{"id":"https://openalex.org/W4417538212","doi":"https://doi.org/10.48550/arxiv.2506.16336","title":"Goal-conditioned Hierarchical Reinforcement Learning for Sample-efficient and Safe Autonomous Driving at Intersections","display_name":"Goal-conditioned Hierarchical Reinforcement Learning for Sample-efficient and Safe Autonomous Driving at Intersections","publication_year":2025,"publication_date":"2025-06-19","ids":{"openalex":"https://openalex.org/W4417538212","doi":"https://doi.org/10.48550/arxiv.2506.16336"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2506.16336","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2506.16336","pdf_url":"https://arxiv.org/pdf/2506.16336","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"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":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2506.16336","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Huang, Yiou","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Huang, Yiou","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"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":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.7752000093460083,"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":0.7752000093460083,"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/T10586","display_name":"Robotic Path Planning Algorithms","score":0.07930000126361847,"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/T10462","display_name":"Reinforcement Learning in Robotics","score":0.06589999794960022,"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/reinforcement-learning","display_name":"Reinforcement learning","score":0.8686000108718872},{"id":"https://openalex.org/keywords/reuse","display_name":"Reuse","score":0.5670999884605408},{"id":"https://openalex.org/keywords/collision-avoidance","display_name":"Collision avoidance","score":0.5504999756813049},{"id":"https://openalex.org/keywords/collision","display_name":"Collision","score":0.48669999837875366},{"id":"https://openalex.org/keywords/reinforcement","display_name":"Reinforcement","score":0.375900000333786},{"id":"https://openalex.org/keywords/control","display_name":"Control (management)","score":0.37119999527931213},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.35269999504089355}],"concepts":[{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.8686000108718872},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6804999709129333},{"id":"https://openalex.org/C206588197","wikidata":"https://www.wikidata.org/wiki/Q846574","display_name":"Reuse","level":2,"score":0.5670999884605408},{"id":"https://openalex.org/C2780864053","wikidata":"https://www.wikidata.org/wiki/Q5147495","display_name":"Collision avoidance","level":3,"score":0.5504999756813049},{"id":"https://openalex.org/C121704057","wikidata":"https://www.wikidata.org/wiki/Q352070","display_name":"Collision","level":2,"score":0.48669999837875366},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.48500001430511475},{"id":"https://openalex.org/C67203356","wikidata":"https://www.wikidata.org/wiki/Q1321905","display_name":"Reinforcement","level":2,"score":0.375900000333786},{"id":"https://openalex.org/C2775924081","wikidata":"https://www.wikidata.org/wiki/Q55608371","display_name":"Control (management)","level":2,"score":0.37119999527931213},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.35269999504089355},{"id":"https://openalex.org/C9180747","wikidata":"https://www.wikidata.org/wiki/Q486893","display_name":"Id, ego and super-ego","level":2,"score":0.3179999887943268},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.3027999997138977},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.29910001158714294},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.29010000824928284},{"id":"https://openalex.org/C2779436431","wikidata":"https://www.wikidata.org/wiki/Q30672407","display_name":"Policy learning","level":2,"score":0.2757999897003174},{"id":"https://openalex.org/C44154836","wikidata":"https://www.wikidata.org/wiki/Q45045","display_name":"Simulation","level":1,"score":0.2587999999523163},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.25850000977516174},{"id":"https://openalex.org/C3017944768","wikidata":"https://www.wikidata.org/wiki/Q1450463","display_name":"Poison control","level":2,"score":0.2578999996185303},{"id":"https://openalex.org/C87833898","wikidata":"https://www.wikidata.org/wiki/Q1060280","display_name":"Advanced driver assistance systems","level":2,"score":0.25519999861717224}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2506.16336","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2506.16336","pdf_url":"https://arxiv.org/pdf/2506.16336","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"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":"text"},{"id":"doi:10.48550/arxiv.2506.16336","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2506.16336","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2506.16336","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2506.16336","pdf_url":"https://arxiv.org/pdf/2506.16336","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"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":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Reinforcement":[0],"learning":[1,34],"(RL)":[2],"exhibits":[3],"remarkable":[4],"potential":[5,57],"in":[6,22],"addressing":[7],"autonomous":[8],"driving":[9],"tasks.":[10],"However,":[11],"it":[12],"is":[13,89],"difficult":[14],"to":[15,55,79,83,115,127,149],"train":[16],"a":[17,30,38],"sample-efficient":[18],"and":[19,121,154],"safe":[20,69],"policy":[21,152],"complex":[23],"scenarios.":[24,108],"In":[25,44,109],"this":[26],"article,":[27],"we":[28],"propose":[29],"novel":[31],"hierarchical":[32,46,94],"reinforcement":[33],"(HRL)":[35],"framework":[36],"with":[37,75],"goal-conditioned":[39],"collision":[40,52],"prediction":[41],"(GCCP)":[42],"module.":[43],"the":[45,48,60,67,76,80,98,111,118,131,134,138,145],"structure,":[47],"GCCP":[49,112],"module":[50],"predicts":[51],"risks":[53],"according":[54,78,126],"different":[56,128],"subgoals":[58,101],"of":[59,100,133],"ego":[61,119,135],"vehicle.":[62],"A":[63,71],"high-level":[64],"decision-maker":[65],"choose":[66],"best":[68],"subgoal.":[70,81],"low-level":[72],"motion-planner":[73],"interacts":[74],"environment":[77],"Compared":[82],"traditional":[84,159],"RL":[85,160],"methods,":[86],"our":[87],"algorithm":[88],"more":[90],"sample-efficient,":[91],"since":[92],"its":[93],"structure":[95],"allows":[96],"reusing":[97],"policies":[99],"across":[102],"similar":[103],"tasks":[104],"for":[105],"various":[106],"navigation":[107],"additional,":[110],"module's":[113],"ability":[114],"predict":[116],"both":[117],"vehicle's":[120],"surrounding":[122],"vehicles'":[123],"future":[124],"actions":[125],"subgoals,":[129],"ensures":[130],"safety":[132,157],"vehicle":[136],"throughout":[137],"decision-making":[139],"process.":[140],"Experimental":[141],"results":[142],"demonstrate":[143],"that":[144],"proposed":[146],"method":[147],"converges":[148],"an":[150],"optimal":[151],"faster":[153],"achieves":[155],"higher":[156],"than":[158],"methods.":[161]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2025-10-10T00:00:00"}
