{"id":"https://openalex.org/W3210137037","doi":"https://doi.org/10.48550/arxiv.2110.14700","title":"DDK: A Deep Koopman Approach for Dynamics Modeling and Trajectory Tracking of Autonomous Vehicles","display_name":"DDK: A Deep Koopman Approach for Dynamics Modeling and Trajectory Tracking of Autonomous Vehicles","publication_year":2021,"publication_date":"2021-10-27","ids":{"openalex":"https://openalex.org/W3210137037","doi":"https://doi.org/10.48550/arxiv.2110.14700","mag":"3210137037"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2110.14700","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2110.14700","pdf_url":"https://arxiv.org/pdf/2110.14700","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"volume":null,"type":"preprint","is_traditional":true,"indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2110.14700","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5042371596","display_name":"Yongqian Xiao","orcid":"https://orcid.org/0000-0002-1674-988X","observed_orcids":["https://orcid.org/0000-0002-1674-988X"]},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Xiao, Yongqian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":["https://openalex.org/A5042371596"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":5,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10805","display_name":"Vehicle Dynamics and Control Systems","score":0.8192999958992004,"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/T10805","display_name":"Vehicle Dynamics and Control Systems","score":0.8192999958992004,"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/T11236","display_name":"Control Systems and Identification","score":0.07259999960660934,"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/T11206","display_name":"Model Reduction and Neural Networks","score":0.05739999935030937,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/koopman-operator","display_name":"Koopman operator","score":0.5870000123977661},{"id":"https://openalex.org/keywords/autonomous-vehicles","display_name":"autonomous vehicles","score":0.6740000247955322},{"id":"https://openalex.org/keywords/trajectory-tracking","display_name":"trajectory tracking","score":0.7590000033378601},{"id":"https://openalex.org/keywords/linear-time-invariant-models","display_name":"linear time-invariant models","score":0.8100000023841858},{"id":"https://openalex.org/keywords/koopman-eigenfunctions","display_name":"Koopman eigenfunctions","score":0.8479999899864197},{"id":"https://openalex.org/keywords/model-predictive-control","display_name":"model predictive control","score":0.7879999876022339},{"id":"https://openalex.org/keywords/nonlinear-system-identification","display_name":"nonlinear system identification","score":0.593999981880188},{"id":"https://openalex.org/keywords/electric-suv","display_name":"electric SUV","score":0.6729999780654907},{"id":"https://openalex.org/keywords/carsim","display_name":"CarSim","score":0.7440000176429749}],"concepts":[{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.7214228510856628},{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.6405598521232605},{"id":"https://openalex.org/C47446073","wikidata":"https://www.wikidata.org/wiki/Q5165890","display_name":"Control theory (sociology)","level":3,"score":0.6187549829483032},{"id":"https://openalex.org/C172205157","wikidata":"https://www.wikidata.org/wiki/Q1782962","display_name":"Model predictive control","level":3,"score":0.5901466012001038},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5542548894882202},{"id":"https://openalex.org/C203479927","wikidata":"https://www.wikidata.org/wiki/Q5165939","display_name":"Controller (irrigation)","level":2,"score":0.5416027307510376},{"id":"https://openalex.org/C77405623","wikidata":"https://www.wikidata.org/wiki/Q598451","display_name":"System dynamics","level":2,"score":0.5035149455070496},{"id":"https://openalex.org/C2776053266","wikidata":"https://www.wikidata.org/wiki/Q5037194","display_name":"CarSim","level":3,"score":0.49856019020080566},{"id":"https://openalex.org/C79487989","wikidata":"https://www.wikidata.org/wiki/Q934680","display_name":"Vehicle dynamics","level":2,"score":0.4679758846759796},{"id":"https://openalex.org/C158622935","wikidata":"https://www.wikidata.org/wiki/Q660848","display_name":"Nonlinear system","level":2,"score":0.4600529372692108},{"id":"https://openalex.org/C133731056","wikidata":"https://www.wikidata.org/wiki/Q4917288","display_name":"Control engineering","level":1,"score":0.4304863512516022},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3470922112464905},{"id":"https://openalex.org/C2775924081","wikidata":"https://www.wikidata.org/wiki/Q55608371","display_name":"Control (management)","level":2,"score":0.2370334267616272},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.21682727336883545},{"id":"https://openalex.org/C6557445","wikidata":"https://www.wikidata.org/wiki/Q173113","display_name":"Agronomy","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/C171146098","wikidata":"https://www.wikidata.org/wiki/Q124192","display_name":"Automotive