{"id":"https://openalex.org/W2885739519","doi":"https://doi.org/10.1109/civemsa.2018.8439980","title":"Autonomous Vehicle Tracking Control Using Deep Learning and Stereo Vision","display_name":"Autonomous Vehicle Tracking Control Using Deep Learning and Stereo Vision","publication_year":2018,"publication_date":"2018-06-01","ids":{"openalex":"https://openalex.org/W2885739519","doi":"https://doi.org/10.1109/civemsa.2018.8439980","mag":"2885739519"},"language":"en","primary_location":{"id":"doi:10.1109/civemsa.2018.8439980","is_oa":false,"landing_page_url":"https://doi.org/10.1109/civemsa.2018.8439980","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 Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA)","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/A5101812846","display_name":"Teng Zhao","orcid":null},"institutions":[{"id":"https://openalex.org/I172980758","display_name":"Kennesaw State University","ror":"https://ror.org/00jeqjx33","country_code":"US","type":"education","lineage":["https://openalex.org/I172980758"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Teng Zhao","raw_affiliation_strings":["Department of Computer Science, Kennesaw State University, Marietta, GA, USA"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Kennesaw State University, Marietta, GA, USA","institution_ids":["https://openalex.org/I172980758"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100351521","display_name":"Ming Li","orcid":"https://orcid.org/0000-0003-3163-5992"},"institutions":[{"id":"https://openalex.org/I172980758","display_name":"Kennesaw State University","ror":"https://ror.org/00jeqjx33","country_code":"US","type":"education","lineage":["https://openalex.org/I172980758"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ming Li","raw_affiliation_strings":["Department of Computer Science, Kennesaw State University, Marietta, GA, USA"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science, Kennesaw State University, Marietta, GA, USA","institution_ids":["https://openalex.org/I172980758"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5010417850","display_name":"Gabriel Chen","orcid":null},"institutions":[{"id":"https://openalex.org/I4210101161","display_name":"Walton County School District","ror":"https://ror.org/018br7a38","country_code":"US","type":"education","lineage":["https://openalex.org/I4210101161"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Gabriel Chen","raw_affiliation_strings":["Walton High School, Marietta, GA"],"affiliations":[{"raw_affiliation_string":"Walton High School, Marietta, GA","institution_ids":["https://openalex.org/I4210101161"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100347007","display_name":"Ying Wang","orcid":"https://orcid.org/0000-0001-8152-2780"},"institutions":[{"id":"https://openalex.org/I172980758","display_name":"Kennesaw State University","ror":"https://ror.org/00jeqjx33","country_code":"US","type":"education","lineage":["https://openalex.org/I172980758"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ying Wang","raw_affiliation_strings":["Department of Mechatronics Engineering, Kennesaw State University, Marietta, GA, USA"],"affiliations":[{"raw_affiliation_string":"Department of Mechatronics Engineering, Kennesaw State University, Marietta, GA, USA","institution_ids":["https://openalex.org/I172980758"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5101812846"],"corresponding_institution_ids":["https://openalex.org/I172980758"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.07630493,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9991999864578247,"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9991999864578247,"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":0.9991000294685364,"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":0.9972000122070312,"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/artificial-intelligence","display_name":"Artificial intelligence","score":0.8097994923591614},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.7735185623168945},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.728885293006897},{"id":"https://openalex.org/keywords/kalman-filter","display_name":"Kalman filter","score":0.5712712407112122},{"id":"https://openalex.org/keywords/stereopsis","display_name":"Stereopsis","score":0.5535206198692322},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5439088940620422},{"id":"https://openalex.org/keywords/reliability","display_name":"Reliability (semiconductor)","score":0.5010910034179688},{"id":"https://openalex.org/keywords/tracking","display_name":"Tracking (education)","score":0.480620801448822},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.4486510753631592},{"id":"https://openalex.org/keywords/controller","display_name":"Controller (irrigation)","score":0.419801265001297},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.41519129276275635}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.8097994923591614},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.7735185623168945},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.728885293006897},{"id":"https://openalex.org/C157286648","wikidata":"https://www.wikidata.org/wiki/Q846780","display_name":"Kalman filter","level":2,"score":0.5712712407112122},{"id":"https://openalex.org/C68537008","wikidata":"https://www.wikidata.org/wiki/Q247932","display_name":"Stereopsis","level":2,"score":0.5535206198692322},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5439088940620422},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.5010910034179688},{"id":"https://openalex.org/C2775936607","wikidata":"https://www.wikidata.org/wiki/Q466845","display_name":"Tracking (education)","level":2,"score":0.480620801448822},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.4486510753631592},{"id":"https://openalex.org/C203479927","wikidata":"https://www.wikidata.org/wiki/Q5165939","display_name":"Controller (irrigation)","level":2,"score":0.419801265001297},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.41519129276275635},{"id":"https://openalex.org/C6557445","wikidata":"https://www.wikidata.org/wiki/Q173113","display_name":"Agronomy","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"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/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"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/C19417346","wikidata":"https://www.wikidata.org/wiki/Q7922","display_name":"Pedagogy","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/civemsa.2018.8439980","is_oa":false,"landing_page_url":"https://doi.org/10.1109/civemsa.2018.8439980","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 Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA)","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":14,"referenced_works":["https://openalex.org/W1987604987","https://openalex.org/W2001860529","https://openalex.org/W2013928382","https://openalex.org/W2037386293","https://openalex.org/W2093379036","https://openalex.org/W2122062344","https://openalex.org/W2177270872","https://openalex.org/W2193145675","https://openalex.org/W2248010213","https://openalex.org/W2297679979","https://openalex.org/W2528746485","https://openalex.org/W2573073349","https://openalex.org/W2744979297","https://openalex.org/W3106250896"],"related_works":["https://openalex.org/W4293226380","https://openalex.org/W4375867731","https://openalex.org/W2084086966","https://openalex.org/W4226493464","https://openalex.org/W4312417841","https://openalex.org/W3133861977","https://openalex.org/W2951211570","https://openalex.org/W3103566983","https://openalex.org/W3167935049","https://openalex.org/W3029198973"],"abstract_inverted_index":{"In":[0,23],"this":[1],"paper,":[2],"a":[3,25,89],"vehicle":[4,48,75],"autonomous":[5],"tracking":[6],"control":[7],"strategy":[8],"is":[9,29,66,86,93],"proposed":[10,114],"through":[11],"fusing":[12],"neural-network":[13],"based":[14,81],"control,":[15],"deep":[16,63],"learning,":[17],"stereo":[18],"vision":[19,34,102],"and":[20,35,38,42,88,99,103],"Kalman":[21],"filtering.":[22],"particular,":[24],"neural":[26],"network":[27],"controller":[28],"developed":[30],"to":[31,68,95],"utilize":[32],"the":[33,40,46,70,73,83,97,110,113],"distance":[36,104],"information":[37],"adjust":[39],"translational":[41],"rotational":[43],"speeds":[44],"of":[45,72,101,112],"follower":[47],"so":[49],"that":[50],"it":[51],"can":[52],"track":[53],"its":[54],"leader":[55,74],"autonomously.":[56],"The":[57,106],"SSD":[58],"(Single":[59],"Shot":[60],"MultiBox":[61],"Detector)":[62],"learning":[64],"technology":[65],"employed":[67],"detect":[69],"position":[71],"visually,":[76],"an":[77],"image":[78,85],"filtering":[79,91],"algorithm":[80],"on":[82],"depth":[84],"proposed,":[87],"dual-Kalman":[90],"approach":[92],"presented":[94],"improve":[96],"reliability":[98],"speed":[100],"measurements.":[105],"experimental":[107],"results":[108],"validate":[109],"effectiveness":[111],"strategy.":[115]},"counts_by_year":[{"year":2022,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}