{"id":"W4287549454","doi":"","title":"The Role of Big Data in the Development of Safety in Fully Automated Vehicles: A Survey","year":2020,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Computer science; Big data; Vehicle safety; Data science; Data mining; Engineering; Automotive engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01057055,0.0001929158,0.0003345746,0.000121542,0.000113409,0.0000369633,0.002941689,0.0002608042,0.000004374502],"category_scores_gemma":[0.001057287,0.0001550552,0.00004631503,0.0005743406,0.0002119004,0.00004386007,0.001587089,0.0007538737,0.000004967379],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008577675,"about_ca_system_score_gemma":0.0003374456,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008420197,"about_ca_topic_score_gemma":0.027363,"domain_scores_codex":[0.9954616,0.002837453,0.0008749422,0.0003535946,0.0002396687,0.0002327772],"domain_scores_gemma":[0.9952384,0.001856295,0.0002695284,0.002315088,0.0002891919,0.00003148306],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0001275553,0.0008861054,0.09423565,0.0007351317,0.0003899908,0.000007393493,0.08617328,0.004956673,0.007381857,0.02889281,0.0004073747,0.7758062],"study_design_scores_gemma":[0.0005655364,4.260293e-7,0.518675,0.001106233,0.00001870319,0.000002234419,0.001090777,0.4368669,0.03011368,0.002636137,0.008545636,0.0003787937],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9494382,0.004007131,0.02397964,0.004363222,0.0002074382,0.001096022,0.0004149901,0.0007739019,0.01571949],"genre_scores_gemma":[0.9936234,0.0004178883,0.005348907,0.000008678006,0.000003347429,0.00002980604,0.0005187468,0.00002202508,0.00002720749],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7754274,"threshold_uncertainty_score":0.9903851,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02727283699309365,"score_gpt":0.2317434425167805,"score_spread":0.2044706055236868,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}