{"id":"https://openalex.org/W2786725038","doi":"https://doi.org/10.1109/ssci.2017.8285398","title":"Accurate pedestrian path prediction using neural networks","display_name":"Accurate pedestrian path prediction using neural networks","publication_year":2017,"publication_date":"2017-11-01","ids":{"openalex":"https://openalex.org/W2786725038","doi":"https://doi.org/10.1109/ssci.2017.8285398","mag":"2786725038"},"language":"en","primary_location":{"id":"doi:10.1109/ssci.2017.8285398","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ssci.2017.8285398","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE Symposium Series on Computational Intelligence (SSCI)","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/A5054035359","display_name":"Yi Lu Murphey","orcid":"https://orcid.org/0000-0002-0501-8002"},"institutions":[{"id":"https://openalex.org/I4210130704","display_name":"University of Michigan\u2013Dearborn","ror":"https://ror.org/035wtm547","country_code":"US","type":"education","lineage":["https://openalex.org/I4210130704"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yi Lu Murphey","raw_affiliation_strings":["Department of Electrical and Computer Engineering, University of Michigan-Dearborn, Dearborn, Michigan, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, University of Michigan-Dearborn, Dearborn, Michigan, USA","institution_ids":["https://openalex.org/I4210130704"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100353132","display_name":"Chang Liu","orcid":"https://orcid.org/0000-0001-5012-7921"},"institutions":[{"id":"https://openalex.org/I4210130704","display_name":"University of Michigan\u2013Dearborn","ror":"https://ror.org/035wtm547","country_code":"US","type":"education","lineage":["https://openalex.org/I4210130704"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Chang Liu","raw_affiliation_strings":["Department of Electrical and Computer Engineering, University of Michigan-Dearborn, Dearborn, Michigan, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, University of Michigan-Dearborn, Dearborn, Michigan, USA","institution_ids":["https://openalex.org/I4210130704"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006619606","display_name":"Muhammad Tayyab","orcid":"https://orcid.org/0000-0003-1860-7372"},"institutions":[{"id":"https://openalex.org/I4210130704","display_name":"University of Michigan\u2013Dearborn","ror":"https://ror.org/035wtm547","country_code":"US","type":"education","lineage":["https://openalex.org/I4210130704"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Muhammad Tayyab","raw_affiliation_strings":["Department of Electrical and Computer Engineering, University of Michigan-Dearborn, Dearborn, Michigan, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, University of Michigan-Dearborn, Dearborn, Michigan, USA","institution_ids":["https://openalex.org/I4210130704"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5045316376","display_name":"Divyendu Narayan","orcid":null},"institutions":[{"id":"https://openalex.org/I4210130704","display_name":"University of Michigan\u2013Dearborn","ror":"https://ror.org/035wtm547","country_code":"US","type":"education","lineage":["https://openalex.org/I4210130704"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Divyendu Narayan","raw_affiliation_strings":["Department of Electrical and Computer Engineering, University of Michigan-Dearborn, Dearborn, Michigan, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, University of Michigan-Dearborn, Dearborn, Michigan, USA","institution_ids":["https://openalex.org/I4210130704"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I4210130704"],"apc_list":null,"apc_paid":null,"fwci":0.6368,"has_fulltext":false,"cited_by_count":16,"citation_normalized_percentile":{"value":0.82847617,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"7"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9997000098228455,"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"}},"topics":[{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9997000098228455,"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"}},{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9977999925613403,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"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/T11980","display_name":"Human Mobility and Location-Based Analysis","score":0.9975000023841858,"subfield":{"id":"https://openalex.org/subfields/3313","display_name":"Transportation"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/pedestrian","display_name":"Pedestrian","score":0.7900214791297913},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7235084772109985},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6142021417617798},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5963100790977478},{"id":"https://openalex.org/keywords/pedestrian-detection","display_name":"Pedestrian