{"id":"https://openalex.org/W4408696324","doi":"https://doi.org/10.1109/itsc58415.2024.10920206","title":"Learning Residual Model of Model Predictive Control via Random Forests for Autonomous Driving","display_name":"Learning Residual Model of Model Predictive Control via Random Forests for Autonomous Driving","publication_year":2024,"publication_date":"2024-09-24","ids":{"openalex":"https://openalex.org/W4408696324","doi":"https://doi.org/10.1109/itsc58415.2024.10920206"},"language":"en","primary_location":{"id":"doi:10.1109/itsc58415.2024.10920206","is_oa":false,"landing_page_url":"https://doi.org/10.1109/itsc58415.2024.10920206","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC)","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/A5009260231","display_name":"Kang Zhao","orcid":"https://orcid.org/0000-0001-8686-3736"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kang Zhao","raw_affiliation_strings":["Institute of Artificial Intelligence and Robotics, College of Artificial Intelligence, Xi&#x0027;an Jiaotong University,National Key Laboratory of Human-Machine Hybrid Augmented Intelligence,Xi&#x0027;an,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of Artificial Intelligence and Robotics, College of Artificial Intelligence, Xi&#x0027;an Jiaotong University,National Key Laboratory of Human-Machine Hybrid Augmented Intelligence,Xi&#x0027;an,China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5031934171","display_name":"Jianru Xue","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jianru Xue","raw_affiliation_strings":["Institute of Artificial Intelligence and Robotics, College of Artificial Intelligence, Xi&#x0027;an Jiaotong University,National Key Laboratory of Human-Machine Hybrid Augmented Intelligence,Xi&#x0027;an,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of Artificial Intelligence and Robotics, College of Artificial Intelligence, Xi&#x0027;an Jiaotong University,National Key Laboratory of Human-Machine Hybrid Augmented Intelligence,Xi&#x0027;an,China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5035119263","display_name":"Xiangning Meng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xiangning Meng","raw_affiliation_strings":["Institute of Artificial Intelligence and Robotics, College of Artificial Intelligence, Xi&#x0027;an Jiaotong University,National Key Laboratory of Human-Machine Hybrid Augmented Intelligence,Xi&#x0027;an,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of Artificial Intelligence and Robotics, College of Artificial Intelligence, Xi&#x0027;an Jiaotong University,National Key Laboratory of Human-Machine Hybrid Augmented Intelligence,Xi&#x0027;an,China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5087634807","display_name":"Gengxin Li","orcid":"https://orcid.org/0000-0001-9778-0450"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gengxin Li","raw_affiliation_strings":["Institute of Artificial Intelligence and Robotics, College of Artificial Intelligence, Xi&#x0027;an Jiaotong University,National Key Laboratory of Human-Machine Hybrid Augmented Intelligence,Xi&#x0027;an,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of Artificial Intelligence and Robotics, College of Artificial Intelligence, Xi&#x0027;an Jiaotong University,National Key Laboratory of Human-Machine Hybrid Augmented Intelligence,Xi&#x0027;an,China","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5036591546","display_name":"Mengsen Wu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mengsen Wu","raw_affiliation_strings":["Institute of Artificial Intelligence and Robotics, College of Artificial Intelligence, Xi&#x0027;an Jiaotong University,National Key Laboratory of Human-Machine Hybrid Augmented Intelligence,Xi&#x0027;an,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of Artificial Intelligence and Robotics, College of Artificial Intelligence, Xi&#x0027;an Jiaotong University,National Key Laboratory of Human-Machine Hybrid Augmented Intelligence,Xi&#x0027;an,China","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"755","last_page":"761"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10791","display_name":"Advanced Control Systems Optimization","score":0.9837999939918518,"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/T10791","display_name":"Advanced Control Systems Optimization","score":0.9837999939918518,"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/T10876","display_name":"Fault Detection and Control Systems","score":0.9452999830245972,"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/T10820","display_name":"Fuzzy Logic and Control Systems","score":0.9059000015258789,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.7305753231048584},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.707033634185791},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5402382016181946},{"id":"https://openalex.org/keywords/model-predictive-control","display_name":"Model