{"id":"https://openalex.org/W4388878649","doi":"https://doi.org/10.1109/tiv.2023.3335218","title":"Car-Following Behavior Modeling With Maximum Entropy Deep Inverse Reinforcement Learning","display_name":"Car-Following Behavior Modeling With Maximum Entropy Deep Inverse Reinforcement Learning","publication_year":2023,"publication_date":"2023-11-21","ids":{"openalex":"https://openalex.org/W4388878649","doi":"https://doi.org/10.1109/tiv.2023.3335218"},"language":"en","primary_location":{"id":"doi:10.1109/tiv.2023.3335218","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tiv.2023.3335218","pdf_url":null,"source":{"id":"https://openalex.org/S4210199657","display_name":"IEEE Transactions on Intelligent Vehicles","issn_l":"2379-8858","issn":["2379-8858","2379-8904"],"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 Vehicles","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/A5019242097","display_name":"Jiangfeng Nan","orcid":"https://orcid.org/0000-0003-2126-1210"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jiangfeng Nan","raw_affiliation_strings":["School of Transportation Science and Engineering, Beihang University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Transportation Science and Engineering, Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5113724748","display_name":"Weiwen Deng","orcid":"https://orcid.org/0000-0002-3736-9368"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Weiwen Deng","raw_affiliation_strings":["School of Transportation Science and Engineering, Beihang University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Transportation Science and Engineering, Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5043023460","display_name":"Ruzheng Zhang","orcid":"https://orcid.org/0000-0002-0479-2792"},"institutions":[{"id":"https://openalex.org/I4401726824","display_name":"Horizon Robotics (China)","ror":"https://ror.org/05cmv6g68","country_code":null,"type":"company","lineage":["https://openalex.org/I4401726824"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ruzheng Zhang","raw_affiliation_strings":["Horizon Robotics, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Horizon Robotics, Beijing, China","institution_ids":["https://openalex.org/I4401726824"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5007357261","display_name":"Rui Zhao","orcid":"https://orcid.org/0000-0002-9401-9575"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Rui Zhao","raw_affiliation_strings":["School of Transportation Science and Engineering, Beihang University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Transportation Science and Engineering, Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100628921","display_name":"Ying Wang","orcid":"https://orcid.org/0000-0002-1121-4712"},"institutions":[{"id":"https://openalex.org/I194450716","display_name":"Jilin University","ror":"https://ror.org/00js3aw79","country_code":"CN","type":"education","lineage":["https://openalex.org/I194450716"]},{"id":"https://openalex.org/I4210134929","display_name":"Jilin Province Science and Technology Department","ror":"https://ror.org/049x38272","country_code":"CN","type":"government","lineage":["https://openalex.org/I4210134929"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ying Wang","raw_affiliation_strings":["College of Computer Science and Technology, Jilin University, Jilin, China"],"affiliations":[{"raw_affiliation_string":"College of Computer Science and Technology, Jilin University, Jilin, China","institution_ids":["https://openalex.org/I4210134929","https://openalex.org/I194450716"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101847348","display_name":"Juan Ding","orcid":"https://orcid.org/0009-0009-1838-9708"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Juan Ding","raw_affiliation_strings":["PanoSim Technology Company, Limited, Jiaxing City, Zhejiang, China"],"affiliations":[{"raw_affiliation_string":"PanoSim Technology Company, Limited, Jiaxing City, Zhejiang, China","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5019242097"],"corresponding_institution_ids":["https://openalex.org/I82880672"],"apc_list":null,"apc_paid":null,"fwci":1.1485,"has_fulltext":false,"cited_by_count":10,"citation_normalized_percentile":{"value":0.77603893,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":99},"biblio":{"volume":"9","issue":"2","first_page":"3998","last_page":"4010"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.9994999766349792,"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":0.9994999766349792,"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":0.9993000030517578,"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/T10370","display_name":"Traffic and Road Safety","score":0.9923999905586243,"subfield":{"id":"https://openalex.org/subfields/2213","display_name":"Safety, Risk, Reliability and Quality"},"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/computer-science","display_name":"Computer