{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001457797,0.001220594,0.001034211,0.003227027,0.0003079465,0.0006557304,0.0006532251,0.0007553441,0.001374808],"category_scores_gemma":[0.00337986,0.0001687415,0.001012637,0.001701023,0.0001923966,0.001345853,0.0004777022,0.0005202296,0.001055313],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002772075,"about_ca_system_score_gemma":0.0004515135,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003640258,"about_ca_topic_score_gemma":0.002604139,"domain_scores_codex":[0.9987684,0.0001454017,0.0001605581,0.0002877993,0.0004928088,0.0001449383],"domain_scores_gemma":[0.9985752,0.0005815616,0.0001118784,0.0001089862,0.0005730247,0.00004928476],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0009611011,0.0003762005,0.01994953,0.0004784637,0.0003185373,0.0001121446,0.00009909701,0.01427725,0.01665739,0.0003901113,0.004925185,0.941455],"study_design_scores_gemma":[0.0002218762,0.002395731,0.1569732,0.000298807,0.0006529744,0.001244825,0.0008158398,0.749368,0.06442476,0.002284389,0.02106791,0.000251685],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5969298,0.01745307,0.3625396,0.000928049,0.0007622843,0.0007105715,0.003420912,0.007643729,0.009611937],"genre_scores_gemma":[0.8339463,0.004377447,0.1495517,0.0001878587,0.0002538237,0.0003565196,0.007453061,0.0002762603,0.003596973],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003640258,"threshold_uncertainty_score":0.007709622,"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."}}