{"id":"https://openalex.org/W4416749314","doi":"https://doi.org/10.1109/iros60139.2025.11247741","title":"Evidential Uncertainty Estimation for Multi-Modal Trajectory Prediction","display_name":"Evidential Uncertainty Estimation for Multi-Modal Trajectory Prediction","publication_year":2025,"publication_date":"2025-10-19","ids":{"openalex":"https://openalex.org/W4416749314","doi":"https://doi.org/10.1109/iros60139.2025.11247741"},"language":null,"primary_location":{"id":"doi:10.1109/iros60139.2025.11247741","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iros60139.2025.11247741","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","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/A5031918016","display_name":"Sajad Marvi","orcid":"https://orcid.org/0000-0003-0430-5613"},"institutions":[{"id":"https://openalex.org/I1332474105","display_name":"Mercedes-Benz (Germany)","ror":"https://ror.org/055rn2a38","country_code":"DE","type":"company","lineage":["https://openalex.org/I1332474105"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Sajad Marvi","raw_affiliation_strings":["Mercedes-Benz AG,Germany"],"affiliations":[{"raw_affiliation_string":"Mercedes-Benz AG,Germany","institution_ids":["https://openalex.org/I1332474105"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5033180385","display_name":"Christoph Rist","orcid":null},"institutions":[{"id":"https://openalex.org/I1332474105","display_name":"Mercedes-Benz (Germany)","ror":"https://ror.org/055rn2a38","country_code":"DE","type":"company","lineage":["https://openalex.org/I1332474105"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Christoph Rist","raw_affiliation_strings":["Mercedes-Benz AG,Germany"],"affiliations":[{"raw_affiliation_string":"Mercedes-Benz AG,Germany","institution_ids":["https://openalex.org/I1332474105"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5012442384","display_name":"Julian Schmidt","orcid":"https://orcid.org/0000-0003-4944-4916"},"institutions":[{"id":"https://openalex.org/I1332474105","display_name":"Mercedes-Benz (Germany)","ror":"https://ror.org/055rn2a38","country_code":"DE","type":"company","lineage":["https://openalex.org/I1332474105"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Julian Schmidt","raw_affiliation_strings":["Mercedes-Benz AG,Germany"],"affiliations":[{"raw_affiliation_string":"Mercedes-Benz AG,Germany","institution_ids":["https://openalex.org/I1332474105"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5007430306","display_name":"Julian Jordan","orcid":null},"institutions":[{"id":"https://openalex.org/I1332474105","display_name":"Mercedes-Benz (Germany)","ror":"https://ror.org/055rn2a38","country_code":"DE","type":"company","lineage":["https://openalex.org/I1332474105"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Julian Jordan","raw_affiliation_strings":["Mercedes-Benz AG,Germany"],"affiliations":[{"raw_affiliation_string":"Mercedes-Benz AG,Germany","institution_ids":["https://openalex.org/I1332474105"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5039639553","display_name":"Abhinav Valada","orcid":"https://orcid.org/0000-0003-4710-3114"},"institutions":[{"id":"https://openalex.org/I161046081","display_name":"University of Freiburg","ror":"https://ror.org/0245cg223","country_code":"DE","type":"education","lineage":["https://openalex.org/I161046081"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Abhinav Valada","raw_affiliation_strings":["University of Freiburg,Department of Computer Science,Germany"],"affiliations":[{"raw_affiliation_string":"University of Freiburg,Department of Computer Science,Germany","institution_ids":["https://openalex.org/I161046081"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5031918016"],"corresponding_institution_ids":["https://openalex.org/I1332474105"],"apc_list":null,"apc_paid":null,"fwci":0.7702,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.79082111,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":97,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"785","last_page":"792"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.9710999727249146,"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":0.9710999727249146,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.00419999985024333,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.00279999990016222,"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/trajectory","display_name":"Trajectory","score":0.7724000215530396},{"id":"https://openalex.org/keywords/mode","display_name":"Mode (computer interface)","score":0.49480000138282776},{"id":"https://openalex.org/keywords/dirichlet-distribution","display_name":"Dirichlet distribution","score":0.4602000117301941},{"id":"https://openalex.org/keywords/probability-distribution","display_name":"Probability distribution","score":0.4569999873638153},{"id":"https://openalex.org/keywords/uncertainty-quantification","display_name":"Uncertainty quantification","score":0.453900009393692},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.43950000405311584},{"id":"https://openalex.org/keywords/measurement-uncertainty","display_name":"Measurement