{"id":"https://openalex.org/W4416749446","doi":"https://doi.org/10.1109/iros60139.2025.11247640","title":"Learning Through Retrospection: Improving Trajectory Prediction for Automated Driving with Error Feedback","display_name":"Learning Through Retrospection: Improving Trajectory Prediction for Automated Driving with Error Feedback","publication_year":2025,"publication_date":"2025-10-19","ids":{"openalex":"https://openalex.org/W4416749446","doi":"https://doi.org/10.1109/iros60139.2025.11247640"},"language":"en","primary_location":{"id":"doi:10.1109/iros60139.2025.11247640","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iros60139.2025.11247640","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":"conference-paper","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/A5053858419","display_name":"Steffen Hagedorn","orcid":null},"institutions":[{"id":"https://openalex.org/I2802101015","display_name":"Robert Bosch Stiftung","ror":"https://ror.org/012kqkf58","country_code":"DE","type":"funder","lineage":["https://openalex.org/I2802101015"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Steffen Hagedorn","raw_affiliation_strings":["University of L&#x00FC;beck,Robert Bosch GmbH, Leonberg, Germany and Institute for Neuro- and Bioinformatics,Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of L&#x00FC;beck,Robert Bosch GmbH, Leonberg, Germany and Institute for Neuro- and Bioinformatics,Germany","institution_ids":["https://openalex.org/I2802101015"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5114389183","display_name":"Aron Distelzweig","orcid":null},"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":"Aron Distelzweig","raw_affiliation_strings":["University of Freiburg,Department of Computer Science,Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Freiburg,Department of Computer Science,Germany","institution_ids":["https://openalex.org/I161046081"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5040264128","display_name":"Marcel Hallgarten","orcid":"https://orcid.org/0009-0003-4652-0466"},"institutions":[{"id":"https://openalex.org/I4210156055","display_name":"Robert Bosch (Netherlands)","ror":"https://ror.org/057aydj06","country_code":"NL","type":"company","lineage":["https://openalex.org/I4210156055","https://openalex.org/I889804353"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Marcel Hallgarten","raw_affiliation_strings":["University of T&#x00FC;bingen,Robert Bosch GmbH, Renningen, Germany and Cognitive Systems Group,Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of T&#x00FC;bingen,Robert Bosch GmbH, Renningen, Germany and Cognitive Systems Group,Germany","institution_ids":["https://openalex.org/I4210156055"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5112353924","display_name":"Alexandru Paul Condurache","orcid":null},"institutions":[{"id":"https://openalex.org/I2802101015","display_name":"Robert Bosch Stiftung","ror":"https://ror.org/012kqkf58","country_code":"DE","type":"funder","lineage":["https://openalex.org/I2802101015"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Alexandru P. Condurache","raw_affiliation_strings":["University of L&#x00FC;beck,Robert Bosch GmbH, Leonberg, Germany and Institute for Neuro- and Bioinformatics,Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of L&#x00FC;beck,Robert Bosch GmbH, Leonberg, Germany and Institute for Neuro- and Bioinformatics,Germany","institution_ids":["https://openalex.org/I2802101015"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"12064","last_page":"12069"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.9740999937057495,"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":0.9740999937057495,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.002899999963119626,"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/T10805","display_name":"Vehicle Dynamics and Control Systems","score":0.0020000000949949026,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.7146999835968018},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.6212000250816345},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5558000206947327},{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.5060999989509583},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.4505000114440918},{"id":"https://openalex.org/keywords/baseline","display_name":"Baseline (sea)","score":0.42669999599456787},{"id":"https://openalex.org/keywords/mean-squared-prediction-error","display_name":"Mean squared prediction error","score":0.41519999504089355}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7450000047683716},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.7146999835968018},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6212000250816345},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5558000206947327},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5422999858856201},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5412999987602234},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.5060999989509583},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.4505000114440918},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.42669999599456787},{"id":"https://openalex.org/C167085575","wikidata":"https://www.wikidata.org/wiki/Q6803654","display_name":"Mean squared prediction