{"id":"https://openalex.org/W7106008436","doi":"https://doi.org/10.1109/lra.2025.3634880","title":"IMPACT: Behavioral Intention-Aware Multimodal Trajectory Prediction With Adaptive Context Trimming","display_name":"IMPACT: Behavioral Intention-Aware Multimodal Trajectory Prediction With Adaptive Context Trimming","publication_year":2025,"publication_date":"2025-11-19","ids":{"openalex":"https://openalex.org/W7106008436","doi":"https://doi.org/10.1109/lra.2025.3634880"},"language":null,"primary_location":{"id":"doi:10.1109/lra.2025.3634880","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lra.2025.3634880","pdf_url":null,"source":{"id":"https://openalex.org/S4210169774","display_name":"IEEE Robotics and Automation Letters","issn_l":"2377-3766","issn":["2377-3766"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Robotics and Automation Letters","raw_type":"journal-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":null,"display_name":"Jiawei Sun","orcid":"https://orcid.org/0009-0000-4532-9174"},"institutions":[{"id":"https://openalex.org/I165932596","display_name":"National University of Singapore","ror":"https://ror.org/01tgyzw49","country_code":"SG","type":"education","lineage":["https://openalex.org/I165932596"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Jiawei Sun","raw_affiliation_strings":["Department of Mechanical Engineering, National University of Singapore, Singapore"],"raw_orcid":"https://orcid.org/0009-0000-4532-9174","affiliations":[{"raw_affiliation_string":"Department of Mechanical Engineering, National University of Singapore, Singapore","institution_ids":["https://openalex.org/I165932596"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Xibin Yue","orcid":null},"institutions":[{"id":"https://openalex.org/I862669128","display_name":"Xiaomi (China)","ror":"https://ror.org/029f7bn57","country_code":"CN","type":"company","lineage":["https://openalex.org/I862669128"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xibin Yue","raw_affiliation_strings":["Xiaomi EV, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xiaomi EV, Beijing, China","institution_ids":["https://openalex.org/I862669128"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Jiahui Li","orcid":"https://orcid.org/0009-0008-4375-7472"},"institutions":[{"id":"https://openalex.org/I165932596","display_name":"National University of Singapore","ror":"https://ror.org/01tgyzw49","country_code":"SG","type":"education","lineage":["https://openalex.org/I165932596"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Jiahui Li","raw_affiliation_strings":["Department of Mechanical Engineering, National University of Singapore, Singapore"],"raw_orcid":"https://orcid.org/0009-0008-4375-7472","affiliations":[{"raw_affiliation_string":"Department of Mechanical Engineering, National University of Singapore, Singapore","institution_ids":["https://openalex.org/I165932596"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Tianle Shen","orcid":"https://orcid.org/0009-0008-2117-1163"},"institutions":[{"id":"https://openalex.org/I165932596","display_name":"National University of Singapore","ror":"https://ror.org/01tgyzw49","country_code":"SG","type":"education","lineage":["https://openalex.org/I165932596"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Tianle Shen","raw_affiliation_strings":["Department of Mechanical Engineering, National University of Singapore, Singapore"],"raw_orcid":"https://orcid.org/0009-0008-2117-1163","affiliations":[{"raw_affiliation_string":"Department of Mechanical Engineering, National University of Singapore, Singapore","institution_ids":["https://openalex.org/I165932596"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Chengran Yuan","orcid":"https://orcid.org/0009-0005-6739-0474"},"institutions":[{"id":"https://openalex.org/I165932596","display_name":"National University of Singapore","ror":"https://ror.org/01tgyzw49","country_code":"SG","type":"education","lineage":["https://openalex.org/I165932596"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Chengran Yuan","raw_affiliation_strings":["Department of Mechanical Engineering, National University of Singapore, Singapore"],"raw_orcid":"https://orcid.org/0009-0005-6739-0474","affiliations":[{"raw_affiliation_string":"Department of Mechanical Engineering, National University of Singapore, Singapore","institution_ids":["https://openalex.org/I165932596"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Shuo Sun","orcid":"https://orcid.org/0000-0002-8432-0452"},"institutions":[{"id":"https://openalex.org/I165932596","display_name":"National University of Singapore","ror":"https://ror.org/01tgyzw49","country_code":"SG","type":"education","lineage":["https://openalex.org/I165932596"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Shuo Sun","raw_affiliation_strings":["Department of Mechanical Engineering, National University of Singapore, Singapore"],"raw_orcid":"https://orcid.org/0000-0002-8432-0452","affiliations":[{"raw_affiliation_string":"Department of Mechanical Engineering, National University of Singapore, Singapore","institution_ids":["https://openalex.org/I165932596"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Sheng