{"id":"https://openalex.org/W3176912151","doi":"https://doi.org/10.1109/tits.2021.3088935","title":"Driving Behavior Modeling Using Naturalistic Human Driving Data With Inverse Reinforcement Learning","display_name":"Driving Behavior Modeling Using Naturalistic Human Driving Data With Inverse Reinforcement Learning","publication_year":2021,"publication_date":"2021-06-18","ids":{"openalex":"https://openalex.org/W3176912151","doi":"https://doi.org/10.1109/tits.2021.3088935","mag":"3176912151"},"language":"en","primary_location":{"id":"doi:10.1109/tits.2021.3088935","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tits.2021.3088935","pdf_url":null,"source":{"id":"https://openalex.org/S144771191","display_name":"IEEE Transactions on Intelligent Transportation Systems","issn_l":"1524-9050","issn":["1524-9050","1558-0016"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Intelligent Transportation Systems","raw_type":"journal-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/A5012295217","display_name":"Zhiyu Huang","orcid":"https://orcid.org/0000-0003-1592-7215"},"institutions":[{"id":"https://openalex.org/I172675005","display_name":"Nanyang Technological University","ror":"https://ror.org/02e7b5302","country_code":"SG","type":"education","lineage":["https://openalex.org/I172675005"]}],"countries":["SG"],"is_corresponding":true,"raw_author_name":"Zhiyu Huang","raw_affiliation_strings":["School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore"],"affiliations":[{"raw_affiliation_string":"School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore","institution_ids":["https://openalex.org/I172675005"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5044215622","display_name":"Jingda Wu","orcid":"https://orcid.org/0000-0002-7336-4492"},"institutions":[{"id":"https://openalex.org/I172675005","display_name":"Nanyang Technological University","ror":"https://ror.org/02e7b5302","country_code":"SG","type":"education","lineage":["https://openalex.org/I172675005"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Jingda Wu","raw_affiliation_strings":["School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore"],"affiliations":[{"raw_affiliation_string":"School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore","institution_ids":["https://openalex.org/I172675005"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5072073374","display_name":"Chen Lv","orcid":"https://orcid.org/0000-0001-6897-4512"},"institutions":[{"id":"https://openalex.org/I172675005","display_name":"Nanyang Technological University","ror":"https://ror.org/02e7b5302","country_code":"SG","type":"education","lineage":["https://openalex.org/I172675005"]}],"countries":["SG"],"is_corresponding":false,"raw_author_name":"Chen Lv","raw_affiliation_strings":["School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore"],"affiliations":[{"raw_affiliation_string":"School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore","institution_ids":["https://openalex.org/I172675005"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5012295217"],"corresponding_institution_ids":["https://openalex.org/I172675005"],"apc_list":null,"apc_paid":null,"fwci":12.6426,"has_fulltext":false,"cited_by_count":198,"citation_normalized_percentile":{"value":0.99306739,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":100},"biblio":{"volume":"23","issue":"8","first_page":"10239","last_page":"10251"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.9997000098228455,"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":0.9997000098228455,"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/T10524","display_name":"Traffic control and management","score":0.9961000084877014,"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/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9945999979972839,"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/reinforcement-learning","display_name":"Reinforcement learning","score":0.6317775845527649},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5260893106460571},{"id":"https://openalex.org/keywords/reinforcement","display_name":"Reinforcement","score":0.43068766593933105},{"id":"https://openalex.org/keywords/inverse","display_name":"Inverse","score":0.4227224588394165},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.32617396116256714},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.2629360854625702},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.0819970965385437},{"id":"https://openalex.org/keywords/structural-engineering","display_name":"Structural engineering","score":0.07667654752731323}],"concepts":[{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.6317775845527649},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5260893106460571},{"id":"https://openalex.org/C67203356","wikidata":"https://www.wikidata.org/wiki/Q1321905","display_name":"Reinforcement","level":2,"score":0.43068766593933105},{"id":"https://openalex.org/C207467116","wikidata":"https://www.wikidata.org/wiki/Q4385666","display_name":"Inverse","level":2,"score":0.4227224588394165},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.32617396116256714},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.2629360854625702},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0819970965385437},{"id":"https://openalex.org/C66938386","wikidata":"https://www.wikidata.org/wiki/Q633538","display_name":"Structural engineering","level":1,"score":0.07667654752731323},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tits.2021.3088935","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tits.2021.3088935","pdf_url":null,"source":{"id":"https://openalex.org/S144771191","display_name":"IEEE Transactions on Intelligent Transportation