{"id":"https://openalex.org/W7160605440","doi":"https://doi.org/10.3390/systems14050535","title":"A Physics-Informed Neural Network for Vehicle Trajectory Reconstruction in Cut-In Scenarios with Sparse and Noisy Observations","display_name":"A Physics-Informed Neural Network for Vehicle Trajectory Reconstruction in Cut-In Scenarios with Sparse and Noisy Observations","publication_year":2026,"publication_date":"2026-05-08","ids":{"openalex":"https://openalex.org/W7160605440","doi":"https://doi.org/10.3390/systems14050535"},"language":"en","primary_location":{"id":"doi:10.3390/systems14050535","is_oa":true,"landing_page_url":"https://doi.org/10.3390/systems14050535","pdf_url":"https://www.mdpi.com/2079-8954/14/5/535/pdf?version=1778252037","source":{"id":"https://openalex.org/S4210219410","display_name":"Systems","issn_l":"2079-8954","issn":["2079-8954"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Systems","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/2079-8954/14/5/535/pdf?version=1778252037","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5135704475","display_name":"Chenyi Xie","orcid":null},"institutions":[{"id":"https://openalex.org/I1282462722","display_name":"United States Department of Transportation","ror":"https://ror.org/02xfw2e90","country_code":"US","type":"government","lineage":["https://openalex.org/I1282462722"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Chenyi Xie","raw_affiliation_strings":["Department of Modern Urban Traffic Technologies and Urban Intelligent Transportation Systems, Southeast University, Nanjing 211189, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Modern Urban Traffic Technologies and Urban Intelligent Transportation Systems, Southeast University, Nanjing 211189, China","institution_ids":["https://openalex.org/I1282462722"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135710878","display_name":"Yuan Zheng","orcid":null},"institutions":[{"id":"https://openalex.org/I1282462722","display_name":"United States Department of Transportation","ror":"https://ror.org/02xfw2e90","country_code":"US","type":"government","lineage":["https://openalex.org/I1282462722"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Yuan Zheng","raw_affiliation_strings":["Department of Modern Urban Traffic Technologies and Urban Intelligent Transportation Systems, Southeast University, Nanjing 211189, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Modern Urban Traffic Technologies and Urban Intelligent Transportation Systems, Southeast University, Nanjing 211189, China","institution_ids":["https://openalex.org/I1282462722"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135690307","display_name":"Qingchao Liu","orcid":"https://orcid.org/0000-0001-6486-0999"},"institutions":[{"id":"https://openalex.org/I115592961","display_name":"Jiangsu University","ror":"https://ror.org/03jc41j30","country_code":"CN","type":"education","lineage":["https://openalex.org/I115592961"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qingchao Liu","raw_affiliation_strings":["Automotive Engineering Research Institute, Jiangsu University, Zhenjiang 212013, China"],"raw_orcid":"https://orcid.org/0000-0001-6486-0999","affiliations":[{"raw_affiliation_string":"Automotive Engineering Research Institute, Jiangsu University, Zhenjiang 212013, China","institution_ids":["https://openalex.org/I115592961"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135638880","display_name":"Jian Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I1282462722","display_name":"United States Department of Transportation","ror":"https://ror.org/02xfw2e90","country_code":"US","type":"government","lineage":["https://openalex.org/I1282462722"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jian Wang","raw_affiliation_strings":["Department of Modern Urban Traffic Technologies and Urban Intelligent Transportation Systems, Southeast University, Nanjing 211189, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Modern Urban Traffic Technologies and Urban Intelligent Transportation Systems, Southeast University, Nanjing 211189, China","institution_ids":["https://openalex.org/I1282462722"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135655523","display_name":"Wenping Duan","orcid":null},"institutions":[{"id":"https://openalex.org/I4210153682","display_name":"Intelligent Health (United Kingdom)","ror":"https://ror.org/0576zak10","country_code":"GB","type":"company","lineage":["https://openalex.org/I4210153682"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Wenping Duan","raw_affiliation_strings":["Intelligent Ecology Platform Seres Group Co., Ltd., Chongqing 400030, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Intelligent Ecology Platform Seres Group Co., Ltd., Chongqing 400030, China","institution_ids":["https://openalex.org/I4210153682"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135718837","display_name":"Yu Tang","orcid":null},"institutions":[{"id":"https://openalex.org/I4210131649","display_name":"China Automotive Engineering Research Institute","ror":"https://ror.org/039jhgf83","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210131649"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yu Tang","raw_affiliation_strings":["Intelligent Cockpit Evaluation Department, China Automotive Engineering Research Institute Co., Ltd., Jinyu Avenue 8, Liangjiang District, Chongqing 