{"id":"https://openalex.org/W2921116083","doi":"https://doi.org/10.1109/robio.2018.8664869","title":"End-to-End Learning Driver Policy using Moments Deep Neural Network","display_name":"End-to-End Learning Driver Policy using Moments Deep Neural Network","publication_year":2018,"publication_date":"2018-12-01","ids":{"openalex":"https://openalex.org/W2921116083","doi":"https://doi.org/10.1109/robio.2018.8664869","mag":"2921116083"},"language":"en","primary_location":{"id":"doi:10.1109/robio.2018.8664869","is_oa":false,"landing_page_url":"https://doi.org/10.1109/robio.2018.8664869","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE International Conference on Robotics and Biomimetics (ROBIO)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5082402131","display_name":"Deheng Qian","orcid":"https://orcid.org/0000-0001-5754-3143"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Deheng Qian","raw_affiliation_strings":["Meituan-Dianping Group, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Meituan-Dianping Group, Beijing, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5018548685","display_name":"Dongchun Ren","orcid":"https://orcid.org/0000-0002-0909-5419"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dongchun Ren","raw_affiliation_strings":["Meituan-Dianping Group, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Meituan-Dianping Group, Beijing, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101904524","display_name":"Yingying Meng","orcid":"https://orcid.org/0000-0002-8292-7293"},"institutions":[{"id":"https://openalex.org/I21193070","display_name":"Beijing Jiaotong University","ror":"https://ror.org/01yj56c84","country_code":"CN","type":"education","lineage":["https://openalex.org/I21193070"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yingying Meng","raw_affiliation_strings":["Institute of Information Science, Beijing Jiaotong University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of Information Science, Beijing Jiaotong University, Beijing, China","institution_ids":["https://openalex.org/I21193070"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100524375","display_name":"Yanliang Zhu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yanliang Zhu","raw_affiliation_strings":["Meituan-Dianping Group, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Meituan-Dianping Group, Beijing, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5021998285","display_name":"Shuguang Ding","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shuguang Ding","raw_affiliation_strings":["Meituan-Dianping Group, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Meituan-Dianping Group, Beijing, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5108079791","display_name":"Sheng Fu","orcid":"https://orcid.org/0000-0002-2441-3120"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sheng Fu","raw_affiliation_strings":["Meituan-Dianping Group, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Meituan-Dianping Group, Beijing, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5106698700","display_name":"Zhichao Wang","orcid":"https://orcid.org/0000-0001-8075-1784"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhichao Wang","raw_affiliation_strings":["Meituan-Dianping Group, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Meituan-Dianping Group, Beijing, China","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5020190879","display_name":"Huaxia Xia","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Huaxia Xia","raw_affiliation_strings":["Meituan-Dianping Group, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Meituan-Dianping Group, Beijing, China","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.19836929,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"1533","last_page":"1538"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10462","display_name":"Reinforcement Learning in Robotics","score":0.9990000128746033,"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":0.9990000128746033,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9984999895095825,"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/T10586","display_name":"Robotic Path Planning Algorithms","score":0.995199978351593,"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/computer-science","display_name":"Computer science","score":0.7235208749771118},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.6779895424842834},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.657681405544281},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.630779504776001},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.562456488609314},{"id":"https://openalex.org/keywords/dimension","display_name":"Dimension (graph theory)","score":0.5558978915214539},{"id":"https://openalex.org/keywords/action","display_name":"Action (physics)","score":0.5387471914291382},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5359642505645752},{"id":"https://openalex.org/keywords/tensor","display_name":"Tensor (intrinsic definition)","score":0.4481654167175293},{"id":"https://openalex.org/keywords/ideal","display_name":"Ideal (ethics)","score":0.43765121698379517},{"id":"https://openalex.org/keywords/probability-distribution","display_name":"Probability distribution","score":0.43269768357276917},{"id":"https://openalex.org/keywords/distribution","display_name":"Distribution (mathematics)","score":0.41323110461235046},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.368862509727478},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.18362578749656677}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7235208749771118},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.6779895424842834},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.657681405544281},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.630779504776001},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.562456488609314},{"id":"https://openalex.org/C33676613","wikidata":"https://www.wikidata.org/wiki/Q13415176","display_name":"Dimension (graph theory)","level":2,"score":0.5558978915214539},{"id":"https://openalex.org/C2780791683","wikidata":"https://www.wikidata.org/wiki/Q846785","display_name":"Action (physics)","level":2,"score":0.5387471914291382},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5359642505645752},{"id":"https://openalex.org/C155281189","wikidata":"https://www.wikidata.org/wiki/Q3518150","display_name":"Tensor (intrinsic definition)","level":2,"score":0.4481654167175293},{"id":"https://openalex.org/C2776639384","wikidata":"https://www.wikidata.org/wiki/Q840396","display_name":"Ideal (ethics)","level":2,"score":0.43765121698379517},{"id":"https://openalex.org/C149441793","wikidata":"https://www.wikidata.org/wiki/Q200726","display_name":"Probability distribution","level":2,"score":0.43269768357276917},{"id":"https://openalex.org/C110121322","wikidata":"https://www.wikidata.org/wiki/Q865811","display_name":"Distribution (mathematics)","level":2,"score":0.41323110461235046},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.368862509727478},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.18362578749656677},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","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/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/robio.2018.8664869","is_oa":false,"landing_page_url":"https://doi.org/10.1109/robio.2018.8664869","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE International Conference on Robotics and Biomimetics (ROBIO)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":31,"referenced_works":["https://openalex.org/W1478687540","https://openalex.org/W1494902192","https://openalex.org/W1594602740","https://openalex.org/W1622063347","https://openalex.org/W1882451095","https://openalex.org/W1931877416","https://openalex.org/W1971086298","https://openalex.org/W2070029751","https://openalex.org/W2117211893","https://openalex.org/W2121863487","https://openalex.org/W2128990851","https://openalex.org/W2145339207","https://openalex.org/W2342840547","https://openalex.org/W2364095994","https://openalex.org/W2543822225","https://openalex.org/W2559767995","https://openalex.org/W2604382266","https://openalex.org/W2619440467","https://openalex.org/W2962867954","https://openalex.org/W2962894046","https://openalex.org/W2962957031","https://openalex.org/W2963277051","https://openalex.org/W4214717370","https://openalex.org/W4255788608","https://openalex.org/W4295719664","https://openalex.org/W4313387916","https://openalex.org/W6629585730","https://openalex.org/W6635524107","https://openalex.org/W6639366559","https://openalex.org/W6640174482","https://openalex.org/W6718092244"],"related_works":["https://openalex.org/W4293226380","https://openalex.org/W2590472383","https://openalex.org/W2383873009","https://openalex.org/W2393580911","https://openalex.org/W2212184297","https://openalex.org/W3156456913","https://openalex.org/W2390945180","https://openalex.org/W2394052932","https://openalex.org/W2350360626","https://openalex.org/W2964954556"],"abstract_inverted_index":{"A":[0],"common":[1],"practice":[2],"for":[3],"autonomous":[4],"driving":[5,43],"is":[6,85,96,146],"to":[7,11,61,71,75,148],"train":[8],"a":[9,23,58,109],"model":[10,44,95],"mimic":[12,46,62],"expert":[13,34],"actions.":[14,80],"However,":[15],"the":[16,29,33,47,51,63,76,83,105,117,124,133,136,150],"actions":[17,37],"are":[18],"randomly":[19],"drawn":[20],"based":[21],"on":[22,129],"underlying":[24],"policy.":[25,64],"The":[26],"policy":[27],"specifies":[28],"probability":[30,77],"distribution":[31,78,84],"of":[32,79,112,126,132,138],"taking":[35],"different":[36],"under":[38],"each":[39,73],"circumstance.":[40],"An":[41],"ideal":[42],"should":[45],"policy,":[48],"rather":[49],"than":[50],"action.":[52],"In":[53,81],"this":[54],"paper,":[55],"we":[56,115],"propose":[57],"new":[59,110],"method":[60,66],"Our":[65],"employs":[67,108],"deep":[68,152],"neural":[69,153],"network":[70,154],"map":[72],"circumstance":[74],"practice,":[82],"approximately":[86],"represented":[87],"by":[88],"its":[89],"low":[90],"order":[91],"moments.":[92],"Hence,":[93],"our":[94],"called":[97],"Moments":[98],"Deep":[99],"Neural":[100],"Network":[101],"(MDNN).":[102],"To":[103],"improve":[104],"efficiency,":[106],"MDNN":[107,145],"type":[111],"dilated":[113,127],"convolution":[114,128],"term":[116],"Dilated":[118],"Tensor":[119],"Convolution":[120],"(DTC).":[121],"DTC":[122],"applies":[123],"idea":[125],"every":[130],"dimension":[131],"input,":[134],"reducing":[135],"number":[137],"parameters":[139],"in":[140],"MDNN.":[141],"On":[142],"multiple":[143],"experiments,":[144],"demonstrated":[147],"outperform":[149],"classical":[151],"PilotNet":[155],"using":[156],"much":[157],"less":[158],"parameters.":[159]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}