{"id":"https://openalex.org/W2970613039","doi":"https://doi.org/10.1109/ivs.2019.8814125","title":"Hybrid Online POMDP Planning and Deep Reinforcement Learning for Safer Self-Driving Cars","display_name":"Hybrid Online POMDP Planning and Deep Reinforcement Learning for Safer Self-Driving Cars","publication_year":2019,"publication_date":"2019-06-01","ids":{"openalex":"https://openalex.org/W2970613039","doi":"https://doi.org/10.1109/ivs.2019.8814125","mag":"2970613039"},"language":"en","primary_location":{"id":"doi:10.1109/ivs.2019.8814125","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ivs.2019.8814125","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE Intelligent Vehicles Symposium (IV)","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/A5035327344","display_name":"Florian Pusse","orcid":null},"institutions":[{"id":"https://openalex.org/I91712215","display_name":"Saarland University","ror":"https://ror.org/01jdpyv68","country_code":"DE","type":"education","lineage":["https://openalex.org/I91712215"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Florian Pusse","raw_affiliation_strings":["Saarland University, Saarbruecken, 66123, Germany","Computer Science Department, Saarland University, Saarbruecken, Germany"],"affiliations":[{"raw_affiliation_string":"Saarland University, Saarbruecken, 66123, Germany","institution_ids":["https://openalex.org/I91712215"]},{"raw_affiliation_string":"Computer Science Department, Saarland University, Saarbruecken, Germany","institution_ids":["https://openalex.org/I91712215"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5059552632","display_name":"Matthias Klusch","orcid":"https://orcid.org/0009-0009-5431-8640"},"institutions":[{"id":"https://openalex.org/I33256026","display_name":"German Research Centre for Artificial Intelligence","ror":"https://ror.org/01ayc5b57","country_code":"DE","type":"funder","lineage":["https://openalex.org/I33256026"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Matthias Klusch","raw_affiliation_strings":["German Research Center for Artificial Intelligence (DFKI), Saarbruecken, 66123, Germany","German Research Center for Artificial Intelligence (DFKI), Saarbruecken, Germany"],"affiliations":[{"raw_affiliation_string":"German Research Center for Artificial Intelligence (DFKI), Saarbruecken, 66123, Germany","institution_ids":["https://openalex.org/I33256026"]},{"raw_affiliation_string":"German Research Center for Artificial Intelligence (DFKI), Saarbruecken, Germany","institution_ids":["https://openalex.org/I33256026"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5035327344"],"corresponding_institution_ids":["https://openalex.org/I91712215"],"apc_list":null,"apc_paid":null,"fwci":1.8916,"has_fulltext":false,"cited_by_count":30,"citation_normalized_percentile":{"value":0.8641259,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1013","last_page":"1020"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.9998000264167786,"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":0.9998000264167786,"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":0.9962000250816345,"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/T11942","display_name":"Transportation and Mobility Innovations","score":0.9943000078201294,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/partially-observable-markov-decision-process","display_name":"Partially observable Markov decision process","score":0.8937528133392334},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.8918466567993164},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7492375373840332},{"id":"https://openalex.org/keywords/safer","display_name":"SAFER","score":0.6600056886672974},{"id":"https://openalex.org/keywords/markov-decision-process","display_name":"Markov decision process","score":0.5859725475311279},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5497192144393921},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5417860150337219},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5304818749427795},{"id":"https://openalex.org/keywords/collision-avoidance","display_name":"Collision avoidance","score":0.4367407560348511},{"id":"https://openalex.org/keywords/markov-process","display_name":"Markov process","score":0.40739673376083374},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.37359726428985596},{"id":"https://openalex.org/keywords/simulation","display_name":"Simulation","score":0.35403668880462646},{"id":"https://openalex.org/keywords/collision","display_name":"Collision","score":0.3331734538078308},{"id":"https://openalex.org/keywords/markov-chain","display_name":"Markov chain","score":0.3080410361289978},{"id":"https://openalex.org/keywords/markov-model","display_name":"Markov model","score":0.2171388864517212},{"id":"https://openalex.org/keywords/computer-security","display_name":"Computer security","score":0.09068882465362549}],"concepts":[{"id":"https://openalex.org/C17098449","wikidata":"https://www.wikidata.org/wiki/Q176814","display_name":"Partially observable Markov decision process","level":4,"score":0.8937528133392334},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.8918466567993164},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7492375373840332},{"id":"https://openalex.org/C2776654903","wikidata":"https://www.wikidata.org/wiki/Q2601463","display_name":"SAFER","level":2,"score":0.6600056886672974},{"id":"https://openalex.org/C106189395","wikidata":"https://www.wikidata.org/wiki/Q176789","display_name":"Markov