{"id":"https://openalex.org/W2978415108","doi":"https://doi.org/10.1109/ijcnn.2019.8852322","title":"End-to-end Learning Method for Self-Driving Cars with Trajectory Recovery Using a Path-following Function","display_name":"End-to-end Learning Method for Self-Driving Cars with Trajectory Recovery Using a Path-following Function","publication_year":2019,"publication_date":"2019-07-01","ids":{"openalex":"https://openalex.org/W2978415108","doi":"https://doi.org/10.1109/ijcnn.2019.8852322","mag":"2978415108"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn.2019.8852322","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2019.8852322","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 International Joint Conference on Neural Networks (IJCNN)","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/A5066842868","display_name":"Tadashi Onishi","orcid":null},"institutions":[{"id":"https://openalex.org/I150744194","display_name":"Waseda University","ror":"https://ror.org/00ntfnx83","country_code":"JP","type":"education","lineage":["https://openalex.org/I150744194"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Tadashi Onishi","raw_affiliation_strings":["Department of Intermedia Art and Science, Waseda University, Tokyo, Japan"],"affiliations":[{"raw_affiliation_string":"Department of Intermedia Art and Science, Waseda University, Tokyo, Japan","institution_ids":["https://openalex.org/I150744194"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5022824326","display_name":"T. Motoyoshi","orcid":null},"institutions":[{"id":"https://openalex.org/I150744194","display_name":"Waseda University","ror":"https://ror.org/00ntfnx83","country_code":"JP","type":"education","lineage":["https://openalex.org/I150744194"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Toshiyuki Motoyoshi","raw_affiliation_strings":["Department of Intermedia Art and Science, Waseda University, Tokyo, Japan"],"affiliations":[{"raw_affiliation_string":"Department of Intermedia Art and Science, Waseda University, Tokyo, Japan","institution_ids":["https://openalex.org/I150744194"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5109529595","display_name":"Yuki Suga","orcid":null},"institutions":[{"id":"https://openalex.org/I150744194","display_name":"Waseda University","ror":"https://ror.org/00ntfnx83","country_code":"JP","type":"education","lineage":["https://openalex.org/I150744194"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Yuki Suga","raw_affiliation_strings":["Department of Intermedia Art and Science, Waseda University, Tokyo, Japan"],"affiliations":[{"raw_affiliation_string":"Department of Intermedia Art and Science, Waseda University, Tokyo, Japan","institution_ids":["https://openalex.org/I150744194"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5058025190","display_name":"Hiroki MORI","orcid":"https://orcid.org/0000-0002-9941-8946"},"institutions":[{"id":"https://openalex.org/I150744194","display_name":"Waseda University","ror":"https://ror.org/00ntfnx83","country_code":"JP","type":"education","lineage":["https://openalex.org/I150744194"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Hiroki Mori","raw_affiliation_strings":["Department of Intermedia Art and Science, Waseda University, Tokyo, Japan"],"affiliations":[{"raw_affiliation_string":"Department of Intermedia Art and Science, Waseda University, Tokyo, Japan","institution_ids":["https://openalex.org/I150744194"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5029210833","display_name":"Tsuya Ogata","orcid":null},"institutions":[{"id":"https://openalex.org/I150744194","display_name":"Waseda University","ror":"https://ror.org/00ntfnx83","country_code":"JP","type":"education","lineage":["https://openalex.org/I150744194"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Tsuya Ogata","raw_affiliation_strings":["Department of Intermedia Art and Science, Waseda University, Tokyo, Japan"],"affiliations":[{"raw_affiliation_string":"Department of Intermedia Art and Science, Waseda University, Tokyo, Japan","institution_ids":["https://openalex.org/I150744194"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5066842868"],"corresponding_institution_ids":["https://openalex.org/I150744194"],"apc_list":null,"apc_paid":null,"fwci":1.0122,"has_fulltext":false,"cited_by_count":25,"citation_normalized_percentile":{"value":0.80964475,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":93,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9987999796867371,"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"}},"topics":[{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9987999796867371,"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/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.9966999888420105,"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/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.9898999929428101,"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/waypoint","display_name":"Waypoint","score":0.8981183767318726},{"id":"https://openalex.org/keywords/end-to-end-principle","display_name":"End-to-end principle","score":0.7213312387466431},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6674072742462158},{"id":"https://openalex.org/keywords/path","display_name":"Path (computing)","score":0.5432968139648438},{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.521439254283905},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.49846982955932617},{"id":"https://openalex.org/keywords/simulation","display_name":"Simulation","score":0.45008817315101624},{"id":"https://openalex.org/keywords/track","display_name":"Track (disk drive)","score":0.43992263078689575},{"id":"https://openalex.org/keywords/brake","display_name":"Brake","score":0.4306914210319519},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4135040044784546},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.38302406668663025},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.2435818314552307},{"id":"https://openalex.org/keywords/automotive-engineering","display_name":"Automotive