{"id":"https://openalex.org/W4396878088","doi":"https://doi.org/10.1109/access.2024.3400159","title":"An Improved Deep Reinforcement Learning Algorithm for Path Planning in Unmanned Driving","display_name":"An Improved Deep Reinforcement Learning Algorithm for Path Planning in Unmanned Driving","publication_year":2024,"publication_date":"2024-01-01","ids":{"openalex":"https://openalex.org/W4396878088","doi":"https://doi.org/10.1109/access.2024.3400159"},"language":"en","primary_location":{"id":"doi:10.1109/access.2024.3400159","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2024.3400159","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10529241.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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 Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10529241.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101876336","display_name":"Kai Yang","orcid":"https://orcid.org/0000-0001-7139-4703"},"institutions":[{"id":"https://openalex.org/I152031979","display_name":"Nanjing Normal University","ror":"https://ror.org/036trcv74","country_code":"CN","type":"education","lineage":["https://openalex.org/I152031979"]},{"id":"https://openalex.org/I161716053","display_name":"China Pharmaceutical University","ror":"https://ror.org/01sfm2718","country_code":"CN","type":"education","lineage":["https://openalex.org/I161716053"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kai Yang","raw_affiliation_strings":["School of Mathematics and Information Science, Nanjing Normal University of Special Education, Nanjing, China","School of Science, China Pharmaceutical University, Nanjing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Mathematics and Information Science, Nanjing Normal University of Special Education, Nanjing, China","institution_ids":["https://openalex.org/I152031979"]},{"raw_affiliation_string":"School of Science, China Pharmaceutical University, Nanjing, China","institution_ids":["https://openalex.org/I161716053"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5103248711","display_name":"Li Liu","orcid":"https://orcid.org/0009-0007-1106-4068"},"institutions":[{"id":"https://openalex.org/I161716053","display_name":"China Pharmaceutical University","ror":"https://ror.org/01sfm2718","country_code":"CN","type":"education","lineage":["https://openalex.org/I161716053"]},{"id":"https://openalex.org/I20942203","display_name":"Hainan University","ror":"https://ror.org/03q648j11","country_code":"CN","type":"education","lineage":["https://openalex.org/I20942203"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Li Liu","raw_affiliation_strings":["School of Science, Hainan University, Haikou, China","School of Science, China Pharmaceutical University, Nanjing, China"],"raw_orcid":"https://orcid.org/0009-0007-1106-4068","affiliations":[{"raw_affiliation_string":"School of Science, Hainan University, Haikou, China","institution_ids":["https://openalex.org/I20942203"]},{"raw_affiliation_string":"School of Science, China Pharmaceutical University, Nanjing, China","institution_ids":["https://openalex.org/I161716053"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":5.9024,"has_fulltext":false,"cited_by_count":24,"citation_normalized_percentile":{"value":0.9659381,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":100},"biblio":{"volume":"12","issue":null,"first_page":"67935","last_page":"67944"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10462","display_name":"Reinforcement Learning in Robotics","score":0.9994000196456909,"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"}},"topics":[{"id":"https://openalex.org/T10462","display_name":"Reinforcement Learning in Robotics","score":0.9994000196456909,"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"}},{"id":"https://openalex.org/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.9994000196456909,"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":0.9979000091552734,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.8447748422622681},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7429278492927551},{"id":"https://openalex.org/keywords/motion-planning","display_name":"Motion planning","score":0.6730344891548157},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5842887759208679},{"id":"https://openalex.org/keywords/path","display_name":"Path (computing)","score":0.5193578004837036},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5036289095878601},{"id":"https://openalex.org/keywords/automotive-industry","display_name":"Automotive industry","score":0.43332844972610474},{"id":"https://openalex.org/keywords/overfitting","display_name":"Overfitting","score":0.4146854281425476},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.38023853302001953},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.31208309531211853},{"id":"https://openalex.org/keywords/robot","display_name":"Robot","score":0.19694772362709045},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.14959090948104858}],"concepts":[{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.8447748422622681},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7429278492927551},{"id":"https://openalex.org/C81074085","wikidata":"https://www.wikidata.org/wiki/Q366872","display_name":"Motion planning","level":3,"score":0.6730344891548157},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5842887759208679},{"id":"https://openalex.org/C2777735758","wikidata":"https://www.wikidata.org/wiki/Q817765","display_name":"Path (computing)","level":2,"score":0.5193578004837036},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5036289095878601},{"id":"https://openalex.org/C526921623","wikidata":"https://www.wikidata.org/wiki/Q190117","display_name":"Automotive industry","level":2,"score":0.43332844972610474},{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.4146854281425476},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.38023853302001953},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.31208309531211853},{"id":"https://openalex.org/C90509273","wikidata":"https://www.wikidata.org/wiki/Q11012","display_name":"Robot","level":2,"score":0.19694772362709045},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.14959090948104858},{"id":"https://openalex.org/C146978453","wikidata":"https://www.wikidata.org/wiki/Q3798668","display_name":"Aerospace engineering","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2024.3400159","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2024.3400159","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10529241.