decision process","level":3,"score":0.5859725475311279},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5497192144393921},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5417860150337219},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5304818749427795},{"id":"https://openalex.org/C2780864053","wikidata":"https://www.wikidata.org/wiki/Q5147495","display_name":"Collision avoidance","level":3,"score":0.4367407560348511},{"id":"https://openalex.org/C159886148","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov process","level":2,"score":0.40739673376083374},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.37359726428985596},{"id":"https://openalex.org/C44154836","wikidata":"https://www.wikidata.org/wiki/Q45045","display_name":"Simulation","level":1,"score":0.35403668880462646},{"id":"https://openalex.org/C121704057","wikidata":"https://www.wikidata.org/wiki/Q352070","display_name":"Collision","level":2,"score":0.3331734538078308},{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.3080410361289978},{"id":"https://openalex.org/C163836022","wikidata":"https://www.wikidata.org/wiki/Q6771326","display_name":"Markov model","level":3,"score":0.2171388864517212},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.09068882465362549},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","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/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ivs.2019.8814125","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ivs.2019.8814125","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE Intelligent Vehicles Symposium (IV)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/3","score":0.5799999833106995,"display_name":"Good health and well-being"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":38,"referenced_works":["https://openalex.org/W123090642","https://openalex.org/W155097506","https://openalex.org/W1502485165","https://openalex.org/W1563965851","https://openalex.org/W1592601589","https://openalex.org/W1605929701","https://openalex.org/W2055501135","https://openalex.org/W2060135607","https://openalex.org/W2099430963","https://openalex.org/W2136848157","https://openalex.org/W2154844948","https://openalex.org/W2567015638","https://openalex.org/W2588289902","https://openalex.org/W2604216058","https://openalex.org/W2607713949","https://openalex.org/W2616635592","https://openalex.org/W2766207971","https://openalex.org/W2774386693","https://openalex.org/W2783963507","https://openalex.org/W2786658125","https://openalex.org/W2808915486","https://openalex.org/W2950872548","https://openalex.org/W2952258289","https://openalex.org/W2962887844","https://openalex.org/W2963809389","https://openalex.org/W2963948945","https://openalex.org/W2964043796","https://openalex.org/W2964104904","https://openalex.org/W3037651954","https://openalex.org/W3098184036","https://openalex.org/W3212743282","https://openalex.org/W4293583840","https://openalex.org/W4297795161","https://openalex.org/W6604974139","https://openalex.org/W6633696232","https://openalex.org/W6692846177","https://openalex.org/W6745899410","https://openalex.org/W6752521830"],"related_works":["https://openalex.org/W2096013579","https://openalex.org/W1760611253","https://openalex.org/W52153049","https://openalex.org/W1589140671","https://openalex.org/W2951545791","https://openalex.org/W1515117609","https://openalex.org/W2294884454","https://openalex.org/W4323315247","https://openalex.org/W3169161914","https://openalex.org/W4321379664"],"abstract_inverted_index":{"The":[0],"problem":[1],"of":[2,6,41,66,81,104,108,136],"pedestrian":[3],"collision-free":[4,64],"navigation":[5,65],"self-driving":[7,67],"cars":[8,68],"modeled":[9],"as":[10],"a":[11,71],"partially":[12],"observable":[13],"Markov":[14],"decision":[15],"process":[16],"can":[17,101],"be":[18,48,131],"solved":[19],"with":[20,70],"either":[21],"deep":[22,117],"reinforcement":[23,118],"learning":[24,119],"or":[25],"approximate":[26,113],"POMDP":[27,114],"planning.":[28],"However,":[29],"it":[30],"is":[31],"not":[32],"known":[33],"whether":[34],"some":[35],"hybrid":[36,60],"approach":[37],"that":[38,99],"combines":[39],"advantages":[40],"these":[42],"fundamentally":[43],"different":[44],"solution":[45,61],"categories":[46],"could":[47],"superior":[49],"to":[50,130,140],"them":[51],"in":[52,120],"this":[53],"context.":[54],"This":[55],"paper":[56],"presents":[57],"the":[58,77,88,109],"first":[59,78],"HyLEAP":[62,100],"for":[63,112],"together":[69],"comparative":[72],"experimental":[73],"performance":[74],"evaluation":[75],"over":[76],"benchmark":[79],"OpenDS-CTS":[80],"simulated":[82],"car-pedestrian":[83],"accident":[84,93,123],"scenarios":[85,124],"based":[86],"on":[87,142],"major":[89],"German":[90],"in-depth":[91],"road":[92],"study":[94],"GIDAS.":[95],"Our":[96],"experiments":[97],"revealed":[98],"outperform":[102],"each":[103],"its":[105],"integrated":[106],"state":[107],"art":[110],"methods":[111],"planning":[115],"and":[116,138],"most":[121],"GIDAS":[122],"regarding":[125,134],"safety,":[126],"while":[127],"they":[128],"appear":[129],"equally":[132],"competitive":[133],"smoothness":[135],"driving":[137],"time":[139],"goal":[141],"average.":[143]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":8},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":7},{"year":2020,"cited_by_count":5}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
