{"id":"https://openalex.org/W3112809593","doi":"https://doi.org/10.1109/syscon47679.2020.9275870","title":"Formal Validation of Emergent Behavior in a Machine Learning Based Collision Avoidance System","display_name":"Formal Validation of Emergent Behavior in a Machine Learning Based Collision Avoidance System","publication_year":2020,"publication_date":"2020-08-24","ids":{"openalex":"https://openalex.org/W3112809593","doi":"https://doi.org/10.1109/syscon47679.2020.9275870","mag":"3112809593"},"language":"en","primary_location":{"id":"doi:10.1109/syscon47679.2020.9275870","is_oa":false,"landing_page_url":"https://doi.org/10.1109/syscon47679.2020.9275870","pdf_url":null,"source":{"id":"https://openalex.org/S4306498685","display_name":"2020 IEEE International Systems Conference (SysCon)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Systems Conference (SysCon)","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/A5103161507","display_name":"Ramakrishnan Raman","orcid":"https://orcid.org/0000-0002-8471-9172"},"institutions":[{"id":"https://openalex.org/I4210101534","display_name":"Honeywell (India)","ror":"https://ror.org/017eb5121","country_code":"IN","type":"company","lineage":["https://openalex.org/I4210101534","https://openalex.org/I82514191"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Ramakrishnan Raman","raw_affiliation_strings":["Honeywell Technology Solutions Lab, Bangalore, India"],"affiliations":[{"raw_affiliation_string":"Honeywell Technology Solutions Lab, Bangalore, India","institution_ids":["https://openalex.org/I4210101534"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5000279949","display_name":"Yogananda Jeppu","orcid":"https://orcid.org/0000-0003-1401-6348"},"institutions":[{"id":"https://openalex.org/I4210101534","display_name":"Honeywell (India)","ror":"https://ror.org/017eb5121","country_code":"IN","type":"company","lineage":["https://openalex.org/I4210101534","https://openalex.org/I82514191"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Yogananda Jeppu","raw_affiliation_strings":["Honeywell Technology Solutions Lab, Bangalore, India"],"affiliations":[{"raw_affiliation_string":"Honeywell Technology Solutions Lab, Bangalore, India","institution_ids":["https://openalex.org/I4210101534"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5103161507"],"corresponding_institution_ids":["https://openalex.org/I4210101534"],"apc_list":null,"apc_paid":null,"fwci":2.3845,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.89790795,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":"2","issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.9950000047683716,"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":0.9950000047683716,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9908000230789185,"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/T11489","display_name":"Air Traffic Management and Optimization","score":0.9860000014305115,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace Engineering"},"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/collision-avoidance","display_name":"Collision avoidance","score":0.7919940948486328},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7525187730789185},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6750043630599976},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5847952961921692},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.4918525218963623},{"id":"https://openalex.org/keywords/collision-avoidance-system","display_name":"Collision avoidance system","score":0.41098344326019287},{"id":"https://openalex.org/keywords/collision","display_name":"Collision","score":0.3975340723991394},{"id":"https://openalex.org/keywords/computer-security","display_name":"Computer security","score":0.14300119876861572}],"concepts":[{"id":"https://openalex.org/C2780864053","wikidata":"https://www.wikidata.org/wiki/Q5147495","display_name":"Collision avoidance","level":3,"score":0.7919940948486328},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7525187730789185},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6750043630599976},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5847952961921692},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.4918525218963623},{"id":"https://openalex.org/C2777016798","wikidata":"https://www.wikidata.org/wiki/Q2001988","display_name":"Collision