engineering","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","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},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2110.14700","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2110.14700","pdf_url":"https://arxiv.org/pdf/2110.14700","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2110.14700","doi":"https://doi.org/10.48550/arxiv.2110.14700","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2110.14700","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article-journal"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2110.14700","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2110.14700","pdf_url":"https://arxiv.org/pdf/2110.14700","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3210137037.pdf","grobid_xml":"https://content.openalex.org/works/W3210137037.grobid-xml"},"referenced_works_count":36,"referenced_works":["https://openalex.org/W1544016679","https://openalex.org/W1591375184","https://openalex.org/W1723433682","https://openalex.org/W1970411044","https://openalex.org/W1997054581","https://openalex.org/W2014356541","https://openalex.org/W2023122515","https://openalex.org/W2078898317","https://openalex.org/W2363637015","https://openalex.org/W2564386551","https://openalex.org/W2730644873","https://openalex.org/W2734278066","https://openalex.org/W2766298182","https://openalex.org/W2772716190","https://openalex.org/W2777417212","https://openalex.org/W2803275621","https://openalex.org/W2810677299","https://openalex.org/W2903569991","https://openalex.org/W2920383363","https://openalex.org/W2921660888","https://openalex.org/W2923554444","https://openalex.org/W2924607344","https://openalex.org/W2961580777","https://openalex.org/W2981095586","https://openalex.org/W2981761302","https://openalex.org/W3011562592","https://openalex.org/W3028413446","https://openalex.org/W3038280333","https://openalex.org/W3094571353","https://openalex.org/W3099674394","https://openalex.org/W3106846639","https://openalex.org/W3127566536","https://openalex.org/W3130946697","https://openalex.org/W3166908073","https://openalex.org/W3194366730","https://openalex.org/W3204346297"],"related_works":["https://openalex.org/W4383097706","https://openalex.org/W3194366730","https://openalex.org/W3038280333","https://openalex.org/W4400646133","https://openalex.org/W4389666488","https://openalex.org/W4397028598","https://openalex.org/W4393184706","https://openalex.org/W4385566738","https://openalex.org/W4414160945","https://openalex.org/W4406461684"],"abstract_inverted_index":{"Autonomous":[0],"driving":[1,18],"has":[2],"attracted":[3],"lots":[4],"of":[5,27,43,75,116,125,146,162,165,176,214,223],"attention":[6],"in":[7,189],"recent":[8],"years.":[9],"An":[10],"accurate":[11],"vehicle":[12,78,119,185,226],"dynamics":[13,44,186,227],"is":[14,69,83,90,150,180,187,205],"important":[15],"for":[16,45,92,211],"autonomous":[17,46,77,118],"techniques,":[19],"e.g.":[20],"trajectory":[21,28,215],"prediction,":[22],"motion":[23,93],"planning,":[24],"and":[25,41,79,95,104,193,236],"control":[26,199,213],"tracking.":[29,216],"Although":[30],"previous":[31],"works":[32],"have":[33],"made":[34],"some":[35],"results,":[36],"the":[37,57,61,73,76,80,99,101,113,117,126,139,144,147,153,158,174,177,209,212,221,224,233,245],"strong":[38],"nonlinearity,":[39],"precision,":[40],"interpretability":[42],"vehicles":[47],"are":[48,107],"open":[49],"problems":[50],"worth":[51],"being":[52,120],"studied.":[53],"In":[54,98],"this":[55],"paper,":[56],"approach":[58],"based":[59,156],"on":[60,157],"Koopman":[62,67,102,127],"operator":[63],"named":[64],"deep":[65],"direct":[66],"(DDK)":[68],"proposed":[70,154,234],"to":[71,122,207],"identify":[72],"model":[74,82,149,179,197,222,247],"identified":[81,140,178,188,230,246],"a":[84,123,131,169,183,195],"linear":[85,196],"time-invariant":[86],"(LTI)":[87],"version,":[88],"which":[89,160],"convenient":[91],"planning":[94],"controller":[96],"design.":[97],"approach,":[100],"eigenvalues":[103],"system":[105],"matrix":[106],"considered":[108],"as":[109],"trainable":[110],"tensors":[111],"with":[112,168,191,244],"original":[114],"states":[115],"concatenated":[121],"part":[124],"eigenfunctions":[128],"so":[129],"that":[130,220,237],"physically":[132],"interpretable":[133],"subsystem":[134],"can":[135,228,241],"be":[136,229,242],"extracted":[137],"from":[138],"latent":[141],"dynamics.":[142],"Subsequently,":[143],"process":[145],"identification":[148],"trained":[151],"under":[152,248],"method":[155,235],"dataset":[159],"consists":[161],"about":[163],"60km":[164],"data":[166],"collected":[167],"real":[170],"electric":[171],"SUV":[172],"while":[173],"effectiveness":[175],"validated.":[181],"Meanwhile,":[182],"high-fidelity":[184],"CarSim":[190],"DDK,":[192],"then,":[194],"predictive":[198],"(MPC)":[200],"called":[201],"DDK-MPC":[202],"integrating":[203],"DDK":[204],"designed":[206],"validate":[208],"performance":[210,240],"Simulation":[217],"results":[218],"illustrate":[219],"nonlinear":[225],"effectively":[231],"via":[232],"excellent":[238],"tracking":[239],"obtained":[243],"DDK-MPC.":[249]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":4}],"updated_date":"2026-11-07T11:25:07.637180","created_date":"2021-11-08T00:00:00"}
