{"id":"https://openalex.org/W3005427504","doi":"https://doi.org/10.1109/icar46387.2019.8981566","title":"A Cognitive Urban Collision Avoidance Framework Based on Agents Priority Using Recurrent Neural Network","display_name":"A Cognitive Urban Collision Avoidance Framework Based on Agents Priority Using Recurrent Neural Network","publication_year":2019,"publication_date":"2019-12-01","ids":{"openalex":"https://openalex.org/W3005427504","doi":"https://doi.org/10.1109/icar46387.2019.8981566","mag":"3005427504"},"language":"en","primary_location":{"id":"doi:10.1109/icar46387.2019.8981566","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icar46387.2019.8981566","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 19th International Conference on Advanced Robotics (ICAR)","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/A5110927146","display_name":"Shenghao Jiang","orcid":null},"institutions":[{"id":"https://openalex.org/I136199984","display_name":"Harvard University","ror":"https://ror.org/03vek6s52","country_code":"US","type":"education","lineage":["https://openalex.org/I136199984"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Shenghao Jiang","raw_affiliation_strings":["School of Engineering and Applied Sciences, Harvard University, 29 Oxford Street, Cambridge, MA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Engineering and Applied Sciences, Harvard University, 29 Oxford Street, Cambridge, MA, USA","institution_ids":["https://openalex.org/I136199984"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5040251822","display_name":"Macheng Shen","orcid":"https://orcid.org/0000-0002-1440-6904"},"institutions":[{"id":"https://openalex.org/I63966007","display_name":"Massachusetts Institute of Technology","ror":"https://ror.org/042nb2s44","country_code":"US","type":"education","lineage":["https://openalex.org/I63966007"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Macheng Shen","raw_affiliation_strings":["Department of Mechanical Engineering, Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, MA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Mechanical Engineering, Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, MA, USA","institution_ids":["https://openalex.org/I63966007"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.21990287,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"61","issue":null,"first_page":"474","last_page":"480"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.9998999834060669,"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":0.9998999834060669,"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/T10370","display_name":"Traffic and Road Safety","score":0.9993000030517578,"subfield":{"id":"https://openalex.org/subfields/2213","display_name":"Safety, Risk, Reliability and Quality"},"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/T10524","display_name":"Traffic control and management","score":0.9991000294685364,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems 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/computer-science","display_name":"Computer science","score":0.7262680530548096},{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.6472664475440979},{"id":"https://openalex.org/keywords/outlier","display_name":"Outlier","score":0.5576232075691223},{"id":"https://openalex.org/keywords/collision-avoidance","display_name":"Collision avoidance","score":0.4920208156108856},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.48973631858825684},{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.45814841985702515},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4578070342540741},{"id":"https://openalex.org/keywords/collision-avoidance-system","display_name":"Collision avoidance system","score":0.440211683511734},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.42869114875793457},{"id":"https://openalex.org/keywords/collision","display_name":"Collision","score":0.4183603823184967},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.34841322898864746}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7262680530548096},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.6472664475440979},{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.5576232075691223},{"id":"https://openalex.org/C2780864053","wikidata":"https://www.wikidata.org/wiki/Q5147495","display_name":"Collision avoidance","level":3,"score":0.4920208156108856},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.48973631858825684},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.45814841985702515},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4578070342540741},{"id":"https://openalex.org/C2777016798","wikidata":"https://www.wikidata.org/wiki/Q2001988","display_name":"Collision