error","level":2,"score":0.41519999504089355},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.2946999967098236},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2777999937534332},{"id":"https://openalex.org/C115901376","wikidata":"https://www.wikidata.org/wiki/Q184199","display_name":"Automation","level":2,"score":0.27480000257492065},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.27230000495910645},{"id":"https://openalex.org/C103088060","wikidata":"https://www.wikidata.org/wiki/Q1062839","display_name":"Error detection and correction","level":2,"score":0.2705000042915344},{"id":"https://openalex.org/C2776544517","wikidata":"https://www.wikidata.org/wiki/Q189447","display_name":"Unexpected events","level":2,"score":0.2669000029563904},{"id":"https://openalex.org/C107551265","wikidata":"https://www.wikidata.org/wiki/Q1458245","display_name":"Displacement (psychology)","level":2,"score":0.26019999384880066},{"id":"https://openalex.org/C2779714256","wikidata":"https://www.wikidata.org/wiki/Q25305062","display_name":"Multiple Models","level":2,"score":0.25279998779296875}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/iros60139.2025.11247640","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iros60139.2025.11247640","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"},{"id":"pmh:oai:freidok.uni-freiburg.de:282820","is_oa":false,"landing_page_url":"https://freidok.uni-freiburg.de/data/282820","pdf_url":null,"source":{"id":"https://openalex.org/S4306401057","display_name":"FreiDok plus (Universit\u00e4tsbibliothek Freiburg)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I161046081","host_organization_name":"University of Freiburg","host_organization_lineage":["https://openalex.org/I161046081"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"The 2025 IEEE/RSJ International Conference on Intelligent Robots and Systems. - Piscataway, NJ, 2025. - 12064-12069, ISBN: 979-8-3315-4393-8","raw_type":"article_in_conference_proceedings"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":32,"referenced_works":["https://openalex.org/W2119112357","https://openalex.org/W2766447205","https://openalex.org/W2955189650","https://openalex.org/W2962894046","https://openalex.org/W2967177252","https://openalex.org/W3009593063","https://openalex.org/W3034722190","https://openalex.org/W3035574168","https://openalex.org/W3035671534","https://openalex.org/W3108486966","https://openalex.org/W3115436181","https://openalex.org/W3116651890","https://openalex.org/W3125605478","https://openalex.org/W3139491754","https://openalex.org/W3169575318","https://openalex.org/W3180491419","https://openalex.org/W3198662297","https://openalex.org/W3204875639","https://openalex.org/W4226239849","https://openalex.org/W4312473433","https://openalex.org/W4312731878","https://openalex.org/W4383172002","https://openalex.org/W4390017891","https://openalex.org/W4390037774","https://openalex.org/W4390872108","https://openalex.org/W4393153766","https://openalex.org/W4400644498","https://openalex.org/W4402449725","https://openalex.org/W4402727673","https://openalex.org/W4402916169","https://openalex.org/W4410738960","https://openalex.org/W4413145140"],"related_works":[],"abstract_inverted_index":{"In":[0],"automated":[1],"driving,":[2],"predicting":[3],"trajectories":[4,33],"of":[5,30,47,109,138,154,162],"surrounding":[6],"vehicles":[7],"supports":[8],"reasoning":[9],"about":[10],"scene":[11],"dynamics":[12],"and":[13,62,70,101,127],"enables":[14],"safe":[15],"planning":[16],"for":[17],"the":[18,41,48,54,86,107,113,144,152],"ego":[19],"vehicle.":[20],"However,":[21],"existing":[22],"models":[23],"handle":[24],"predictions":[25,100],"as":[26],"an":[27],"instantaneous":[28],"task":[29],"forecasting":[31],"future":[32],"based":[34],"on":[35,83,98,125],"observed":[36],"information.":[37],"As":[38],"time":[39],"proceeds,":[40],"next":[42],"prediction":[43],"is":[44],"made":[45],"independently":[46],"previous":[49,99],"one,":[50],"which":[51],"means":[52],"that":[53],"model":[55,87,114],"cannot":[56],"correct":[57,118],"its":[58,103],"errors":[59,104,120],"during":[60,121],"inference":[61],"will":[63],"repeat":[64],"them.":[65],"To":[66],"alleviate":[67],"this":[68],"problem":[69],"better":[71,160],"leverage":[72],"temporal":[73],"data,":[74],"we":[75],"propose":[76],"a":[77,130,159],"novel":[78],"retrospection":[79],"technique.":[80],"Through":[81],"training":[82],"closed-loop":[84],"rollouts":[85],"learns":[88],"to":[89,105,117,140,143],"use":[90],"aggregated":[91],"feedback.":[92],"Given":[93],"new":[94],"observations":[95],"it":[96],"reflects":[97],"analyzes":[102],"improve":[106],"quality":[108],"subsequent":[110],"predictions.":[111],"Thus,":[112],"can":[115],"learn":[116],"systematic":[119],"inference.":[122],"Comprehensive":[123],"experiments":[124],"nuScenes":[126],"Argoverse":[128],"demonstrate":[129],"considerable":[131],"decrease":[132],"in":[133],"minimum":[134],"Average":[135],"Displacement":[136],"Error":[137],"up":[139],"31.9%":[141],"compared":[142],"state-of-the-art":[145],"baseline":[146],"without":[147],"retrospection.":[148],"We":[149],"further":[150],"showcase":[151],"robustness":[153],"our":[155],"technique":[156],"by":[157],"demonstrating":[158],"handling":[161],"out-of-distribution":[163],"scenarios":[164],"with":[165],"undetected":[166],"road-users.":[167]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-11-28T00:00:00"}