science","score":0.7076661586761475},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.6962897777557373},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6121760606765747},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5419626235961914},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.47332558035850525},{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.46016713976860046},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.45737695693969727},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.4212574362754822}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7076661586761475},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.6962897777557373},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6121760606765747},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5419626235961914},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.47332558035850525},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.46016713976860046},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.45737695693969727},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.4212574362754822},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"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/C1276947","wikidata":"https://www.wikidata.org/wiki/Q333","display_name":"Astronomy","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tiv.2023.3335218","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tiv.2023.3335218","pdf_url":null,"source":{"id":"https://openalex.org/S4210199657","display_name":"IEEE Transactions on Intelligent Vehicles","issn_l":"2379-8858","issn":["2379-8858","2379-8904"],"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 Vehicles","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5600000023841858,"display_name":"Decent work and economic growth","id":"https://metadata.un.org/sdg/8"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":36,"referenced_works":["https://openalex.org/W643528169","https://openalex.org/W1527702126","https://openalex.org/W1573205248","https://openalex.org/W1965455100","https://openalex.org/W2005071617","https://openalex.org/W2047688790","https://openalex.org/W2071969188","https://openalex.org/W2077679034","https://openalex.org/W2097645351","https://openalex.org/W2098955201","https://openalex.org/W2124298315","https://openalex.org/W2268044283","https://openalex.org/W2487175822","https://openalex.org/W2744369598","https://openalex.org/W2755552418","https://openalex.org/W2891385160","https://openalex.org/W2904814783","https://openalex.org/W2963165400","https://openalex.org/W2963787234","https://openalex.org/W3013376041","https://openalex.org/W3037606473","https://openalex.org/W3091165146","https://openalex.org/W3093456443","https://openalex.org/W3107209377","https://openalex.org/W3107293915","https://openalex.org/W3154384271","https://openalex.org/W3176912151","https://openalex.org/W3202432377","https://openalex.org/W4282971481","https://openalex.org/W4285047693","https://openalex.org/W4307413605","https://openalex.org/W6620991601","https://openalex.org/W6674884181","https://openalex.org/W6769297746","https://openalex.org/W6802142037","https://openalex.org/W6846271118"],"related_works":["https://openalex.org/W4306904969","https://openalex.org/W2138720691","https://openalex.org/W4362501864","https://openalex.org/W4323768008","https://openalex.org/W1941703695","https://openalex.org/W4380318855","https://openalex.org/W3084456289","https://openalex.org/W2024136090","https://openalex.org/W3131574667","https://openalex.org/W4391331176"],"abstract_inverted_index":{"Modeling":[0],"driving":[1,162,181,216],"behavior":[2,24,163],"plays":[3],"a":[4,22,125,135],"pivotal":[5],"role":[6],"in":[7,128],"advancing":[8],"the":[9,36,43,76,83,91,107,117,121,146,149,159,165,168,173,187,190,199,205,210,215],"development":[10],"of":[11,17,47,120,161,189],"human-like":[12],"autonomous":[13],"driving.":[14],"In":[15,152],"light":[16],"this,":[18],"this":[19,102,104],"paper":[20,105],"proposes":[21,106],"car-following":[23,206],"modeling":[25,63],"method":[26,201],"with":[27,39,148,172],"sample-based":[28,108,114],"deep":[29,68],"inverse":[30],"reinforcement":[31,79],"learning":[32,80],"(DIRL).":[33],"Due":[34],"to":[35,57,71,81,154,179],"challenges":[37],"associated":[38],"feature":[40],"extraction":[41],"and":[42,93,98,144,164,185,208],"limited":[44],"fitting":[45],"capacity":[46],"linear":[48,55],"functions,":[49,60],"traditional":[50],"IRL,":[51],"which":[52],"employs":[53],"feature-based":[54],"functions":[56],"represent":[58,72],"reward":[59,73,88,166,175,192,212],"exhibits":[61],"low":[62],"accuracy.":[64],"Accordingly,":[65],"DIRL":[66,115,156],"leverages":[67],"neural":[69],"networks":[70],"functions.":[74],"However,":[75],"requirement":[77],"for":[78,86,183],"determine":[82],"optimal":[84],"policy":[85],"DIRL's":[87],"function":[89,123,176,213],"makes":[90],"training":[92,184],"inference":[94],"processes":[95],"computationally":[96],"resource-intensive":[97],"inefficient.":[99],"To":[100],"address":[101],"issue,":[103],"DIRL.":[109],"Through":[110],"solution":[111],"space":[112],"discretization,":[113],"streamlines":[116],"integration":[118],"calculation":[119],"partition":[122],"into":[124],"summation,":[126],"resulting":[127],"improved":[129],"computational":[130],"efficiency.":[131],"Specifically,":[132],"it":[133],"is":[134,177],"three-stage":[136],"framework:":[137],"sampling":[138],"candidate":[139,142],"trajectories,":[140,143],"evaluating":[141],"selecting":[145],"trajectory":[147],"highest":[150],"reward.":[151],"order":[153],"evaluate":[155],"at":[157],"both":[158],"level":[160],"function,":[167],"MPC-based":[169],"virtual":[170],"driver":[171],"explicit":[174],"utilized":[178],"collect":[180],"data":[182],"assessing":[186],"convergence":[188],"learned":[191],"function.":[193],"The":[194],"experimental":[195],"results":[196],"confirm":[197],"that":[198],"proposed":[200],"can":[202],"accurately":[203],"model":[204],"behavior,":[207],"acquire":[209],"driver's":[211],"from":[214],"data.":[217]},"counts_by_year":[{"year":2025,"cited_by_count":7},{"year":2024,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}