{"id":"W4212791280","doi":"10.1109/tits.2022.3151264","title":"Siamese Temporal Convolutional Networks for Driver Identification Using Driver Steering Behavior Analysis","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Transportation Systems","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Identification (biology); Computer science; Personalization; Telematics; Intelligent transportation system; Variety (cybernetics); Advanced driver assistance systems; Function (biology); Artificial intelligence; Machine learning; Real-time computing; Engineering; Transport engineering; Telecommunications","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":[0.000490335,0.0005182413,0.0003059387,0.0005820265,0.0002252339,0.0004029261,0.0006476235,0.000417236,0.001482898],"category_scores_gemma":[0.0009468659,0.0002336365,0.0004241091,0.000521512,0.0002330527,0.0006998936,0.0004896158,0.0007818755,0.000563286],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008173618,"about_ca_system_score_gemma":0.001036738,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02137294,"about_ca_topic_score_gemma":0.03084552,"domain_scores_codex":[0.9998291,0.00002253638,0.000009887597,0.00007181238,0.00003512904,0.00003155873],"domain_scores_gemma":[0.9997645,0.00005994317,0.00003180101,0.00003955579,0.00008756449,0.000016596],"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":[0.0003339149,0.0003749734,0.009472769,0.00008099467,0.000221712,0.0001920776,0.0001170075,0.3964028,0.02814927,0.005828467,0.006778723,0.5520472],"study_design_scores_gemma":[0.000002110268,0.00001562628,0.0009601328,0.000002114751,0.0000105068,0.0000201358,0.000005454501,0.994646,0.002927874,0.0009204331,0.0004850388,0.000004595688],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2052776,0.001136798,0.7839621,0.0005160011,0.0001290075,0.00007995546,0.0007944995,0.003989617,0.00411442],"genre_scores_gemma":[0.9090378,0.0004446551,0.08178626,0.0001526784,0.00004639669,0.00005016612,0.001657656,0.00005990646,0.006764497],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02137294,"threshold_uncertainty_score":0.0424971,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02243233130397846,"score_gpt":0.2477301170146924,"score_spread":0.2252977857107139,"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."}}