{"id":"https://openalex.org/W7157253347","doi":"https://doi.org/10.48550/arxiv.2604.22973","title":"Collaborative Trajectory Prediction via Late Fusion","display_name":"Collaborative Trajectory Prediction via Late Fusion","publication_year":2026,"publication_date":"2026-04-24","ids":{"openalex":"https://openalex.org/W7157253347","doi":"https://doi.org/10.48550/arxiv.2604.22973"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.22973","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.22973","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2604.22973","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5041956030","display_name":"Nadya Abdel Madjid","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Madjid, Nadya Abdel","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5092050188","display_name":"Murad Mebrahtu","orcid":"https://orcid.org/0000-0002-3305-9055"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mebrahtu, Murad","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5097424597","display_name":"Zakhar Yagudin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yagudin, Zakhar","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134754652","display_name":"Bilal Hassan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hassan, Bilal","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5059512412","display_name":"Naoufel Werghi","orcid":"https://orcid.org/0000-0002-5542-448X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Werghi, Naoufel","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134779061","display_name":"Jorge Dias","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dias, Jorge","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133608827","display_name":"Dzmitry Tsetserukou","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tsetserukou, Dzmitry","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5056369727","display_name":"Majid Khonji","orcid":"https://orcid.org/0000-0002-1548-9377"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Khonji, Majid","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.9323999881744385,"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":0.9323999881744385,"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/T10761","display_name":"Vehicular Ad Hoc Networks (VANETs)","score":0.014399999752640724,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic 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":0.008100000210106373,"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":0.6445000171661377},{"id":"https://openalex.org/keywords/asynchronous-communication","display_name":"Asynchronous communication","score":0.5683000087738037},{"id":"https://openalex.org/keywords/overhead","display_name":"Overhead (engineering)","score":0.5034000277519226},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4650999903678894},{"id":"https://openalex.org/keywords/sensor-fusion","display_name":"Sensor fusion","score":0.43070000410079956},{"id":"https://openalex.org/keywords/fuse","display_name":"Fuse (electrical)","score":0.3871000111103058},{"id":"https://openalex.org/keywords/perception","display_name":"Perception","score":0.3779999911785126},{"id":"https://openalex.org/keywords/collision-avoidance","display_name":"Collision avoidance","score":0.3714999854564667}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.704800009727478},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.6445000171661377},{"id":"https://openalex.org/C151319957","wikidata":"https://www.wikidata.org/wiki/Q752739","display_name":"Asynchronous communication","level":2,"score":0.5683000087738037},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.5034000277519226},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.49239999055862427},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4650999903678894},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4498000144958496},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.43070000410079956},{"id":"https://openalex.org/C141353440","wikidata":"https://www.wikidata.org/wiki/Q182221","display_name":"Fuse (electrical)","level":2,"score":0.3871000111103058},{"id":"https://openalex.org/C26760741","wikidata":"https://www.wikidata.org/wiki/Q160402","display_name":"Perception","level":2,"score":0.3779999911785126},{"id":"https://openalex.org/C2780864053","wikidata":"https://www.wikidata.org/wiki/Q5147495","display_name":"Collision avoidance","level":3,"score":0.3714999854564667},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3571999967098236},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.35339999198913574},{"id":"https://openalex.org/C87833898","wikidata":"https://www.wikidata.org/wiki/Q1060280","display_name":"Advanced