{"id":"W2774270279","doi":"10.1109/jsen.2017.2780089","title":"Driving Maneuver Classification: A Comparison of Feature Extraction Methods","year":2017,"lang":"en","type":"article","venue":"IEEE Sensors Journal","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":50,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Feature extraction; Preprocessor; Computer science; Pattern recognition (psychology); Artificial intelligence; Principal component analysis; Classifier (UML); Statistical classification; Data pre-processing; Feature (linguistics); Data mining","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0003621012,0.0001063371,0.0002156621,0.00009032052,0.0003916268,0.00005078296,0.0002599317,0.0002337305,0.00004952147],"category_scores_gemma":[0.00005853997,0.0001016173,0.0000812466,0.00003732479,0.00009288135,0.0002200596,0.0000119161,0.0008102462,0.00001522333],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006035164,"about_ca_system_score_gemma":0.00001353974,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001345193,"about_ca_topic_score_gemma":0.000005326858,"domain_scores_codex":[0.9993289,0.00005298403,0.0002572576,0.00009086817,0.0001033069,0.0001667549],"domain_scores_gemma":[0.9992259,0.00005718376,0.0002585084,0.0003451841,0.00005758133,0.00005565083],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00004309744,0.00009799413,0.04400251,0.00006528742,0.0002997305,0.00006208175,0.001113072,0.02937987,0.5944027,0.0007531822,0.01665178,0.3131287],"study_design_scores_gemma":[0.000610882,0.00006582653,0.3959403,0.0001017668,0.00009361984,0.0009596689,0.000589092,0.2850833,0.2872972,0.0009759468,0.02790695,0.0003753974],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9216493,0.0002473199,0.06915371,0.0006653109,0.001617346,0.00006400314,0.000001475608,0.0001764467,0.006425027],"genre_scores_gemma":[0.9748748,0.000079429,0.02448537,0.000004735571,0.000188341,0.000001049759,3.810361e-7,0.00001746223,0.0003484257],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3519377,"threshold_uncertainty_score":0.4143834,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0452357521931929,"score_gpt":0.3748261357315726,"score_spread":0.3295903835383797,"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."}}