401122, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Intelligent Cockpit Evaluation Department, China Automotive Engineering Research Institute Co., Ltd., Jinyu Avenue 8, Liangjiang District, Chongqing 401122, China","institution_ids":["https://openalex.org/I4210131649"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5135660427","display_name":"Bin Ran","orcid":null},"institutions":[{"id":"https://openalex.org/I1282462722","display_name":"United States Department of Transportation","ror":"https://ror.org/02xfw2e90","country_code":"US","type":"government","lineage":["https://openalex.org/I1282462722"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Bin Ran","raw_affiliation_strings":["Department of Modern Urban Traffic Technologies and Urban Intelligent Transportation Systems, Southeast University, Nanjing 211189, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Modern Urban Traffic Technologies and Urban Intelligent Transportation Systems, Southeast University, Nanjing 211189, China","institution_ids":["https://openalex.org/I1282462722"]}]}],"institutions":[],"countries_distinct_count":3,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5135710878"],"corresponding_institution_ids":["https://openalex.org/I1282462722"],"apc_list":{"value":1600,"currency":"CHF","value_usd":1732},"apc_paid":{"value":1600,"currency":"CHF","value_usd":1732},"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.52326261,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"14","issue":"5","first_page":"535","last_page":"535"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10524","display_name":"Traffic control and management","score":0.3781000077724457,"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"}},"topics":[{"id":"https://openalex.org/T10524","display_name":"Traffic control and management","score":0.3781000077724457,"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"}},{"id":"https://openalex.org/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.2800000011920929,"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/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.08640000224113464,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.8729000091552734},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.5737000107765198},{"id":"https://openalex.org/keywords/kinematics","display_name":"Kinematics","score":0.550599992275238},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5121999979019165},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.3982999920845032},{"id":"https://openalex.org/keywords/anticipation","display_name":"Anticipation (artificial intelligence)","score":0.3806999921798706},{"id":"https://openalex.org/keywords/baseline","display_name":"Baseline (sea)","score":0.362199991941452},{"id":"https://openalex.org/keywords/intelligent-transportation-system","display_name":"Intelligent transportation system","score":0.35510000586509705}],"concepts":[{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.8729000091552734},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5936999917030334},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.5737000107765198},{"id":"https://openalex.org/C39920418","wikidata":"https://www.wikidata.org/wiki/Q11476","display_name":"Kinematics","level":2,"score":0.550599992275238},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5121999979019165},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.3982999920845032},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3944999873638153},{"id":"https://openalex.org/C176777502","wikidata":"https://www.wikidata.org/wiki/Q4774623","display_name":"Anticipation (artificial intelligence)","level":2,"score":0.3806999921798706},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3767000138759613},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.362199991941452},{"id":"https://openalex.org/C47796450","wikidata":"https://www.wikidata.org/wiki/Q508378","display_name":"Intelligent transportation system","level":2,"score":0.35510000586509705},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.3393000066280365},{"id":"https://openalex.org/C2776029896","wikidata":"https://www.wikidata.org/wiki/Q3935810","display_name":"Relaxation (psychology)","level":2,"score":0.32359999418258667},{"id":"https://openalex.org/C2781170535","wikidata":"https://www.wikidata.org/wiki/Q30587856","display_name":"Noisy data","level":2,"score":0.32120001316070557},{"id":"https://openalex.org/C2775936607","wikidata":"https://www.wikidata.org/wiki/Q466845","display_name":"Tracking (education)","level":2,"score":0.3197999894618988},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.3174999952316284},{"id":"https://openalex.org/C183356978","wikidata":"https://www.wikidata.org/wiki/Q1779213","display_name":"Tracking error","level":3,"score":0.2896000146865845},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.28519999980926514},{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.2815999984741211},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2662999927997589},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.2655999958515167},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.2515999972820282}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.3390/systems14050535","is_oa":true,"landing_page_url":"https://doi.org/10.3390/systems14050535","pdf_url":"https://www.mdpi.com/2079-8954/14/5/535/pdf?version=1778252037","source":{"id":"https://openalex.org/S4210219410","display_name":"Systems","issn_l":"2079-8954","issn":["2079-8954"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Systems","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:84b24d655e8c4c5f80b91337e7af0588","is_oa":true,"landing_page_url":"https://doaj.org/article/84b24d655e8c4c5f80b91337e7af0588","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Systems, Vol 