{"id":"https://openalex.org/W7138061269","doi":"https://doi.org/10.1609/aaai.v40i1.37045","title":"Predict and Resist: Long-Term Accident Anticipation Under Sensor Noise","display_name":"Predict and Resist: Long-Term Accident Anticipation Under Sensor Noise","publication_year":2026,"publication_date":"2026-03-14","ids":{"openalex":"https://openalex.org/W7138061269","doi":"https://doi.org/10.1609/aaai.v40i1.37045"},"language":null,"primary_location":{"id":"doi:10.1609/aaai.v40i1.37045","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v40i1.37045","pdf_url":null,"source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://doi.org/10.1609/aaai.v40i1.37045","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5082916240","display_name":"Xingcheng Liu","orcid":"https://orcid.org/0000-0002-1910-1376"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Xingcheng Liu","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5124390217","display_name":"Bin Rao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bin Rao","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129679956","display_name":"Yanchen Guan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yanchen Guan","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129729172","display_name":"Chengyue Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chengyue Wang","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129728501","display_name":"Haicheng Liao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Haicheng Liao","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129668527","display_name":"Jiaxun Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jiaxun Zhang","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101406656","display_name":"Chengyu Lin","orcid":"https://orcid.org/0000-0002-7904-3673"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chengyu Lin","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129699799","display_name":"Meixin Zhu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Meixin Zhu","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5129686169","display_name":"Zhenning Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhenning Li","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":9,"corresponding_author_ids":["https://openalex.org/A5082916240"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.38181818,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"40","issue":"1","first_page":"782","last_page":"790"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.5546000003814697,"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":0.5546000003814697,"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":0.22669999301433563,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.029899999499320984,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/anticipation","display_name":"Anticipation (artificial intelligence)","score":0.6001999974250793},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.478300005197525},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.46380001306533813},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.39910000562667847},{"id":"https://openalex.org/keywords/motion","display_name":"Motion (physics)","score":0.3977000117301941},{"id":"https://openalex.org/keywords/perspective","display_name":"Perspective (graphical)","score":0.3458000123500824},{"id":"https://openalex.org/keywords/moment","display_name":"Moment (physics)","score":0.3361000120639801},{"id":"https://openalex.org/keywords/noise-reduction","display_name":"Noise reduction","score":0.3336000144481659}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7178000211715698},{"id":"https://openalex.org/C176777502","wikidata":"https://www.wikidata.org/wiki/Q4774623","display_name":"Anticipation (artificial intelligence)","level":2,"score":0.6001999974250793},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5663999915122986},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.478300005197525},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.46380001306533813},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4462999999523163},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.43130001425743103},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.39910000562667847},{"id":"https://openalex.org/C104114177","wikidata":"https://www.wikidata.org/wiki/Q79782","display_name":"Motion (physics)","level":2,"score":0.3977000117301941},{"id":"https://openalex.org/C12713177","wikidata":"https://www.wikidata.org/wiki/Q1900281","display_name":"Perspective (graphical)","level":2,"score":0.3458000123500824},{"id":"https://openalex.org/C179254644","wikidata":"https://www.wikidata.org/wiki/Q13222844","display_name":"Moment (physics)","level":2,"score":0.3361000120639801},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.3336000144481659},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.30329999327659607},{"id":"https://openalex.org/C29825287","wikidata":"https://www.wikidata.org/wiki/Q1427940","display_name":"Warning system","level":2,"score":0.29409998655319214},{"id":"https://openalex.org/C70836080","wikidata":"https://www.wikidata.org/wiki/Q837940","display_name":"Impulse (physics)","level":2,"score":0.28700000047683716},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.27709999680519104},{"id":"https://openalex.org/C127372701","wikidata":"https://www.wikidata.org/wiki/Q16979398","display_name":"Impulse noise","level":3,"score":0.2720000147819519},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.27079999446868896},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.26840001344680786},{"id":"https://openalex.org/C87833898","wikidata":"https://www.wikidata.org/wiki/Q1060280","display_name":"Advanced driver assistance systems","level":2,"score":0.26570001244544983},{"id":"https://openalex.org/C29265498","wikidata":"https://www.wikidata.org/wiki/Q7047719","display_name":"Noise measurement","level":3,"score":0.262800008058548},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.25209999084472656}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1609/aaai.v40i1.37045","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v40i1.37045","pdf_url":null,"source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1609/aaai.v40i1.37045","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v40i1.37045","pdf_url":null,"source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"},"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":{"Accident":[0],"anticipation":[1],"is":[2],"essential":[3],"for":[4,174],"proactive":[5],"and":[6,41,82,91,106,134,146,160,166],"safe":[7],"autonomous":[8],"driving,":[9],"where":[10],"even":[11],"a":[12,60,68],"brief":[13],"advance":[14],"warning":[15],"can":[16],"enable":[17],"critical":[18,89],"evasive":[19],"actions.":[20],"However,":[21],"two":[22],"key":[23],"challenges":[24],"hinder":[25],"real-world":[26],"deployment:":[27],"(1)":[28],"noisy":[29],"or":[30,38],"degraded":[31],"sensory":[32],"inputs":[33],"from":[34],"weather,":[35],"motion":[36,90],"blur,":[37],"hardware":[39],"limitations,":[40],"(2)":[42],"the":[43,99,111],"need":[44],"to":[45,72,109,114],"issue":[46],"timely":[47],"yet":[48],"reliable":[49],"predictions":[50,162],"that":[51,63,153],"balance":[52],"early":[53,119],"alerts":[54],"with":[55,67,121],"false-alarm":[56],"suppression.":[57],"We":[58],"propose":[59],"unified":[61],"framework":[62],"integrates":[64],"diffusion-based":[65],"denoising":[66],"time-aware":[69],"actor-critic":[70,100],"model":[71,155],"address":[73],"these":[74],"challenges.":[75],"The":[76],"diffusion":[77],"module":[78],"reconstructs":[79],"noise-resilient":[80],"image":[81],"object":[83],"features":[84],"through":[85],"iterative":[86],"refinement,":[87],"preserving":[88],"interaction":[92],"cues":[93],"under":[94,144],"sensor":[95],"degradation.":[96],"In":[97],"parallel,":[98],"architecture":[101],"leverages":[102],"long-horizon":[103],"temporal":[104],"reasoning":[105],"time-weighted":[107],"rewards":[108],"determine":[110],"optimal":[112],"moment":[113],"raise":[115],"an":[116],"alert,":[117],"aligning":[118],"detection":[120],"reliability.":[122],"Experiments":[123],"on":[124],"three":[125],"benchmark":[126],"datasets":[127],"(DAD,":[128],"CCD,":[129],"A3D)":[130],"demonstrate":[131],"state-of-the-art":[132],"accuracy":[133],"significant":[135],"gains":[136],"in":[137,163],"mean":[138],"time-to-accident,":[139],"while":[140],"maintaining":[141],"robust":[142],"performance":[143],"Gaussian":[145],"impulse":[147],"noise.":[148],"Qualitative":[149],"analyses":[150],"further":[151],"show":[152],"our":[154],"produces":[156],"earlier,":[157],"more":[158],"stable,":[159],"human-aligned":[161],"both":[164],"routine":[165],"highly":[167],"complex":[168],"traffic":[169],"scenarios,":[170],"highlighting":[171],"its":[172],"potential":[173],"real-world,":[175],"safety-critical":[176],"deployment.":[177]},"counts_by_year":[],"updated_date":"2026-03-18T06:31:55.123368","created_date":"2026-03-18T00:00:00"}
