{"id":"https://openalex.org/W2783966546","doi":"https://doi.org/10.1109/coase.2017.8256245","title":"Functional gradient descent optimization for automatic test case generation for vehicle controllers","display_name":"Functional gradient descent optimization for automatic test case generation for vehicle controllers","publication_year":2017,"publication_date":"2017-08-01","ids":{"openalex":"https://openalex.org/W2783966546","doi":"https://doi.org/10.1109/coase.2017.8256245","mag":"2783966546"},"language":"en","primary_location":{"id":"doi:10.1109/coase.2017.8256245","is_oa":false,"landing_page_url":"https://doi.org/10.1109/coase.2017.8256245","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 13th IEEE Conference on Automation Science and Engineering (CASE)","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/A5080673186","display_name":"Cumhur Erkan Tuncali","orcid":"https://orcid.org/0000-0002-8948-187X"},"institutions":[{"id":"https://openalex.org/I55732556","display_name":"Arizona State University","ror":"https://ror.org/03efmqc40","country_code":"US","type":"education","lineage":["https://openalex.org/I55732556"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Cumhur Erkan Tuncali","raw_affiliation_strings":["School of Computing, Arizona State University, Tempe, AZ, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computing, Arizona State University, Tempe, AZ, USA","institution_ids":["https://openalex.org/I55732556"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5112272179","display_name":"Shakiba Yaghoubi","orcid":null},"institutions":[{"id":"https://openalex.org/I55732556","display_name":"Arizona State University","ror":"https://ror.org/03efmqc40","country_code":"US","type":"education","lineage":["https://openalex.org/I55732556"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Shakiba Yaghoubi","raw_affiliation_strings":["School of Computing, Arizona State University, Tempe, AZ, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computing, Arizona State University, Tempe, AZ, USA","institution_ids":["https://openalex.org/I55732556"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5005386595","display_name":"Theodore P. Pavlic","orcid":"https://orcid.org/0000-0002-7073-6932"},"institutions":[{"id":"https://openalex.org/I55732556","display_name":"Arizona State University","ror":"https://ror.org/03efmqc40","country_code":"US","type":"education","lineage":["https://openalex.org/I55732556"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Theodore P. Pavlic","raw_affiliation_strings":["School of Computing, Arizona State University, Tempe, AZ, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computing, Arizona State University, Tempe, AZ, USA","institution_ids":["https://openalex.org/I55732556"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5058732387","display_name":"Georgios Fainekos","orcid":"https://orcid.org/0000-0002-0456-2129"},"institutions":[{"id":"https://openalex.org/I55732556","display_name":"Arizona State University","ror":"https://ror.org/03efmqc40","country_code":"US","type":"education","lineage":["https://openalex.org/I55732556"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Georgios Fainekos","raw_affiliation_strings":["School of Computing, Arizona State University, Tempe, AZ, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computing, Arizona State University, Tempe, AZ, USA","institution_ids":["https://openalex.org/I55732556"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.25,"has_fulltext":false,"cited_by_count":10,"citation_normalized_percentile":{"value":0.83351205,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1059","last_page":"1064"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.9995999932289124,"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":0.9995999932289124,"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":0.9969000220298767,"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/T10370","display_name":"Traffic and Road Safety","score":0.9925000071525574,"subfield":{"id":"https://openalex.org/subfields/2213","display_name":"Safety, Risk, Reliability and Quality"},"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/fidelity","display_name":"Fidelity","score":0.7349982857704163},{"id":"https://openalex.org/keywords/cruise-control","display_name":"Cruise control","score":0.7161529064178467},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7025627493858337},{"id":"https://openalex.org/keywords/high-fidelity","display_name":"High fidelity","score":0.6240362524986267},{"id":"https://openalex.org/keywords/simulated-annealing","display_name":"Simulated annealing","score":0.5995702743530273},{"id":"https://openalex.org/keywords/gradient-descent","display_name":"Gradient descent","score":0.5920068025588989},{"id":"https://openalex.org/keywords/test-case","display_name":"Test case","score":0.5243375897407532},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.502368688583374},{"id":"https://openalex.org/keywords/collision-avoidance","display_name":"Collision avoidance","score":0.4490422308444977},{"id":"https://openalex.org/keywords/collision","display_name":"Collision","score":0.34362196922302246},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.296716570854187},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.2663496136665344},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.1911136507987976},{"id":"https://openalex.org/keywords/control","display_name":"Control (management)","score":0.15798699855804443},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.13541856408119202},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.07758784294128418}],"concepts":[{"id":"https://openalex.org/C2776459999","wikidata":"https://www.wikidata.org/wiki/Q2119376","display_name":"Fidelity","level":2,"score":0.7349982857704163},{"id":"https://openalex.org/C113168747","wikidata":"https://www.wikidata.org/wiki/Q507295","display_name":"Cruise control","level":3,"score":0.7161529064178467},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7025627493858337},{"id":"https://openalex.org/C113364801","wikidata":"https://www.wikidata.org/wiki/Q26674","display_name":"High fidelity","level":2,"score":0.6240362524986267},{"id":"https://openalex.org/C126980161","wikidata":"https://www.wikidata.org/wiki/Q863783","display_name":"Simulated annealing","level":2,"score":0.5995702743530273},{"id":"https://openalex.org/C153258448","wikidata":"https://www.wikidata.org/wiki/Q1199743","display_name":"Gradient descent","level":3,"score":0.5920068025588989},{"id":"https://openalex.org/C128942645","wikidata":"https://www.wikidata.org/wiki/Q1568346","display_name":"Test case","level":3,"score":0.5243375897407532},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.502368688583374},{"id":"https://openalex.org/C2780864053","wikidata":"https://www.wikidata.org/wiki/Q5147495","display_name":"Collision avoidance","level":3,"score":0.4490422308444977},{"id":"https://openalex.org/C121704057","wikidata":"https://www.wikidata.org/wiki/Q352070","display_name":"Collision","level":2,"score":0.34362196922302246},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.296716570854187},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2663496136665344},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.1911136507987976},{"id":"https://openalex.org/C2775924081","wikidata":"https://www.wikidata.org/wiki/Q55608371","display_name":"Control (management)","level":2,"score":0.15798699855804443},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.13541856408119202},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.07758784294128418},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.0},{"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/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/coase.2017.8256245","is_oa":false,"landing_page_url":"https://doi.org/10.1109/coase.2017.8256245","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 13th IEEE Conference on Automation Science and Engineering (CASE)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/7","score":0.6299999952316284,"display_name":"Affordable and clean energy"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W1572813036","https://openalex.org/W1617899343","https://openalex.org/W1979308183","https://openalex.org/W2013967953","https://openalex.org/W2037936106","https://openalex.org/W2047030012","https://openalex.org/W2054704105","https://openalex.org/W2056605754","https://openalex.org/W2099604946","https://openalex.org/W2131399618","https://openalex.org/W2166751342","https://openalex.org/W2172184261","https://openalex.org/W2289542989","https://openalex.org/W2397089707","https://openalex.org/W2489535233","https://openalex.org/W2554968732","https://openalex.org/W2556282465","https://openalex.org/W2563408008","https://openalex.org/W2564317698","https://openalex.org/W2564973635","https://openalex.org/W2735488281","https://openalex.org/W4230425707","https://openalex.org/W4247176093","https://openalex.org/W6679643274","https://openalex.org/W6696792689"],"related_works":["https://openalex.org/W2510731964","https://openalex.org/W2904810179","https://openalex.org/W2136292948","https://openalex.org/W2906048164","https://openalex.org/W2800151007","https://openalex.org/W1495726887","https://openalex.org/W2057455638","https://openalex.org/W2017572488","https://openalex.org/W2095302588","https://openalex.org/W2783966546"],"abstract_inverted_index":{"A":[0],"hierarchical":[1],"framework":[2,21,61],"is":[3,62],"proposed":[4,60],"for":[5,13,26,55,80],"improving":[6],"the":[7,50,56],"automatic":[8],"test":[9,51,86,92],"case":[10,52],"generation":[11,53],"process":[12,54],"high-fidelity":[14,57],"models":[15,25,48],"with":[16,36,70,76],"long":[17],"execution":[18],"times.":[19],"The":[20,46,59],"incorporates":[22],"related":[23],"low-fidelity":[24,47],"which":[27,81],"certain":[28],"properties":[29],"can":[30],"be":[31],"analytically":[32],"or":[33,43],"computationally":[34],"evaluated":[35],"provable":[37],"guarantees":[38],"(e.g.,":[39],"gradients":[40],"of":[41,67],"safety":[42],"performance":[44],"metrics).":[45],"drive":[49],"models.":[58],"demonstrated":[63],"on":[64,88],"a":[65,68],"model":[66],"vehicle":[69],"Full":[71],"Range":[72],"Adaptive":[73],"Cruise":[74],"Control":[75],"Collision":[77],"Avoidance":[78],"(FRACC),":[79],"it":[82],"generates":[83],"more":[84],"challenging":[85],"cases":[87,93],"average":[89],"compared":[90],"to":[91],"generated":[94],"using":[95],"Simulated":[96],"Annealing.":[97]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":3},{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":2}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