Guo","orcid":"https://orcid.org/0000-0002-5779-7917"},"institutions":[{"id":"https://openalex.org/I165932596","display_name":"National University of Singapore","ror":"https://ror.org/01tgyzw49","country_code":"SG","type":"education","lineage":["https://openalex.org/I165932596"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Sheng Guo","raw_affiliation_strings":["Department of Mechanical Engineering, National University of Singapore, Singapore"],"raw_orcid":"https://orcid.org/0000-0002-5779-7917","affiliations":[{"raw_affiliation_string":"Department of Mechanical Engineering, National University of Singapore, Singapore","institution_ids":["https://openalex.org/I165932596"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Quanyun Zhou","orcid":null},"institutions":[{"id":"https://openalex.org/I862669128","display_name":"Xiaomi (China)","ror":"https://ror.org/029f7bn57","country_code":"CN","type":"company","lineage":["https://openalex.org/I862669128"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Quanyun Zhou","raw_affiliation_strings":["Xiaomi EV, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xiaomi EV, Beijing, China","institution_ids":["https://openalex.org/I862669128"]}]},{"author_position":"last","author":{"id":null,"display_name":"Marcelo H Ang Jr","orcid":"https://orcid.org/0000-0001-8277-6408"},"institutions":[{"id":"https://openalex.org/I165932596","display_name":"National University of Singapore","ror":"https://ror.org/01tgyzw49","country_code":"SG","type":"education","lineage":["https://openalex.org/I165932596"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Marcelo H Ang Jr","raw_affiliation_strings":["Department of Mechanical Engineering, National University of Singapore, Singapore"],"raw_orcid":"https://orcid.org/0000-0001-8277-6408","affiliations":[{"raw_affiliation_string":"Department of Mechanical Engineering, National University of Singapore, Singapore","institution_ids":["https://openalex.org/I165932596"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":9,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.4985,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.71792415,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":"11","issue":"1","first_page":"610","last_page":"617"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.963699996471405,"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":0.963699996471405,"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":0.00279999990016222,"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"}},{"id":"https://openalex.org/T10525","display_name":"Human-Automation Interaction and Safety","score":0.002400000113993883,"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/trajectory","display_name":"Trajectory","score":0.57669997215271},{"id":"https://openalex.org/keywords/trimming","display_name":"Trimming","score":0.4684999883174896},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.45410001277923584},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.44929999113082886},{"id":"https://openalex.org/keywords/decoding-methods","display_name":"Decoding methods","score":0.4016000032424927},{"id":"https://openalex.org/keywords/redundancy","display_name":"Redundancy (engineering)","score":0.3833000063896179},{"id":"https://openalex.org/keywords/prior-probability","display_name":"Prior probability","score":0.3684999942779541},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.36579999327659607},{"id":"https://openalex.org/keywords/context-model","display_name":"Context model","score":0.358599990606308}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.819100022315979},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.57669997215271},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5583999752998352},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5461999773979187},{"id":"https://openalex.org/C56951928","wikidata":"https://www.wikidata.org/wiki/Q3539213","display_name":"Trimming","level":2,"score":0.4684999883174896},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.45410001277923584},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.44929999113082886},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.4016000032424927},{"id":"https://openalex.org/C152124472","wikidata":"https://www.wikidata.org/wiki/Q1204361","display_name":"Redundancy (engineering)","level":2,"score":0.3833000063896179},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.3684999942779541},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.36579999327659607},{"id":"https://openalex.org/C183322885","wikidata":"https://www.wikidata.org/wiki/Q17007702","display_name":"Context model","level":3,"score":0.358599990606308},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.3434000015258789},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.3190000057220459},{"id":"https://openalex.org/C147764199","wikidata":"https://www.wikidata.org/wiki/Q6865248","display_name":"Minification","level":2,"score":0.3158999979496002},{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.30970001220703125},{"id":"https://openalex.org/C167085575","wikidata":"https://www.wikidata.org/wiki/Q6803654","display_name":"Mean