{"id":"https://openalex.org/W2887175977","doi":"https://doi.org/10.1109/icufn.2018.8436950","title":"Learning Through Adverse Event for Collision Avoidance: A Self-Learning Approach","display_name":"Learning Through Adverse Event for Collision Avoidance: A Self-Learning Approach","publication_year":2018,"publication_date":"2018-07-01","ids":{"openalex":"https://openalex.org/W2887175977","doi":"https://doi.org/10.1109/icufn.2018.8436950","mag":"2887175977"},"language":"en","primary_location":{"id":"doi:10.1109/icufn.2018.8436950","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icufn.2018.8436950","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 Tenth International Conference on Ubiquitous and Future Networks (ICUFN)","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/A5108273880","display_name":"Hyun-Jun Han","orcid":null},"institutions":[{"id":"https://openalex.org/I39534123","display_name":"Gwangju Institute of Science and Technology","ror":"https://ror.org/024kbgz78","country_code":"KR","type":"education","lineage":["https://openalex.org/I39534123"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Hyunjun Han","raw_affiliation_strings":["Department of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology (GIST), Gwangju, Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology (GIST), Gwangju, Korea","institution_ids":["https://openalex.org/I39534123"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5065256405","display_name":"Ju-Sung Kang","orcid":"https://orcid.org/0000-0002-7908-9452"},"institutions":[{"id":"https://openalex.org/I39534123","display_name":"Gwangju Institute of Science and Technology","ror":"https://ror.org/024kbgz78","country_code":"KR","type":"education","lineage":["https://openalex.org/I39534123"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Jusung Kang","raw_affiliation_strings":["Department of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology (GIST), Gwangju, Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology (GIST), Gwangju, Korea","institution_ids":["https://openalex.org/I39534123"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5113963620","display_name":"Muhammad Asif Raza","orcid":"https://orcid.org/0009-0003-9060-6056"},"institutions":[{"id":"https://openalex.org/I39534123","display_name":"Gwangju Institute of Science and Technology","ror":"https://ror.org/024kbgz78","country_code":"KR","type":"education","lineage":["https://openalex.org/I39534123"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Muhammad Asif Raza","raw_affiliation_strings":["Department of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology (GIST), Gwangju, Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology (GIST), Gwangju, Korea","institution_ids":["https://openalex.org/I39534123"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5034347136","display_name":"Heung-No Lee","orcid":"https://orcid.org/0000-0001-8528-5778"},"institutions":[{"id":"https://openalex.org/I39534123","display_name":"Gwangju Institute of Science and Technology","ror":"https://ror.org/024kbgz78","country_code":"KR","type":"education","lineage":["https://openalex.org/I39534123"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Heung-No Lee","raw_affiliation_strings":["Department of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology (GIST), Gwangju, Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology (GIST), Gwangju, Korea","institution_ids":["https://openalex.org/I39534123"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.08828855,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"874","last_page":"877"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9972000122070312,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9972000122070312,"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/T10462","display_name":"Reinforcement Learning in Robotics","score":0.9955999851226807,"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":0.9952999949455261,"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":0.7480944395065308},{"id":"https://openalex.org/keywords/collision","display_name":"Collision","score":0.7267696261405945},{"id":"https://openalex.org/keywords/collision-avoidance","display_name":"Collision avoidance","score":0.6731156706809998},{"id":"https://openalex.org/keywords/event","display_name":"Event (particle physics)","score":0.6623414754867554},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5824495553970337},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5364516377449036},{"id":"https://openalex.org/keywords/semi-supervised-learning","display_name":"Semi-supervised learning","score":0.45994871854782104},{"id":"https://openalex.org/keywords/online-machine-learning","display_name":"Online machine learning","score":0.45791900157928467},{"id":"https://openalex.org/keywords/supervised-learning","display_name":"Supervised learning","score":0.45166897773742676},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4125072956085205},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.1575494110584259},{"id":"https://openalex.org/keywords/computer-security","display_name":"Computer security","score":0.07997140288352966}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7480944395065308},{"id":"https://openalex.org/C121704057","wikidata":"https://www.wikidata.org/wiki/Q352070","display_name":"Collision","level":2,"score":0.7267696261405945},{"id":"https://openalex.org/C2780864053","wikidata":"https://www.wikidata.org/wiki/Q5147495","display_name":"Collision avoidance","level":3,"score":0.6731156706809998},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.6623414754867554},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5824495553970337},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5364516377449036},{"id":"https://openalex.org/C58973888","wikidata":"https://www.wikidata.org/wiki/Q1041418","display_name":"Semi-supervised learning","level":2,"score":0.45994871854782104},{"id":"https://openalex.org/C115903097","wikidata":"https://www.wikidata.org/wiki/Q7094097","display_name":"Online machine learning","level":3,"score":0.45791900157928467},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.45166897773742676},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4125072956085205},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.1575494110584259},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.07997140288352966},{"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/icufn.2018.8436950","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icufn.2018.8436950","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 Tenth International Conference on Ubiquitous and Future Networks (ICUFN)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":12,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1538131130","https://openalex.org/W2064675550","https://openalex.org/W2296673577","https://openalex.org/W2528501250","https://openalex.org/W2608112326","https://openalex.org/W2962957005","https://openalex.org/W2963110888","https://openalex.org/W2963428623","https://openalex.org/W4298857966","https://openalex.org/W6632100814","https://openalex.org/W6637967152"],"related_works":["https://openalex.org/W1756896031","https://openalex.org/W2098239572","https://openalex.org/W2380231294","https://openalex.org/W1549064177","https://openalex.org/W1571236831","https://openalex.org/W2531570999","https://openalex.org/W2616146705","https://openalex.org/W2026068354","https://openalex.org/W4205475721","https://openalex.org/W4247023040"],"abstract_inverted_index":{"We":[0],"introduce":[1],"a":[2],"deep":[3],"learning":[4,10,17,22,57],"based":[5,8],"collision":[6,76],"avoidance":[7],"on":[9],"events":[11],"accompanied":[12],"by":[13,29],"an":[14],"online,":[15],"semi-supervised":[16],"algorithm":[18],"that":[19,62],"allows":[20],"the":[21,50],"agent":[23,84],"to":[24,71,85],"gain":[25],"experiences":[26],"and":[27,56],"learn":[28],"itself":[30],"without":[31,90],"any":[32],"preacquired":[33],"training":[34],"dataset":[35],"through":[36],"online":[37],"trial-and-error":[38],"approach.":[39],"Using":[40],"distance":[41],"sequences":[42],"as":[43],"inputs,":[44],"two":[45],"procedures":[46],"are":[47],"performed":[48],"in":[49,74],"proposed":[51],"algorithm;":[52],"data":[53],"gathering":[54],"procedure":[55],"procedure.":[58],"Simulation":[59],"results":[60],"show":[61],"our":[63],"system":[64],"can":[65],"achieve":[66],"minimum":[67],"of":[68],"99.86%":[69],"up":[70],"99.99%":[72],"accuracy":[73],"classifying":[75],"event":[77],"from":[78],"all":[79],"possible":[80],"events,":[81],"allowing":[82],"autonomous":[83],"navigate":[86],"within":[87],"simulated":[88],"environments":[89],"collision.":[91]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}