{"id":"W3195591203","doi":"10.1109/tits.2021.3101000","title":"Leveraging Human Driving Preferences to Predict Vehicle Speed","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Transportation Systems","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"China Scholarship Council","keywords":"Leverage (statistics); Computer science; Hidden Markov model; Driving simulator; Traffic speed; Mean squared error; Markov chain; Mean squared prediction error; Engineering; Artificial intelligence; Simulation; Machine learning; Statistics; Mathematics; Transport engineering","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0002725571,0.0005571598,0.0003261241,0.0005979312,0.000140706,0.000323778,0.000449221,0.0003695139,0.0004460402],"category_scores_gemma":[0.001035553,0.0002712203,0.0003860224,0.0004534121,0.0001450115,0.000498271,0.0002731296,0.000556912,0.0002549967],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002222735,"about_ca_system_score_gemma":0.0004640208,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008645384,"about_ca_topic_score_gemma":0.01323432,"domain_scores_codex":[0.9998623,0.00002523427,0.000008484226,0.0000511636,0.00002980484,0.00002303803],"domain_scores_gemma":[0.9996289,0.0001339521,0.00007170009,0.00003914173,0.0000947056,0.00003155582],"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":[0.0001827608,0.0001889022,0.05513724,0.00006433778,0.0001130382,0.000101342,0.00009119674,0.7874663,0.006752696,0.0009956784,0.001062742,0.1478438],"study_design_scores_gemma":[0.000001932767,0.00002561275,0.003967345,0.000002640643,0.000007892899,0.0000160178,0.000009701092,0.9944205,0.0008781522,0.0005041835,0.0001595003,0.000006576262],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5502113,0.0003228618,0.4457495,0.000194352,0.00007499231,0.00004282842,0.0006196659,0.0006336066,0.002150977],"genre_scores_gemma":[0.9854048,0.00008839404,0.01360277,0.00002118812,0.00001652976,0.00001262536,0.0003330867,0.00001198989,0.0005085981],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008645384,"threshold_uncertainty_score":0.0171901,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02783166972572346,"score_gpt":0.2484949566241867,"score_spread":0.2206632868984633,"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."}}