detection","score":0.557315468788147},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5280531048774719},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5255221724510193},{"id":"https://openalex.org/keywords/collision","display_name":"Collision","score":0.4489556550979614},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.4281199276447296},{"id":"https://openalex.org/keywords/software","display_name":"Software","score":0.4199831485748291},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3908352255821228},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.3838704526424408},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3603461682796478},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3278769850730896},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.2850433886051178},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.18809941411018372},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.10219821333885193}],"concepts":[{"id":"https://openalex.org/C2777113093","wikidata":"https://www.wikidata.org/wiki/Q221488","display_name":"Pedestrian","level":2,"score":0.7900214791297913},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7235084772109985},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6142021417617798},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5963100790977478},{"id":"https://openalex.org/C2780156472","wikidata":"https://www.wikidata.org/wiki/Q2355550","display_name":"Pedestrian detection","level":3,"score":0.557315468788147},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5280531048774719},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5255221724510193},{"id":"https://openalex.org/C121704057","wikidata":"https://www.wikidata.org/wiki/Q352070","display_name":"Collision","level":2,"score":0.4489556550979614},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.4281199276447296},{"id":"https://openalex.org/C2777904410","wikidata":"https://www.wikidata.org/wiki/Q7397","display_name":"Software","level":2,"score":0.4199831485748291},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3908352255821228},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.3838704526424408},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3603461682796478},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3278769850730896},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2850433886051178},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.18809941411018372},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.10219821333885193},{"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/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C22212356","wikidata":"https://www.wikidata.org/wiki/Q775325","display_name":"Transport engineering","level":1,"score":0.0},{"id":"https://openalex.org/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ssci.2017.8285398","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ssci.2017.8285398","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE Symposium Series on Computational Intelligence (SSCI)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5,"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320321672","display_name":"Else Kr\u00f6ner-Fresenius-Stiftung","ror":"https://ror.org/03zcxha54"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":14,"referenced_works":["https://openalex.org/W289259108","https://openalex.org/W1993793087","https://openalex.org/W2004641798","https://openalex.org/W2031454541","https://openalex.org/W2043064428","https://openalex.org/W2057067242","https://openalex.org/W2121299011","https://openalex.org/W2126413547","https://openalex.org/W2128670446","https://openalex.org/W2139479830","https://openalex.org/W2286744228","https://openalex.org/W2493462685","https://openalex.org/W2561547472","https://openalex.org/W6610479102"],"related_works":["https://openalex.org/W2392100589","https://openalex.org/W2512789322","https://openalex.org/W3122828758","https://openalex.org/W2101960027","https://openalex.org/W2972620127","https://openalex.org/W2899977359","https://openalex.org/W1630669003","https://openalex.org/W2981141433","https://openalex.org/W2913302899","https://openalex.org/W3204901196"],"abstract_inverted_index":{"This":[0],"paper":[1],"presents":[2],"a":[3,10,48,59,69,74,80,94,126,144],"study":[4,20],"on":[5,97,136],"predicting":[6],"pedestrian":[7,34,56,95],"path":[8],"in":[9,47,58],"short":[11,61],"time":[12,63,81],"horizon,":[13],"e.g.":[14],"less":[15],"than":[16],"5":[17],"seconds.":[18],"Our":[19],"is":[21,52],"conducted":[22],"within":[23],"the":[24,37,90],"context":[25],"of":[26,93],"pre-collision":[27,49],"detection":[28,50],"and":[29,33,79,118],"avoidance":[30],"between":[31],"vehicle":[32],"using":[35,143],"only":[36],"positioning":[38],"data":[39,138],"transmitted":[40],"through":[41],"V2P":[42],"communications.":[43],"An":[44,100],"important":[45,123],"component":[46],"system":[51],"to":[53,88,115],"accurately":[54],"predict":[55,89],"positions":[57],"very":[60],"future":[62,91],"period.":[64],"Three":[65],"methods":[66,133],"are":[67,86,113,122,134],"presented,":[68],"dead":[70],"reckoning":[71],"prediction":[72,119],"method,":[73],"pattern":[75,127],"recognition":[76,128],"neural":[77,83,129],"network":[78],"series":[82],"network,":[84],"both":[85],"designed":[87],"position":[92],"based":[96],"recent":[98],"movements.":[99],"innovative":[101],"feature":[102,110],"extraction":[103],"method":[104],"has":[105],"been":[106],"developed":[107],"for":[108,124],"generating":[109],"vectors":[111],"that":[112],"invariant":[114],"trip":[116,137],"location":[117],"time,":[120],"which":[121],"training":[125],"network.":[130],"All":[131],"three":[132],"evaluated":[135],"recorded":[139],"from":[140],"two":[141],"pedestrians":[142],"Smartphone":[145],"application":[146],"software.":[147]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":4},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":4}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