avoidance system","level":4,"score":0.440211683511734},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.42869114875793457},{"id":"https://openalex.org/C121704057","wikidata":"https://www.wikidata.org/wiki/Q352070","display_name":"Collision","level":2,"score":0.4183603823184967},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.34841322898864746},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C1276947","wikidata":"https://www.wikidata.org/wiki/Q333","display_name":"Astronomy","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icar46387.2019.8981566","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icar46387.2019.8981566","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 19th International Conference on Advanced Robotics (ICAR)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.8299999833106995,"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W1508521005","https://openalex.org/W1981290083","https://openalex.org/W1983259923","https://openalex.org/W1986864914","https://openalex.org/W1988274166","https://openalex.org/W1997121359","https://openalex.org/W2040855262","https://openalex.org/W2055556996","https://openalex.org/W2079150870","https://openalex.org/W2088173505","https://openalex.org/W2088900086","https://openalex.org/W2091392553","https://openalex.org/W2107338474","https://openalex.org/W2123514142","https://openalex.org/W2215255691","https://openalex.org/W2341848647","https://openalex.org/W2572939427","https://openalex.org/W2611696746","https://openalex.org/W2769409584","https://openalex.org/W3121899607","https://openalex.org/W6605349051"],"related_works":["https://openalex.org/W1819938260","https://openalex.org/W2340892746","https://openalex.org/W3163022373","https://openalex.org/W3005999311","https://openalex.org/W2390040493","https://openalex.org/W4253267946","https://openalex.org/W2983014903","https://openalex.org/W2945385675","https://openalex.org/W1767544057","https://openalex.org/W2153778739"],"abstract_inverted_index":{"We":[0],"propose":[1],"a":[2,20,30,34,43,172,216],"novel":[3],"cognitive":[4],"collision":[5],"avoidance":[6],"(CA)":[7],"framework":[8,168,200],"for":[9,110],"autonomous":[10],"driving":[11,121],"(AD)":[12],"vehicles":[13],"in":[14,76,175,208,224],"urban":[15,179],"environments.":[16],"In":[17],"this":[18,167],"framework,":[19],"hybrid":[21],"future":[22,144],"trajectory":[23,40,92,134],"predictor":[24,41],"is":[25,61,102,169,220],"developed,":[26],"which":[27,113,186,219],"consists":[28],"of":[29,52,81,93,166,210,222],"static":[31],"agent":[32],"classifier,":[33],"recurrent":[35],"neural":[36],"network":[37],"(RNN)":[38],"based":[39],"and":[42,66,90,135,150,162,190,204],"lane-based":[44],"kinematic":[45],"model":[46],"predictor.":[47],"To":[48],"fuse":[49],"the":[50,68,78,86,94,106,115,118,137,143,151,211],"outputs":[51],"different":[53],"predictors,":[54],"an":[55,97],"iterative":[56],"multivariate":[57],"Gaussian":[58],"weighted":[59],"algorithm":[60],"designed":[62],"to":[63,104,123],"drop":[64],"outliers":[65],"estimate":[67],"predicted":[69],"dynamic":[70,88,145],"features":[71,89],"more":[72],"reliably.":[73],"Subsequently,":[74],"fed":[75],"with":[77,85,182,215],"fused":[79],"results":[80,207],"observed":[82,194],"agents,":[83],"together":[84],"current":[87],"planned":[91,125],"ego":[95],"vehicle,":[96],"RNN-based":[98],"priority":[99,107,147,212],"prediction":[100],"engine":[101],"applied":[103],"infer":[105],"probabilities":[108,148],"distribution":[109,149,213],"CA":[111,152,184],"decision,":[112],"indicates":[114],"likelihood":[116],"that":[117],"vehicle":[119],"continue":[120],"according":[122],"its":[124],"trajectory.":[126],"By":[127],"observing":[128],"surrounding":[129],"agents'":[130],"historical":[131],"ground":[132],"truth":[133],"taking":[136],"road":[138],"geometry":[139],"constraints":[140],"into":[141],"consideration,":[142],"features,":[146],"decision":[153],"can":[154],"be":[155],"figured":[156],"out":[157],"at":[158],"every":[159],"timestamp":[160],"cognitively":[161],"adaptively.":[163],"The":[164],"performance":[165],"evaluated":[170],"on":[171],"prototype":[173],"car":[174],"multiple":[176],"typical":[177],"USA":[178],"scenarios,":[180],"comparing":[181],"conventional":[183],"systems":[185],"assume":[187],"constant":[188],"velocity":[189],"only":[191],"work":[192],"when":[193],"agents":[195],"follow":[196],"traffic":[197],"rules,":[198],"our":[199],"alleviates":[201],"these":[202],"limitations":[203],"achieves":[205],"encouraging":[206],"terms":[209],"estimation,":[214],"frequency":[217],">20Hz,":[218],"capable":[221],"running":[223],"real-time.":[225]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}