{"id":"W4392156457","doi":"10.1155/2024/6628559","title":"Evaluation of Preferred Automated Driving Patterns Based on a Driving Propensity Using Fuzzy Inference System","year":2024,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Korean National Police Agency; University of Seoul","keywords":"Inference; Fuzzy inference system; Fuzzy inference; Fuzzy logic; Computer science; Inference system; Artificial intelligence; Adaptive neuro fuzzy inference system; Fuzzy control system","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007869145,0.0001290287,0.0002482975,0.0002676693,0.00004007861,0.00001365647,0.00008929466,0.0001067992,0.000005820487],"category_scores_gemma":[0.00003955447,0.0001195015,0.00008282909,0.0002366117,0.0000174848,0.0003937886,0.000001397041,0.0002671466,9.925039e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000305935,"about_ca_system_score_gemma":0.0001436527,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003248136,"about_ca_topic_score_gemma":0.00003975442,"domain_scores_codex":[0.998652,0.00005749676,0.0005903879,0.0001136205,0.0004598418,0.0001266577],"domain_scores_gemma":[0.9992512,0.00007053911,0.0002017048,0.000115071,0.0003266129,0.00003482012],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001934777,0.00001939725,0.004875015,0.0003118899,0.00006166505,0.00001898019,0.000406226,0.9523789,0.02862528,0.0001035371,7.979434e-7,0.01317898],"study_design_scores_gemma":[0.0004062111,0.00008011171,0.206144,0.001800095,0.0002001026,0.000007258185,0.0001423472,0.7779456,0.01309661,0.00008156786,0.000003012145,0.00009311947],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9359152,0.0001016242,0.06288409,0.0000144646,0.0003894242,0.0002014027,0.000007638066,0.0004297575,0.00005638656],"genre_scores_gemma":[0.9970536,0.00001246469,0.00286727,0.000001973565,0.00002666446,0.00000473859,0.000009329736,0.00002339238,5.912472e-7],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.201269,"threshold_uncertainty_score":0.4873129,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02464793406515197,"score_gpt":0.2785044512397021,"score_spread":0.2538565171745501,"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."}}