"qualitatively":[102],"demonstrate":[103],"its":[104],"capabilities.":[107],"Our":[108],"experiments":[109],"show":[110],"that":[111],"state-of-the-art":[112],"significantly":[114],"reduce":[115],"(-40/50%)":[116],"rate":[119],"when":[120],"module.":[125],"code":[127],"available":[129],"at":[130],"https://github.com/CVML-CFU/ECAM.":[131]},"counts_by_year":[],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2025-10-10T00:00:00"}