uncertainty","score":0.3813999891281128}],"concepts":[{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.7724000215530396},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6144999861717224},{"id":"https://openalex.org/C48677424","wikidata":"https://www.wikidata.org/wiki/Q6888088","display_name":"Mode (computer interface)","level":2,"score":0.49480000138282776},{"id":"https://openalex.org/C169214877","wikidata":"https://www.wikidata.org/wiki/Q981016","display_name":"Dirichlet distribution","level":3,"score":0.4602000117301941},{"id":"https://openalex.org/C149441793","wikidata":"https://www.wikidata.org/wiki/Q200726","display_name":"Probability distribution","level":2,"score":0.4569999873638153},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.45590001344680786},{"id":"https://openalex.org/C32230216","wikidata":"https://www.wikidata.org/wiki/Q7882499","display_name":"Uncertainty quantification","level":2,"score":0.453900009393692},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.43950000405311584},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.39969998598098755},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.39660000801086426},{"id":"https://openalex.org/C137209882","wikidata":"https://www.wikidata.org/wiki/Q1403517","display_name":"Measurement uncertainty","level":2,"score":0.3813999891281128},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3059999942779541},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.2924000024795532},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.28209999203681946},{"id":"https://openalex.org/C110121322","wikidata":"https://www.wikidata.org/wiki/Q865811","display_name":"Distribution (mathematics)","level":2,"score":0.2768000066280365},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.27459999918937683},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.2703000009059906},{"id":"https://openalex.org/C29265498","wikidata":"https://www.wikidata.org/wiki/Q7047719","display_name":"Noise measurement","level":3,"score":0.2702000141143799},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.26820001006126404},{"id":"https://openalex.org/C167928553","wikidata":"https://www.wikidata.org/wiki/Q1376021","display_name":"Estimation theory","level":2,"score":0.2578999996185303}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iros60139.2025.11247741","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iros60139.2025.11247741","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":16,"referenced_works":["https://openalex.org/W2254249950","https://openalex.org/W2741066535","https://openalex.org/W2774231091","https://openalex.org/W2915977493","https://openalex.org/W2955189650","https://openalex.org/W3091146265","https://openalex.org/W3199048838","https://openalex.org/W4285744203","https://openalex.org/W4312731878","https://openalex.org/W4324292616","https://openalex.org/W4383108316","https://openalex.org/W4388521737","https://openalex.org/W4389665387","https://openalex.org/W4389666837","https://openalex.org/W4392607781","https://openalex.org/W4401497022"],"related_works":[],"abstract_inverted_index":{"Accurate":[0],"trajectory":[1,22,48,146],"prediction":[2,23,49,147],"is":[3],"crucial":[4],"for":[5,75,82],"autonomous":[6],"driving,":[7],"yet":[8],"uncertainty":[9,35,63,77,93,141],"in":[10,64,94],"agent":[11],"behavior":[12],"and":[13,60,78,132],"perception":[14],"noise":[15],"makes":[16],"it":[17,88,138],"inherently":[18],"challenging.":[19],"While":[20],"multi-modal":[21,47],"models":[24],"generate":[25],"multiple":[26],"plausible":[27],"future":[28],"paths":[29],"with":[30,105],"associated":[31],"probabilities,":[32],"effectively":[33],"quantifying":[34],"remains":[36],"an":[37],"open":[38],"problem.":[39],"In":[40],"this":[41],"work,":[42],"we":[43,103],"propose":[44],"a":[45,70,79,95],"novel":[46],"approach":[50,68],"based":[51],"on":[52,128],"evidential":[53],"deep":[54],"learning":[55],"that":[56,137],"estimates":[57,142],"both":[58,90],"positional":[59,76],"mode":[61,83],"probability":[62],"real":[65],"time.":[66],"Our":[67],"leverages":[69],"Normal":[71],"Inverse":[72],"Gamma":[73],"distribution":[74,81],"Dirichlet":[80],"uncertainty.":[84],"Unlike":[85],"sampling-based":[86],"methods,":[87],"infers":[89],"types":[91],"of":[92,125],"single":[96],"forward":[97],"pass,":[98],"significantly":[99],"improving":[100],"efficiency.":[101],"Additionally,":[102],"experimented":[104],"uncertainty-driven":[106],"importance":[107],"sampling":[108],"to":[109],"improve":[110],"training":[111],"efficiency":[112],"by":[113],"prioritizing":[114],"underrepresented":[115],"high-uncertainty":[116],"samples":[117],"over":[118],"redundant":[119],"ones.":[120],"We":[121],"perform":[122],"extensive":[123],"evaluations":[124],"our":[126],"method":[127],"the":[129],"Argoverse":[130,133],"1":[131],"2":[134],"datasets,":[135],"demonstrating":[136],"provides":[139],"reliable":[140],"while":[143],"maintaining":[144],"high":[145],"accuracy.":[148]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-03-07T16:01:11.037858","created_date":"2025-11-28T00:00:00"}