Systems","issn_l":"1524-9050","issn":["1524-9050","1558-0016"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Intelligent Transportation Systems","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/16","score":0.8199999928474426,"display_name":"Peace, Justice and strong institutions"}],"awards":[{"id":"https://openalex.org/G5555145734","display_name":null,"funder_award_id":"M4082268.050","funder_id":"https://openalex.org/F4320320766","funder_display_name":"Nanyang Technological University"}],"funders":[{"id":"https://openalex.org/F4320320766","display_name":"Nanyang Technological University","ror":"https://ror.org/02e7b5302"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":35,"referenced_works":["https://openalex.org/W1527702126","https://openalex.org/W1573205248","https://openalex.org/W1965455100","https://openalex.org/W2033669280","https://openalex.org/W2056877664","https://openalex.org/W2181849516","https://openalex.org/W2281287867","https://openalex.org/W2565615809","https://openalex.org/W2744369598","https://openalex.org/W2795543364","https://openalex.org/W2801597796","https://openalex.org/W2891385160","https://openalex.org/W2892825772","https://openalex.org/W2942735643","https://openalex.org/W2990048843","https://openalex.org/W2990696475","https://openalex.org/W2996287921","https://openalex.org/W3000638052","https://openalex.org/W3004687390","https://openalex.org/W3012481664","https://openalex.org/W3026104448","https://openalex.org/W3037434189","https://openalex.org/W3037606473","https://openalex.org/W3091165146","https://openalex.org/W3103869897","https://openalex.org/W3104181348","https://openalex.org/W3109323072","https://openalex.org/W3110662215","https://openalex.org/W3113116643","https://openalex.org/W3130972395","https://openalex.org/W3158597572","https://openalex.org/W6638440308","https://openalex.org/W6674884181","https://openalex.org/W6780225596","https://openalex.org/W6787266491"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W4310083477","https://openalex.org/W2328553770","https://openalex.org/W2920061524","https://openalex.org/W1977959518","https://openalex.org/W2038908348","https://openalex.org/W2107890255","https://openalex.org/W2106552856"],"abstract_inverted_index":{"Driving":[0],"behavior":[1,52,64],"modeling":[2,65,214,219,224],"is":[3,31,90,122,133,185,249],"of":[4,166,256,279,291],"great":[5],"importance":[6],"for":[7,140],"designing":[8],"safe,":[9],"smart,":[10],"and":[11,71,99,119,169,235,259,276],"personalized":[12,137,213,280],"autonomous":[13],"driving":[14,23,42,51,58,148,197],"systems.":[15],"In":[16],"this":[17],"paper,":[18],"an":[19],"internal":[20],"reward":[21,36,83,138,157,180,202,285],"function-based":[22],"model":[24,112],"that":[25,53,154,177,251],"emulates":[26],"the":[27,35,62,101,105,117,127,145,155,164,178,195,201,205,209,212,217,223,253,266,273,277,283,289,292],"human\u2019s":[28],"decision-making":[29],"mechanism":[30],"utilized.":[32],"To":[33],"infer":[34],"function":[37,103,203],"parameters":[38],"from":[39,144],"naturalistic":[40],"human":[41,50,142,196,227],"data,":[43],"we":[44],"propose":[45],"a":[46,68,86,189],"structural":[47],"assumption":[48],"about":[49],"focuses":[54],"on":[55,288],"discrete":[56,69],"latent":[57],"intentions.":[59],"It":[60],"converts":[61],"continuous":[63],"problem":[66],"to":[67,81,92,124,135,161,194,232,243],"setting":[70],"thus":[72],"makes":[73],"maximum":[74,106],"entropy":[75,107],"inverse":[76],"reinforcement":[77],"learning":[78],"(IRL)":[79],"tractable":[80],"learn":[82,136],"functions.":[84],"Specifically,":[85],"polynomial":[87],"trajectory":[88],"sampler":[89],"adopted":[91],"generate":[93],"candidate":[94],"trajectories":[95,198],"considering":[96,113],"high-level":[97],"intentions":[98],"approximate":[100],"partition":[102],"in":[104,192,204,226,271],"IRL":[108],"framework.":[109],"An":[110],"environment":[111],"interactive":[114],"behaviors":[115],"among":[116],"ego":[118,267],"surrounding":[120,257],"vehicles":[121,258],"built":[123],"better":[125,240],"estimate":[126],"generated":[128,274],"trajectories.":[129],"The":[130,150,173],"proposed":[131],"method":[132,215],"applied":[134],"functions":[139,158,181,286],"individual":[141],"drivers":[143,168],"NGSIM":[146],"highway":[147],"dataset.":[149],"qualitative":[151],"results":[152,175,241],"demonstrate":[153],"learned":[156,179,284],"are":[159,182,269],"able":[160],"explicitly":[162],"express":[163],"preferences":[165],"different":[167],"interpret":[170],"their":[171,261],"decisions.":[172],"quantitative":[174],"reveal":[176],"robust,":[183],"which":[184],"manifested":[186],"by":[187,265],"only":[188],"marginal":[190],"decline":[191],"proximity":[193],"when":[199],"applying":[200],"testing":[206,210],"conditions.":[207],"For":[208],"performance,":[211],"outperforms":[216],"general":[218],"approach,":[220],"significantly":[221],"reducing":[222],"errors":[225],"likeness":[228],"(a":[229],"custom":[230],"metric":[231],"gauge":[233],"accuracy),":[234],"these":[236],"two":[237],"methods":[238],"deliver":[239],"compared":[242],"other":[244],"baseline":[245],"methods.":[246],"Moreover,":[247],"it":[248],"found":[250],"predicting":[252],"response":[254],"actions":[255],"incorporating":[260],"potential":[262],"decelerations":[263],"caused":[264],"vehicle":[268],"critical":[270],"estimating":[272],"trajectories,":[275],"accuracy":[278,290],"planning":[281],"using":[282],"relies":[287],"forecasting":[293],"model.":[294]},"counts_by_year":[{"year":2026,"cited_by_count":7},{"year":2025,"cited_by_count":55},{"year":2024,"cited_by_count":51},{"year":2023,"cited_by_count":61},{"year":2022,"cited_by_count":20},{"year":2021,"cited_by_count":4}],"updated_date":"2026-04-02T15:55:50.835912","created_date":"2025-10-10T00:00:00"}