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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 Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:2ae145ac2d984356ad0d6af84f42a486","is_oa":true,"landing_page_url":"https://doaj.org/article/2ae145ac2d984356ad0d6af84f42a486","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 12, Pp 67935-67944 (2024)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2024.3400159","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2024.3400159","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10529241.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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 Access","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure","score":0.6100000143051147}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W4396878088.pdf"},"referenced_works_count":38,"referenced_works":["https://openalex.org/W1997880753","https://openalex.org/W2007595256","https://openalex.org/W2119717200","https://openalex.org/W2145339207","https://openalex.org/W2192586580","https://openalex.org/W2205698351","https://openalex.org/W2746553466","https://openalex.org/W2793998795","https://openalex.org/W2891816981","https://openalex.org/W2896752436","https://openalex.org/W2914952404","https://openalex.org/W2946497559","https://openalex.org/W2952332632","https://openalex.org/W2963864421","https://openalex.org/W3005620403","https://openalex.org/W3040288082","https://openalex.org/W3088314378","https://openalex.org/W3122230257","https://openalex.org/W3140904135","https://openalex.org/W3168655568","https://openalex.org/W3168738617","https://openalex.org/W3178646029","https://openalex.org/W3197218539","https://openalex.org/W4211228948","https://openalex.org/W4226388845","https://openalex.org/W4285154843","https://openalex.org/W4285591701","https://openalex.org/W4286496598","https://openalex.org/W4298857966","https://openalex.org/W4310552918","https://openalex.org/W4312408383","https://openalex.org/W4312955277","https://openalex.org/W4313555673","https://openalex.org/W4313890081","https://openalex.org/W6637967152","https://openalex.org/W6684290936","https://openalex.org/W6684921986","https://openalex.org/W6730742100"],"related_works":["https://openalex.org/W4362597605","https://openalex.org/W1574414179","https://openalex.org/W2359600231","https://openalex.org/W2380019117","https://openalex.org/W3138952546","https://openalex.org/W1987886368","https://openalex.org/W1660309994","https://openalex.org/W2369187583","https://openalex.org/W3197207153","https://openalex.org/W3154539612"],"abstract_inverted_index":{"In":[0],"the":[1,7,19,28,55,64,68,70,83,90,101,119,128,131,139,144,158,161,175,180],"domain":[2],"of":[3,9,67,92,147,160],"intelligent":[4],"transportation":[5],"systems,":[6],"advent":[8],"autonomous":[10],"driving":[11],"technology":[12],"represents":[13],"a":[14,107],"critical":[15],"milestone,":[16],"profoundly":[17],"shaping":[18],"automotive":[20],"industry\u2019s":[21],"evolutionary":[22],"path.":[23],"This":[24,39,141],"technology\u2019s":[25],"core,":[26],"particularly":[27],"algorithms":[29],"facilitating":[30],"driverless":[31],"path":[32,60,170],"planning,":[33],"has":[34],"attracted":[35],"significant":[36],"scholarly":[37],"interest.":[38],"paper":[40],"presents":[41],"an":[42,113],"advanced":[43],"Deep":[44],"Reinforcement":[45],"Learning":[46],"algorithm":[47,72,88,129],"for":[48,85],"Path":[49],"Planning":[50],"(DRL-PP),":[51],"designed":[52],"to":[53,76,99,106,123,138],"rectify":[54],"shortcomings":[56],"inherent":[57],"in":[58,169,178,191],"existing":[59],"planning":[61],"techniques.":[62],"Considering":[63],"complex":[65],"nature":[66],"environment,":[69],"DRL-PP":[71],"is":[73],"meticulously":[74],"crafted":[75],"ascertain":[77],"optimal":[78,114],"actions,":[79],"thereby":[80,156],"effectively":[81],"reducing":[82],"propensity":[84],"overfitting.":[86],"The":[87],"harnesses":[89],"capabilities":[91],"deep":[93],"reinforcement":[94],"learning,":[95],"utilizing":[96],"neural":[97],"networks":[98],"identify":[100],"most":[102],"advantageous":[103],"action":[104,115,148,162],"corresponding":[105],"specific":[108],"state.":[109],"It":[110],"then":[111],"constructs":[112],"sequence,":[116],"extending":[117],"from":[118],"vehicle\u2019s":[120],"initial":[121],"position":[122],"its":[124],"designated":[125],"target.":[126],"Additionally,":[127],"enhances":[130],"reward":[132,154,181],"function":[133],"by":[134],"incorporating":[135],"data":[136],"pertinent":[137],"objective.":[140],"refinement":[142],"enables":[143],"nuanced":[145],"differentiation":[146],"values":[149],"based":[150],"on":[151],"dynamically":[152],"adjusted":[153],"metrics,":[155],"augmenting":[157],"efficiency":[159],"selection":[163],"process":[164],"and":[165],"yielding":[166],"improved":[167],"results":[168,173],"planning.":[171],"Empirical":[172],"validate":[174],"algorithm\u2019s":[176],"proficiency":[177],"stabilizing":[179],"metric":[182],"while":[183],"minimizing":[184],"exploratory":[185],"steps,":[186],"consistently":[187],"surpassing":[188],"comparative":[189],"models":[190],"path-finding":[192],"effectiveness.":[193]},"counts_by_year":[{"year":2026,"cited_by_count":9},{"year":2025,"cited_by_count":12},{"year":2024,"cited_by_count":3}],"updated_date":"2026-07-15T18:14:33.161393","created_date":"2025-10-10T00:00:00"}