squared prediction error","level":2,"score":0.3091000020503998},{"id":"https://openalex.org/C108010975","wikidata":"https://www.wikidata.org/wiki/Q500094","display_name":"Pruning","level":2,"score":0.30790001153945923},{"id":"https://openalex.org/C78639753","wikidata":"https://www.wikidata.org/wiki/Q3318160","display_name":"Behavioral modeling","level":2,"score":0.3019999861717224},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.2973000109195709},{"id":"https://openalex.org/C100279451","wikidata":"https://www.wikidata.org/wiki/Q372193","display_name":"Perplexity","level":3,"score":0.2824999988079071},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2784000039100647},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.26829999685287476},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.2671000063419342},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.26330000162124634},{"id":"https://openalex.org/C55166926","wikidata":"https://www.wikidata.org/wiki/Q2892946","display_name":"Oracle","level":2,"score":0.25040000677108765}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/lra.2025.3634880","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lra.2025.3634880","pdf_url":null,"source":{"id":"https://openalex.org/S4210169774","display_name":"IEEE Robotics and Automation Letters","issn_l":"2377-3766","issn":["2377-3766"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Robotics and Automation Letters","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W3204875639","https://openalex.org/W3206626796","https://openalex.org/W4312731878","https://openalex.org/W4313189526","https://openalex.org/W4385221361","https://openalex.org/W4386076407","https://openalex.org/W4386076672","https://openalex.org/W4390788068","https://openalex.org/W4390871698","https://openalex.org/W4390874523","https://openalex.org/W4392207805","https://openalex.org/W4393078713","https://openalex.org/W4393149085","https://openalex.org/W4401416678","https://openalex.org/W4402667889","https://openalex.org/W4408696705","https://openalex.org/W4408698206","https://openalex.org/W4413144409","https://openalex.org/W4413925510","https://openalex.org/W4415795959"],"related_works":[],"abstract_inverted_index":{"This":[0],"paper":[1],"presents":[2],"a":[3,62,109],"unified":[4],"framework":[5,193,214],"that":[6,114],"jointly":[7],"predicts":[8],"behavioral":[9,50,86],"intentions":[10,51],"and":[11,31,69,76,134,147,158,176],"vectorized":[12,110],"occupancy,":[13],"leveraging":[14,131],"them":[15],"as":[16],"priors":[17],"to":[18,200],"dynamically":[19],"prune":[20],"context":[21,64],"information":[22,77],"during":[23],"trajectory":[24,45,70],"decoding,":[25],"thereby":[26,72],"enhancing":[27],"prediction":[28,112],"accuracy,":[29],"interpretability,":[30],"efficiency.":[32],"While":[33],"most":[34],"prior":[35],"work":[36],"has":[37,215],"focused":[38],"on":[39,171,180,219],"boosting":[40],"the":[41,82,100,116,125,151,172,181,195,201,212],"precision":[42],"of":[43,49,84,118,144],"multimodal":[44],"prediction,":[46],"explicit":[47],"modeling":[48],"(e.g.,":[52],"yielding,":[53],"overtaking)":[54],"remains":[55],"underexplored.":[56],"To":[57],"this":[58,104],"end,":[59],"we":[60,80],"employ":[61],"shared":[63],"encoder":[65],"for":[66],"both":[67],"intention":[68,87,133],"predictions,":[71],"reducing":[73,155],"structural":[74],"redundancy":[75],"loss.":[78],"Moreover,":[79],"address":[81],"lack":[83],"ground-truth":[85],"labels":[88],"in":[89,103,150,206,226],"mainstream":[90],"datasets":[91],"(Waymo,":[92],"Argoverse)":[93],"by":[94,124,197],"auto-labeling":[95],"these":[96,132],"datasets,":[97],"thus":[98],"advancing":[99],"community's":[101],"efforts":[102],"direction.":[105],"We":[106],"further":[107],"introduce":[108],"occupancy":[111,135],"module":[113],"infers":[115],"probability":[117],"each":[119],"map":[120,148],"polyline":[121],"being":[122],"occupied":[123],"target":[126],"vehicle's":[127],"future":[128],"trajectory.":[129],"By":[130],"predictions":[136],"priors,":[137],"our":[138,191],"method":[139],"conducts":[140],"dynamic,":[141],"modality-dependent":[142],"pruning":[143],"irrelevant":[145],"agents":[146],"polylines":[149],"decoding":[152],"stage,":[153],"effectively":[154],"computational":[156],"overhead":[157],"mitigating":[159],"noise":[160],"from":[161],"non-critical":[162],"elements.":[163],"Our":[164],"approach":[165],"ranks":[166],"first":[167],"among":[168],"LiDAR-free":[169],"methods":[170],"Waymo":[173,182,207],"Motion":[174],"Dataset":[175],"achieves":[177],"SOTA":[178,203],"performance":[179],"Interactive":[183,208],"Prediction":[184,209],"Dataset.":[185],"Remarkably,":[186],"even":[187],"without":[188],"model":[189],"ensembling,":[190],"single-model":[192],"improves":[194],"softmAP":[196],"10%":[198],"compared":[199],"previous":[202],"method,":[204],"BETOP,":[205],"Leaderboard.":[210],"Furthermore,":[211],"proposed":[213],"been":[216],"successfully":[217],"deployed":[218],"real":[220],"vehicles,":[221],"demonstrating":[222],"its":[223],"practical":[224],"effectiveness":[225],"real-world":[227],"applications.":[228]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-11-19T00:00:00"}