driver assistance systems","level":2,"score":0.3495999872684479},{"id":"https://openalex.org/C121704057","wikidata":"https://www.wikidata.org/wiki/Q352070","display_name":"Collision","level":2,"score":0.34040001034736633},{"id":"https://openalex.org/C173414695","wikidata":"https://www.wikidata.org/wiki/Q5510276","display_name":"Fusion mechanism","level":4,"score":0.33070001006126404},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.32659998536109924},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.2903999984264374},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.28200000524520874},{"id":"https://openalex.org/C2776459999","wikidata":"https://www.wikidata.org/wiki/Q2119376","display_name":"Fidelity","level":2,"score":0.2793999910354614},{"id":"https://openalex.org/C2781133158","wikidata":"https://www.wikidata.org/wiki/Q1088669","display_name":"Survivability","level":2,"score":0.27459999918937683},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.26170000433921814},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.26170000433921814},{"id":"https://openalex.org/C107551265","wikidata":"https://www.wikidata.org/wiki/Q1458245","display_name":"Displacement (psychology)","level":2,"score":0.25920000672340393},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.25859999656677246},{"id":"https://openalex.org/C2776257435","wikidata":"https://www.wikidata.org/wiki/Q1576430","display_name":"Bandwidth (computing)","level":2,"score":0.25290000438690186}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.22973","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.22973","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2604.22973","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.22973","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Predicting":[0],"future":[1,79],"trajectories":[2],"of":[3,91,173],"surrounding":[4],"traffic":[5],"agents":[6,175],"is":[7,75,128],"critical":[8],"for":[9,123,198],"safe":[10],"autonomous":[11],"navigation":[12],"and":[13,37,95,100,118,130,146,151,163,196],"collision":[14],"avoidance.":[15],"Despite":[16],"all":[17,155],"advances":[18],"in":[19],"the":[20,24,62,78,89,115,140,143,171,184,191],"trajectory":[21,54,165],"forecasting":[22],"realm,":[23],"prediction":[25,55,116,189],"models":[26],"remains":[27],"vulnerable":[28],"to":[29,65,88,114],"uncertainty":[30,47],"caused":[31],"by":[32,48,109,194],"occlusions,":[33],"limited":[34],"sensing":[35],"range,":[36],"perception":[38,63,113],"errors.":[39],"Collaborative":[40],"vehicle-to-vehicle":[41],"(V2V)":[42],"approaches":[43],"help":[44],"reduce":[45],"this":[46,72],"sharing":[49],"complementary":[50],"information.":[51],"Existing":[52],"collaborative":[53,152,188],"methods":[56],"typically":[57],"fuse":[58],"feature":[59,93],"maps":[60],"at":[61],"stage":[64],"construct":[66],"a":[67,120],"holistic":[68,73],"scene":[69],"view.":[70],"Further":[71],"representation":[74],"decoded":[76],"into":[77],"trajectories.":[80],"Such":[81],"design":[82],"incurs":[83],"substantial":[84],"communication":[85],"overhead":[86],"due":[87],"exchange":[90],"high-dimensional":[92],"representations":[94],"often":[96],"assumes":[97],"idealized":[98],"bandwidth":[99],"synchronization,":[101],"limiting":[102],"practical":[103],"deployment.":[104],"We":[105,138],"address":[106],"these":[107],"limitations":[108],"shifting":[110],"collaboration":[111],"from":[112],"module":[117],"introducing":[119],"late-fusion":[121],"framework":[122,127],"shared":[124],"forecasts.":[125],"The":[126],"model-agnostic":[129],"treats":[131],"collaborating":[132],"vehicles":[133],"as":[134,170],"independent":[135],"asynchronous":[136],"agents.":[137],"evaluate":[139],"approach":[141],"on":[142],"OPV2V,":[144],"V2V4Real,":[145],"DeepAccident":[147],"datasets,":[148,156],"comparing":[149],"individual":[150,205],"forecasting.":[153,206],"Across":[154],"late":[157],"fusion":[158],"consistently":[159],"reduces":[160],"miss":[161],"rate":[162,167,193],"improves":[164,190],"success":[166,192],"($\\mathrm{TSR}_{0.5}$),":[168],"defined":[169],"fraction":[172],"ground-truth":[174],"with":[176,204],"final":[177],"displacement":[178],"error":[179],"below":[180],"0.5":[181],"m.":[182],"On":[183],"real-world":[185],"V2V4Real":[186],"dataset,":[187],"$1.69\\%$":[195],"$1.22\\%$":[197],"both":[199],"intelligent":[200],"vehicles,":[201],"respectively,":[202],"compared":[203]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-04-29T00:00:00"}