{"id":"https://openalex.org/W7128991134","doi":"https://doi.org/10.48550/arxiv.2602.12902","title":"Robustness of Object Detection of Autonomous Vehicles in Adverse Weather Conditions","display_name":"Robustness of Object Detection of Autonomous Vehicles in Adverse Weather Conditions","publication_year":2026,"publication_date":"2026-02-13","ids":{"openalex":"https://openalex.org/W7128991134","doi":"https://doi.org/10.48550/arxiv.2602.12902"},"language":null,"primary_location":{"id":"pmh:doi:10.48550/arxiv.2602.12902","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","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":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"type":"article","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":null,"any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5126099021","display_name":"Fox Pettersen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pettersen, Fox","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5126118771","display_name":"Hong Zhu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhu, Hong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.12969171,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.597000002861023,"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":0.597000002861023,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.219200000166893,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.037700001150369644,"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/robustness","display_name":"Robustness (evolution)","score":0.876800000667572},{"id":"https://openalex.org/keywords/adverse-weather","display_name":"Adverse weather","score":0.8409000039100647},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.4334000051021576},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.36719998717308044},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.2793999910354614}],"concepts":[{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.876800000667572},{"id":"https://openalex.org/C2992147540","wikidata":"https://www.wikidata.org/wiki/Q1277161","display_name":"Adverse weather","level":2,"score":0.8409000039100647},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5622000098228455},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.4334000051021576},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3752000033855438},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.36719998717308044},{"id":"https://openalex.org/C44154836","wikidata":"https://www.wikidata.org/wiki/Q45045","display_name":"Simulation","level":1,"score":0.302700012922287},{"id":"https://openalex.org/C200601418","wikidata":"https://www.wikidata.org/wiki/Q2193887","display_name":"Reliability engineering","level":1,"score":0.30149999260902405},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.28790000081062317},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.28290000557899475},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.2802000045776367},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.2793999910354614},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.27070000767707825},{"id":"https://openalex.org/C3020368824","wikidata":"https://www.wikidata.org/wiki/Q6546192","display_name":"Light intensity","level":2,"score":0.2669000029563904},{"id":"https://openalex.org/C115901376","wikidata":"https://www.wikidata.org/wiki/Q184199","display_name":"Automation","level":2,"score":0.2565999925136566}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:doi:10.48550/arxiv.2602.12902","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","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":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},{"id":"doi:10.48550/arxiv.2602.12902","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.12902","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:doi:10.48550/arxiv.2602.12902","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","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":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","score":0.5034679770469666,"display_name":"Sustainable cities and communities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"As":[0],"self-driving":[1],"technology":[2],"advances":[3],"toward":[4],"widespread":[5],"adoption,":[6],"determining":[7],"safe":[8],"operational":[9],"thresholds":[10],"across":[11],"varying":[12],"environmental":[13],"conditions":[14,59,72,127,134,215,233],"becomes":[15],"critical":[16],"for":[17,25],"public":[18],"safety.":[19],"This":[20],"paper":[21,104],"proposes":[22],"a":[23],"method":[24,147,200],"evaluating":[26],"the":[27,56,66,70,75,83,90,97,101,146,157,170,175,194,206],"robustness":[28,81,158,177,230],"of":[29,55,69,82,135,159,183,197,208],"object":[30,76,84,110,160],"detection":[31,77,85,111,161],"ML":[32],"models":[33,162],"in":[34,100,163,231,245],"autonomous":[35],"vehicles":[36],"under":[37],"adverse":[38,57,71,165,188,213,232],"weather":[39,126],"conditions.":[40,167],"It":[41,222],"employs":[42],"data":[43,49,121,143,218],"augmentation":[44,122],"operators":[45,123],"to":[46,64,153,204],"generate":[47],"synthetic":[48,217],"that":[50,124,145,211,225],"simulates":[51],"different":[52],"severance":[53],"degrees":[54],"operation":[58,166,214],"at":[60,73],"progressive":[61],"intensity":[62,68],"levels":[63],"find":[65],"lowest":[67],"which":[74],"model":[78,86,173,209,220],"fails.":[79],"The":[80,103,141,199],"is":[87,148,201,223],"measured":[88],"by":[89],"average":[91,181],"first":[92],"failure":[93],"coefficients":[94],"(AFFC)":[95],"over":[96,185],"input":[98],"images":[99],"benchmark.":[102],"reports":[105],"an":[106,179],"experiment":[107,142],"with":[108,178],"four":[109],"models:":[112],"YOLOv5s,":[113],"YOLOv11s,":[114],"Faster":[115,171],"R-CNN,":[116],"and":[117,130,132,139,151,155,240],"Detectron2,":[118],"utilising":[119],"seven":[120,187],"simulate":[125],"fog,":[128],"rain,":[129],"snow,":[131],"lighting":[133],"dark,":[136],"bright,":[137],"flaring,":[138],"shadow.":[140],"show":[144],"feasible,":[149],"effective,":[150],"efficient":[152],"evaluate":[154],"compare":[156],"various":[164],"In":[168],"particular,":[169],"R-CNN":[172],"achieved":[174],"highest":[176],"overall":[180],"AFFC":[182,195],"71.9%":[184],"all":[186],"conditions,":[189],"while":[190],"YOLO":[191],"variants":[192],"showed":[193],"values":[196],"43%.":[198],"also":[202],"applied":[203],"assess":[205],"impact":[207],"training":[210,227],"targets":[212],"using":[216],"on":[219],"robustness.":[221],"observed":[224],"such":[226],"can":[228],"improve":[229],"but":[234],"may":[235],"suffer":[236],"from":[237],"diminishing":[238],"returns":[239],"forgetting":[241],"phenomena":[242],"(i.e.,":[243],"decline":[244],"robustness)":[246],"if":[247],"overtrained.":[248]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-02-17T00:00:00"}