14, Iss 5, p 535 (2026)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.3390/systems14050535","is_oa":true,"landing_page_url":"https://doi.org/10.3390/systems14050535","pdf_url":"https://www.mdpi.com/2079-8954/14/5/535/pdf?version=1778252037","source":{"id":"https://openalex.org/S4210219410","display_name":"Systems","issn_l":"2079-8954","issn":["2079-8954"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Systems","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11","score":0.43449583649635315}],"awards":[{"id":"https://openalex.org/G2205725795","display_name":null,"funder_award_id":"BK20241326","funder_id":"https://openalex.org/F4320322769","funder_display_name":"Natural Science Foundation of Jiangsu Province"},{"id":"https://openalex.org/G5959218494","display_name":null,"funder_award_id":"2242025F10009","funder_id":"https://openalex.org/F4320335787","funder_display_name":"Fundamental Research Funds for the Central Universities"}],"funders":[{"id":"https://openalex.org/F4320322769","display_name":"Natural Science Foundation of Jiangsu Province","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320324856","display_name":"Southeast University","ror":"https://ror.org/04ct4d772"},{"id":"https://openalex.org/F4320335787","display_name":"Fundamental Research Funds for the Central Universities","ror":null}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7160605440.pdf","grobid_xml":"https://content.openalex.org/works/W7160605440.grobid-xml"},"referenced_works_count":28,"referenced_works":["https://openalex.org/W1985403019","https://openalex.org/W2002102776","https://openalex.org/W2019733672","https://openalex.org/W2111876879","https://openalex.org/W2344057403","https://openalex.org/W2789294806","https://openalex.org/W2992776208","https://openalex.org/W3013886221","https://openalex.org/W3107856286","https://openalex.org/W3117223116","https://openalex.org/W3130143314","https://openalex.org/W3153200540","https://openalex.org/W3163993681","https://openalex.org/W3216996634","https://openalex.org/W4205146220","https://openalex.org/W4220717841","https://openalex.org/W4309899247","https://openalex.org/W4361278404","https://openalex.org/W4362571221","https://openalex.org/W4382045846","https://openalex.org/W4390651083","https://openalex.org/W4392902002","https://openalex.org/W4400647059","https://openalex.org/W4401917226","https://openalex.org/W4403674612","https://openalex.org/W4405241386","https://openalex.org/W4409196544","https://openalex.org/W7135237025"],"related_works":[],"abstract_inverted_index":{"Accurate":[0],"trajectory":[1,45,68,149,157],"data":[2,72],"are":[3,128],"fundamental":[4],"to":[5,21],"traffic":[6,164],"modeling":[7],"and":[8,25,47,83,94,119,130,144,173],"autonomous":[9],"vehicle":[10],"development.":[11],"However,":[12],"reconstructing":[13],"trajectories":[14],"in":[15,151],"cut-in":[16,152],"scenarios":[17],"is":[18],"challenging":[19],"due":[20],"complex":[22],"multi-vehicle":[23],"interactions":[24],"frequently":[26],"sparse,":[27],"noisy":[28],"observations.":[29],"Existing":[30],"model-based":[31],"methods":[32,40],"require":[33],"extensive":[34],"parameter":[35],"tuning,":[36],"while":[37],"purely":[38],"data-driven":[39],"depend":[41],"on":[42,99],"densely":[43],"labeled":[44],"datasets":[46],"may":[48],"violate":[49],"physical":[50],"consistency.":[51],"To":[52],"address":[53],"these":[54],"limitations,":[55],"this":[56],"paper":[57],"proposes":[58],"CI-PINN":[59,86],"(cut-in":[60],"physics-informed":[61],"neural":[62],"network),":[63],"a":[64,76,112,120,142],"self-supervised":[65],"framework":[66,147],"for":[67,148],"reconstruction":[69,150],"under":[70],"severe":[71],"degradation.":[73],"By":[74],"integrating":[75],"longitudinal":[77],"interaction":[78],"model":[79],"that":[80],"captures":[81],"anticipation":[82],"relaxation":[84],"behaviors,":[85],"ensures":[87],"kinematic":[88],"plausibility":[89],"by":[90],"jointly":[91],"minimizing":[92],"data-fitting":[93],"physics":[95],"residual":[96],"losses.":[97],"Experiments":[98],"the":[100,134,159],"NGSIM":[101],"dataset":[102],"demonstrate":[103,141],"robust":[104],"performance":[105],"across":[106],"missing":[107],"rates":[108],"of":[109,116,124],"80\u201390%,":[110],"achieving":[111],"mean":[113,121],"absolute":[114],"error":[115,123],"0.91":[117],"m":[118],"squared":[122],"2.17":[125],"m2,":[126],"which":[127],"63.2%":[129],"78.1%":[131],"lower":[132],"than":[133],"best":[135],"baseline":[136],"method,":[137],"respectively.":[138],"These":[139],"results":[140],"label-efficient":[143],"physically":[145],"consistent":[146],"scenarios.":[153],"Beyond":[154],"improving":[155],"microscopic":[156],"fidelity,":[158],"proposed":[160],"method":[161],"preserves":[162],"system-level":[163],"metrics":[165],"more":[166,169],"reliably,":[167],"facilitating":[168],"accurate":[170],"safety":[171],"assessments":[172],"intelligent":[174],"transportation":[175],"applications.":[176]},"counts_by_year":[],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2026-05-09T00:00:00"}