predictive control","score":0.5401074290275574},{"id":"https://openalex.org/keywords/control","display_name":"Control (management)","score":0.45527005195617676},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3776969015598297},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.33839818835258484},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.1022581160068512}],"concepts":[{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.7305753231048584},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.707033634185791},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5402382016181946},{"id":"https://openalex.org/C172205157","wikidata":"https://www.wikidata.org/wiki/Q1782962","display_name":"Model predictive control","level":3,"score":0.5401074290275574},{"id":"https://openalex.org/C2775924081","wikidata":"https://www.wikidata.org/wiki/Q55608371","display_name":"Control (management)","level":2,"score":0.45527005195617676},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3776969015598297},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.33839818835258484},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.1022581160068512}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/itsc58415.2024.10920206","is_oa":false,"landing_page_url":"https://doi.org/10.1109/itsc58415.2024.10920206","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.6399999856948853,"id":"https://metadata.un.org/sdg/13","display_name":"Climate action"}],"awards":[{"id":"https://openalex.org/G8144621808","display_name":null,"funder_award_id":"62036008,62273057","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W1571870753","https://openalex.org/W1591375184","https://openalex.org/W2013439434","https://openalex.org/W2170912685","https://openalex.org/W2396317032","https://openalex.org/W2560978298","https://openalex.org/W2753956499","https://openalex.org/W2769646558","https://openalex.org/W2801749227","https://openalex.org/W2803275621","https://openalex.org/W2954115742","https://openalex.org/W2963945659","https://openalex.org/W2968428478","https://openalex.org/W2992833799","https://openalex.org/W3000508506","https://openalex.org/W3155272911","https://openalex.org/W4292510839","https://openalex.org/W4297957988"],"related_works":["https://openalex.org/W1990079087","https://openalex.org/W3193043704","https://openalex.org/W4386259002","https://openalex.org/W1546989560","https://openalex.org/W3171520305","https://openalex.org/W3202234113","https://openalex.org/W1924178503","https://openalex.org/W3135126032","https://openalex.org/W4394984040","https://openalex.org/W2556120871"],"abstract_inverted_index":{"One":[0],"major":[1],"issue":[2],"in":[3],"learning-based":[4,138],"model":[5,29,67,77,111,123,129,182],"predictive":[6,183],"control":[7,55,184],"(MPC)":[8],"for":[9],"autonomous":[10],"driving":[11,160],"is":[12,124],"the":[13,16,31,46,90,93,107,116,127,132,137,159,172,177],"contradiction":[14],"between":[15],"system":[17,28,133],"model's":[18],"prediction":[19,162],"accuracy":[20,163,166,193],"and":[21,40,52,72,92,146,164],"computation":[22,198],"efficiency.":[23],"The":[24,75,82,119],"more":[25,32],"situations":[26],"a":[27,63,141,196],"covers,":[30],"complex":[33],"it":[34],"is,":[35],"along":[36],"with":[37,112,171,176],"highly":[38],"nonlinear":[39,181],"nonconvex":[41],"properties.":[42],"These":[43],"issues":[44],"make":[45],"optimization":[47,147],"too":[48],"complicated":[49],"to":[50,88,99,126,130],"solve":[51,151],"render":[53],"real-time":[54],"impractical.":[56],"To":[57],"address":[58],"these":[59],"issues,":[60],"we":[61,105],"propose":[62],"hierarchical":[64],"learning":[65],"residual":[66],"which":[68],"leverages":[69],"random":[70,97],"forests":[71,98],"linear":[73,86,102,108,121],"regression.":[74],"learned":[76],"consists":[78],"of":[79],"two":[80],"levels.":[81],"low":[83],"level":[84,95],"uses":[85,96],"regression":[87,122],"fit":[89],"residues,":[91],"high":[94],"switch":[100],"different":[101],"models.":[103],"Meanwhile,":[104],"adopt":[106],"dynamic":[109],"bicycle":[110],"error":[113],"states":[114],"as":[115,140],"nominal":[117,128,173],"model.":[118,134],"switched":[120],"added":[125],"form":[131],"It":[135],"reformulates":[136],"MPC":[139],"quadratic":[142],"program":[143],"(QP)":[144],"problem,":[145],"solvers":[148],"can":[149],"effectively":[150],"it.":[152],"Experimental":[153],"path":[154],"tracking":[155,165,192],"results":[156],"show":[157],"that":[158],"vehicle's":[161],"are":[167],"significantly":[168],"improved":[169],"compared":[170],"MPC.":[174],"Compared":[175],"state-of-the-art":[178],"Gaussian":[179],"process-based":[180],"(GP-NMPC),":[185],"our":[186],"method":[187],"gets":[188],"better":[189],"performance":[190],"on":[191],"while":[194],"maintaining":[195],"lower":[197],"consumption.":[199]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
