{"id":"W3162356234","doi":"10.1109/wcnc49053.2021.9417463","title":"Driver Identification Using Vehicular Sensing Data: A Deep Learning Approach","year":2021,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"Canada Research Chairs","keywords":"Computer science; Classifier (UML); Identification (biology); Benchmark (surveying); Support vector machine; Architecture; Encoder; Artificial intelligence; Deep learning; Advanced driver assistance systems; Machine learning; Data modeling; Real-time computing; Database","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":[0.0004081394,0.0005805646,0.0003884719,0.000670484,0.0002223048,0.0004477279,0.0008213514,0.0006369086,0.0005946502],"category_scores_gemma":[0.0007863471,0.0002769336,0.000388213,0.0005504138,0.0001718137,0.0005463538,0.0005803882,0.0008833033,0.0003228567],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004089593,"about_ca_system_score_gemma":0.0005629505,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007022421,"about_ca_topic_score_gemma":0.009343815,"domain_scores_codex":[0.9998432,0.00002429352,0.00000875959,0.00004813751,0.00003347164,0.00004216381],"domain_scores_gemma":[0.9998091,0.00005116878,0.00002033053,0.00002744913,0.00007748296,0.00001451052],"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":[0.0002292589,0.0005324612,0.01303708,0.00009711462,0.0001389648,0.0001665245,0.0001315674,0.3736272,0.01279612,0.004265086,0.003980887,0.5909978],"study_design_scores_gemma":[0.000002105085,0.00002637535,0.0009859585,0.000005873193,0.000008464192,0.00001482738,0.00002032037,0.9958133,0.00157459,0.00108466,0.0004587709,0.000004674523],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2089476,0.0009894663,0.7842279,0.0005366374,0.0001535467,0.00009262386,0.0005580888,0.001116141,0.003377971],"genre_scores_gemma":[0.9439451,0.0003596726,0.05125222,0.00009325162,0.00006666903,0.00004895851,0.0011178,0.00001989847,0.00309652],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007022421,"threshold_uncertainty_score":0.01396304,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0803018506557022,"score_gpt":0.3238158038821682,"score_spread":0.243513953226466,"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."}}