{"id":"https://openalex.org/W3089385868","doi":"https://doi.org/10.1109/icra40945.2020.9197027","title":"Imitative Reinforcement Learning Fusing Vision and Pure Pursuit for Self-driving","display_name":"Imitative Reinforcement Learning Fusing Vision and Pure Pursuit for Self-driving","publication_year":2020,"publication_date":"2020-05-01","ids":{"openalex":"https://openalex.org/W3089385868","doi":"https://doi.org/10.1109/icra40945.2020.9197027","mag":"3089385868"},"language":"en","primary_location":{"id":"doi:10.1109/icra40945.2020.9197027","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icra40945.2020.9197027","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Conference on Robotics and Automation (ICRA)","raw_type":"proceedings-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/A5011352502","display_name":"Mingxing Peng","orcid":"https://orcid.org/0000-0002-7741-1137"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Mingxing Peng","raw_affiliation_strings":["School of Data and Computer Science, Sun Yat-sen University, China"],"affiliations":[{"raw_affiliation_string":"School of Data and Computer Science, Sun Yat-sen University, China","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5055008059","display_name":"Zhihao Gong","orcid":"https://orcid.org/0000-0002-3116-1996"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhihao Gong","raw_affiliation_strings":["School of Data and Computer Science, Sun Yat-sen University, China"],"affiliations":[{"raw_affiliation_string":"School of Data and Computer Science, Sun Yat-sen University, China","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100722234","display_name":"Chen Sun","orcid":"https://orcid.org/0000-0001-8772-9627"},"institutions":[{"id":"https://openalex.org/I151746483","display_name":"University of Waterloo","ror":"https://ror.org/01aff2v68","country_code":"CA","type":"education","lineage":["https://openalex.org/I151746483"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Chen Sun","raw_affiliation_strings":["Waterloo CogDrive Lab, University of Waterloo, Canada"],"affiliations":[{"raw_affiliation_string":"Waterloo CogDrive Lab, University of Waterloo, Canada","institution_ids":["https://openalex.org/I151746483"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100336450","display_name":"Long Chen","orcid":"https://orcid.org/0000-0003-4925-0572"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Long Chen","raw_affiliation_strings":["School of Data and Computer Science, Sun Yat-sen University, China"],"affiliations":[{"raw_affiliation_string":"School of Data and Computer Science, Sun Yat-sen University, China","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5031991761","display_name":"Dongpu Cao","orcid":"https://orcid.org/0000-0001-7929-4336"},"institutions":[{"id":"https://openalex.org/I151746483","display_name":"University of Waterloo","ror":"https://ror.org/01aff2v68","country_code":"CA","type":"education","lineage":["https://openalex.org/I151746483"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Dongpu Cao","raw_affiliation_strings":["Waterloo CogDrive Lab, University of Waterloo, Canada"],"affiliations":[{"raw_affiliation_string":"Waterloo CogDrive Lab, University of Waterloo, Canada","institution_ids":["https://openalex.org/I151746483"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5011352502"],"corresponding_institution_ids":["https://openalex.org/I157773358"],"apc_list":null,"apc_paid":null,"fwci":0.5081,"has_fulltext":false,"cited_by_count":10,"citation_normalized_percentile":{"value":0.67399827,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"3298","last_page":"3304"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.9988999962806702,"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":0.9988999962806702,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9980999827384949,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9973000288009644,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/reinforcement-learning","display_name":"Reinforcement learning","score":0.8984571695327759},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7253553867340088},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.6775286197662354},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6377821564674377},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6264732480049133},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.6142975091934204},{"id":"https://openalex.org/keywords/imitation","display_name":"Imitation","score":0.6071505546569824},{"id":"https://openalex.org/keywords/dependency","display_name":"Dependency (UML)","score":0.5366033911705017},{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.4761119782924652},{"id":"https://openalex.org/keywords/reinforcement","display_name":"Reinforcement","score":0.465806782245636},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.37740105390548706},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.35530227422714233},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.13259273767471313},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.10702180862426758}],"concepts":[{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.8984571695327759},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7253553867340088},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.6775286197662354},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6377821564674377},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6264732480049133},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.6142975091934204},{"id":"https://openalex.org/C126388530","wikidata":"https://www.wikidata.org/wiki/Q1131737","display_name":"Imitation","level":2,"score":0.6071505546569824},{"id":"https://openalex.org/C19768560","wikidata":"https://www.wikidata.org/wiki/Q320727","display_name":"Dependency (UML)","level":2,"score":0.5366033911705017},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.4761119782924652},{"id":"https://openalex.org/C67203356","wikidata":"https://www.wikidata.org/wiki/Q1321905","display_name":"Reinforcement","level":2,"score":0.465806782245636},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.37740105390548706},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.35530227422714233},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.13259273767471313},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.10702180862426758},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C77805123","wikidata":"https://www.wikidata.org/wiki/Q161272","display_name":"Social psychology","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","level":1,"score":0.0},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","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/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","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/C66938386","wikidata":"https://www.wikidata.org/wiki/Q633538","display_name":"Structural engineering","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/icra40945.2020.9197027","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icra40945.2020.9197027","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Conference on Robotics and Automation (ICRA)","raw_type":"proceedings-article"},{"id":"pmh:oai:hub.hku.hk:10722/352999","is_oa":false,"landing_page_url":"https://hub.hku.hk/handle/10722/352999","pdf_url":null,"source":{"id":"https://openalex.org/S4377196271","display_name":"The HKU Scholars Hub (University of Hong Kong)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I889458895","host_organization_name":"University of Hong Kong","host_organization_lineage":["https://openalex.org/I889458895"],"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":"Conference_Paper"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","score":0.8299999833106995,"id":"https://metadata.un.org/sdg/11"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":36,"referenced_works":["https://openalex.org/W2051059753","https://openalex.org/W2173248099","https://openalex.org/W2342840547","https://openalex.org/W2559767995","https://openalex.org/W2741122588","https://openalex.org/W2761595090","https://openalex.org/W2772709170","https://openalex.org/W2788862220","https://openalex.org/W2796290181","https://openalex.org/W2837605352","https://openalex.org/W2903825112","https://openalex.org/W2938321354","https://openalex.org/W2950955091","https://openalex.org/W2961540362","https://openalex.org/W2962867954","https://openalex.org/W2962887844","https://openalex.org/W2962894046","https://openalex.org/W2963327228","https://openalex.org/W2963376229","https://openalex.org/W2963864421","https://openalex.org/W2964319688","https://openalex.org/W2968983352","https://openalex.org/W2990831234","https://openalex.org/W3009593063","https://openalex.org/W3104515094","https://openalex.org/W4288281368","https://openalex.org/W4293580146","https://openalex.org/W4295719664","https://openalex.org/W6684921986","https://openalex.org/W6704559304","https://openalex.org/W6744454805","https://openalex.org/W6745935785","https://openalex.org/W6746177919","https://openalex.org/W6752818208","https://openalex.org/W6766313662","https://openalex.org/W6770576168"],"related_works":["https://openalex.org/W4387497383","https://openalex.org/W3183948672","https://openalex.org/W3173606202","https://openalex.org/W3110381201","https://openalex.org/W2948807893","https://openalex.org/W2935909890","https://openalex.org/W2778153218","https://openalex.org/W2758277628","https://openalex.org/W1531601525","https://openalex.org/W2920061524"],"abstract_inverted_index":{"Autonomous":[0],"urban":[1],"driving":[2,100,181],"navigation":[3,202],"is":[4,96,187],"still":[5],"an":[6,39],"open":[7],"problem":[8,145],"and":[9,19,56,142,166,193],"has":[10],"ample":[11],"room":[12],"for":[13,62],"improvement":[14],"in":[15,155,175,198],"unknown":[16,199],"complex":[17],"environments":[18,200],"terrible":[20],"weather":[21,90,191],"conditions.":[22,91],"In":[23,65,127],"this":[24,128],"paper,":[25],"we":[26,104,159],"propose":[27],"a":[28,106,161],"two-stage":[29],"framework,":[30],"called":[31],"IPP-RL,":[32],"to":[33,171,189],"handle":[34],"these":[35],"problems.":[36],"IPP":[37,67,125],"means":[38,58],"Imitation":[40],"learning":[41,94,108,138],"method":[42],"fusing":[43],"visual":[44,70],"information":[45,71],"with":[46],"the":[47,69,79,99,114,120,134,144,168,173],"additional":[48],"steering":[49,81],"angle":[50],"calculated":[51,80],"by":[52,73,78,98,151],"Pure-Pursuit":[53],"(PP)":[54],"method,":[55],"RL":[57],"using":[59],"Reinforcement":[60],"Learning":[61],"further":[63],"training.":[64],"our":[66,130,185],"model,":[68],"captured":[72],"camera":[74],"can":[75,132],"be":[76],"compensated":[77],"angle,":[82],"thus":[83],"it":[84],"could":[85],"perform":[86],"well":[87],"under":[88],"bad":[89],"However,":[92],"imitation":[93,137],"performance":[95],"limited":[97],"data":[101,141],"severely.":[102],"Thus":[103],"use":[105,167],"reinforcement":[107,156],"method-Deep":[109],"Deterministic":[110],"Policy":[111],"Gradient":[112],"(DDPG)-in":[113],"second":[115],"stage":[116],"training,":[117],"which":[118],"shares":[119],"learned":[121],"weights":[122,154],"from":[123],"pretrained":[124],"model.":[126],"way,":[129],"IPP-RL":[131,186],"lower":[133],"dependency":[135],"of":[136,146],"on":[139,179,201],"demonstration":[140],"solve":[143],"low":[147],"exploration":[148],"efficiency":[149],"caused":[150],"randomly":[152],"initialized":[153],"learning.":[157],"Moreover,":[158],"design":[160],"more":[162],"reasonable":[163],"reward":[164],"function":[165],"n-step":[169],"return":[170],"update":[172],"critic-network":[174],"DDPG.":[176],"Our":[177],"experiments":[178],"CARLA":[180],"benchmark":[182],"demonstrate":[183],"that":[184],"robust":[188],"lousy":[190],"conditions":[192],"shows":[194],"remarkable":[195],"generalization":[196],"capability":[197],"task.":[203]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":2}],"updated_date":"2026-03-27T14:29:43.386196","created_date":"2025-10-10T00:00:00"}