avoidance system","level":4,"score":0.41098344326019287},{"id":"https://openalex.org/C121704057","wikidata":"https://www.wikidata.org/wiki/Q352070","display_name":"Collision","level":2,"score":0.3975340723991394},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.14300119876861572}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/syscon47679.2020.9275870","is_oa":false,"landing_page_url":"https://doi.org/10.1109/syscon47679.2020.9275870","pdf_url":null,"source":{"id":"https://openalex.org/S4306498685","display_name":"2020 IEEE International Systems Conference (SysCon)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Systems Conference (SysCon)","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":15,"referenced_works":["https://openalex.org/W1944672","https://openalex.org/W850898932","https://openalex.org/W1575486920","https://openalex.org/W2051592730","https://openalex.org/W2065919808","https://openalex.org/W2068883935","https://openalex.org/W2100205815","https://openalex.org/W2133056442","https://openalex.org/W2136604063","https://openalex.org/W2540093921","https://openalex.org/W2741086668","https://openalex.org/W2796676158","https://openalex.org/W2895120768","https://openalex.org/W2975715929","https://openalex.org/W6755219234"],"related_works":["https://openalex.org/W1819938260","https://openalex.org/W2340892746","https://openalex.org/W3005999311","https://openalex.org/W3042530408","https://openalex.org/W2390040493","https://openalex.org/W4253267946","https://openalex.org/W2983014903","https://openalex.org/W2945385675","https://openalex.org/W1767544057","https://openalex.org/W2153778739"],"abstract_inverted_index":{"Collision":[0,68,189],"Avoidance":[1,69,190],"Systems":[2,191],"play":[3],"a":[4,92,174,204,223],"major":[5],"role":[6],"in":[7,44,83,158,222],"the":[8,17,57,74,143,159,220],"development":[9],"and":[10,29,34,38,40,80,107,138,150,184,214,218],"integration":[11],"of":[12,59,116,132,142,145,161,179,188],"Unmanned":[13],"Aerial":[14],"Vehicles":[15],"into":[16],"airspace.":[18],"There":[19,90],"has":[20,51,86],"been":[21,52],"extensive":[22],"research":[23],"on":[24,56,211],"various":[25,60],"collision":[26],"avoidance":[27,43],"algorithms":[28,33],"techniques.":[30],"Typically,":[31],"these":[32,84],"techniques":[35],"involve":[36],"sensing":[37],"detection,":[39],"approaches":[41,152],"to":[42,94,126,156,229],"2D":[45],"or":[46,169],"3D":[47],"scenarios.":[48],"Recently,":[49],"there":[50],"an":[53],"exponential":[54],"increase":[55],"adoption":[58],"neural":[61],"network":[62],"based":[63],"machine":[64,196,205],"learning":[65,197,206],"models":[66],"for":[67,182],"Systems.":[70],"With":[71],"this":[72],"trend,":[73],"systems":[75,85,103],"are":[76,104,120,140,153,193],"becoming":[77],"increasingly":[78,88],"complex,":[79],"achieving":[81],"confidence":[82],"become":[87],"difficult.":[89],"is":[91,112],"need":[93],"ensure":[95],"that":[96,192,209],"emergent":[97,163,186,216,232],"behavior":[98,187],"associated":[99],"with":[100],"such":[101],"complex":[102,110,146],"well":[105],"analyzed":[106],"understood.":[108],"A":[109],"system":[111],"characterized":[113],"by":[114,195],"emergence":[115],"global":[117],"properties":[118],"which":[119,165],"very":[121],"difficult,":[122],"if":[123],"not":[124],"impossible,":[125],"anticipate":[127],"just":[128],"from":[129],"complete":[130],"knowledge":[131],"component":[133],"behaviors.":[134],"Emergence,":[135],"hierarchical":[136],"organization":[137],"numerosity":[139],"some":[141],"characteristics":[144],"systems.":[147],"Traditional":[148],"verification":[149,225],"validation":[151],"often":[154],"inadequate":[155],"bring":[157],"nuances":[160],"potential":[162,212],"behavior,":[164],"may":[166],"be":[167],"positive":[168,215],"negative.":[170],"This":[171],"paper":[172],"describes":[173],"novel":[175],"approach":[176,201],"towards":[177],"application":[178],"formal":[180,224],"methods":[181],"analyzing":[183],"evaluating":[185],"governed":[194],"models.":[198],"The":[199],"proposed":[200],"involves":[202],"developing":[203],"classifier":[207,221],"model":[208,226],"learns":[210],"negative":[213,231],"behaviors,":[217],"leveraging":[219],"checking":[227],"environment":[228],"assert":[230],"behavior.":[233]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":4}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