engineering","score":0.22530558705329895}],"concepts":[{"id":"https://openalex.org/C2781271823","wikidata":"https://www.wikidata.org/wiki/Q138081","display_name":"Waypoint","level":2,"score":0.8981183767318726},{"id":"https://openalex.org/C74296488","wikidata":"https://www.wikidata.org/wiki/Q2527392","display_name":"End-to-end principle","level":2,"score":0.7213312387466431},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6674072742462158},{"id":"https://openalex.org/C2777735758","wikidata":"https://www.wikidata.org/wiki/Q817765","display_name":"Path (computing)","level":2,"score":0.5432968139648438},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.521439254283905},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.49846982955932617},{"id":"https://openalex.org/C44154836","wikidata":"https://www.wikidata.org/wiki/Q45045","display_name":"Simulation","level":1,"score":0.45008817315101624},{"id":"https://openalex.org/C89992363","wikidata":"https://www.wikidata.org/wiki/Q5961558","display_name":"Track (disk drive)","level":2,"score":0.43992263078689575},{"id":"https://openalex.org/C2780999251","wikidata":"https://www.wikidata.org/wiki/Q17022503","display_name":"Brake","level":2,"score":0.4306914210319519},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4135040044784546},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.38302406668663025},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.2435818314552307},{"id":"https://openalex.org/C171146098","wikidata":"https://www.wikidata.org/wiki/Q124192","display_name":"Automotive engineering","level":1,"score":0.22530558705329895},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","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},{"id":"https://openalex.org/C1276947","wikidata":"https://www.wikidata.org/wiki/Q333","display_name":"Astronomy","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn.2019.8852322","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2019.8852322","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 International Joint Conference on Neural Networks (IJCNN)","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":25,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1903029394","https://openalex.org/W2048060899","https://openalex.org/W2088335308","https://openalex.org/W2167224731","https://openalex.org/W2342840547","https://openalex.org/W2559767995","https://openalex.org/W2760878839","https://openalex.org/W2785147712","https://openalex.org/W2837605352","https://openalex.org/W2889987506","https://openalex.org/W2952295876","https://openalex.org/W2962762260","https://openalex.org/W2962867954","https://openalex.org/W2962894046","https://openalex.org/W2964121744","https://openalex.org/W4293861706","https://openalex.org/W4295719664","https://openalex.org/W6631190155","https://openalex.org/W6684338915","https://openalex.org/W6704559304","https://openalex.org/W6730751587","https://openalex.org/W6739575509","https://openalex.org/W6745193792","https://openalex.org/W6745935785"],"related_works":["https://openalex.org/W2688471429","https://openalex.org/W4360939124","https://openalex.org/W2077345734","https://openalex.org/W2899287767","https://openalex.org/W2275928629","https://openalex.org/W4387126713","https://openalex.org/W2612848489","https://openalex.org/W2915444871","https://openalex.org/W2096610529","https://openalex.org/W3138430576"],"abstract_inverted_index":{"We":[0,179],"propose":[1],"an":[2,16],"end-to-end":[3],"learning":[4],"method":[5,124,256,265],"for":[6,26,40,240,245,251,253,284],"autonomous":[7,27],"driving":[8,28,56,100,151,156,201,206],"systems":[9,29],"in":[10,163,213],"this":[11],"article.":[12],"End-to-end":[13,24],"model":[14,25,260],"estimates":[15],"appropriate":[17],"motor":[18],"command":[19],"from":[20,76,228],"raw":[21],"sensory":[22],"signals.":[23],"has":[30],"recently":[31],"been":[32],"based":[33,72,175,261],"on":[34,73,113,176,183,262],"neural":[35,155,205],"networks,":[36],"which":[37],"are":[38],"popular":[39],"their":[41],"good":[42],"recognition":[43],"ability.":[44],"A":[45,259],"common":[46],"problem":[47],"is":[48,125],"how":[49],"to":[50,54,272],"return":[51],"a":[52,77,85,89,104,114,138,154,177],"car":[53,60,110],"the":[55,59,63,74,93,109,118,160,170,184,188,204,210,220,225,229,234,254,263,273,277],"lane":[57],"when":[58],"goes":[61],"off":[62],"track.":[64],"In":[65,130,146,196],"our":[66],"research,":[67],"we":[68,102,133,149,199,223],"collect":[69,134,180],"recovery":[70,94,185],"data":[71,135,161,182,211],"distance":[75,227,236],"desired":[78],"track":[79],"(the":[80],"nearest":[81,230],"waypoint":[82,231],"link)":[83],"during":[84,141,191],"road":[86,274],"test":[87],"with":[88,242,247,257],"simulator.":[90],"To":[91,218],"train":[92],"behavior,":[95],"instead":[96],"of":[97,144,194],"collecting":[98],"human":[99],"data,":[101],"use":[103],"path-following":[105,139,189],"module":[106,140,157,190,207],"(which":[107],"means":[108],"automatically":[111,283],"drives":[112],"pre-decided":[115],"route":[116],"using":[117,137,153,187,203],"car's":[119],"current":[120],"position).":[121],"Our":[122],"proposed":[123,221,255,264],"divided":[126],"into":[127],"three":[128],"phases.":[129],"phase":[131,147,164,197],"1,":[132],"only":[136],"100":[142,192],"laps":[143,193],"driving.":[145,195],"2,":[148],"generate":[150,200],"behavior":[152,186,202],"trained":[158,208,288],"by":[159,209],"collected":[162,212],"1.":[165],"This":[166],"includes":[167],"switching":[168],"between":[169],"accelerator,":[171],"brake":[172],"and":[173,216,233,250,268,279,287,290],"steering":[174],"threshold.":[178],"further":[181],"3,":[198],"phases":[214],"1":[215],"2.":[217],"assess":[219],"method,":[222],"compared":[224],"average":[226,235],"link":[232],"traveled":[237],"per":[238],"lap":[239],"datasets":[241,246,252],"no":[243],"recovery,":[244,249],"random":[248],"recovery.":[258],"drove":[266],"well":[267],"paid":[269],"more":[270],"attention":[271],"rather":[275],"than":[276],"sky":[278],"other":[280],"unrelated":[281],"objects":[282],"both":[285],"untrained":[286],"courses":[289],"weather.":[291]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":5},{"year":2022,"cited_by_count":5},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}