{"id":"W4388499209","doi":"10.1145/3631613","title":"A Deep Time Delay Filter for Cooperative Adaptive Cruise Control","year":2023,"lang":"en","type":"article","venue":"ACM Transactions on Cyber-Physical Systems","topic":"Traffic control and management","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph; Memorial University of Newfoundland; University of Toronto","funders":"","keywords":"Cruise control; Cruise; Computer science; Filter (signal processing); Control (management); Control theory (sociology); Geology; Artificial intelligence; Oceanography; Computer vision","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":[0.0003403864,0.0005351913,0.0003298039,0.0001921176,0.0002664197,0.0003520902,0.0007787725,0.0006976065,0.00163726],"category_scores_gemma":[0.0008624187,0.0001807087,0.0002750435,0.0001690467,0.00034189,0.0005362183,0.0005130148,0.0009620878,0.0002869504],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007329499,"about_ca_system_score_gemma":0.0009057511,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009014289,"about_ca_topic_score_gemma":0.00963402,"domain_scores_codex":[0.9998515,0.00001343948,0.000007210857,0.00003970441,0.00006030441,0.00002791442],"domain_scores_gemma":[0.999774,0.00006723528,0.00003037908,0.00002069049,0.00008893503,0.00001873349],"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":[0.0001702915,0.0000911071,0.001045917,0.00008604854,0.00004499881,0.00008359525,0.00007289846,0.7707931,0.02634929,0.007151856,0.002082929,0.1920281],"study_design_scores_gemma":[0.00000414099,0.00003640607,0.00009322717,0.000003415126,0.000004697259,0.000008418706,0.000002034374,0.9963413,0.002090377,0.0006025315,0.000809825,0.00000365765],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02063713,0.0002422258,0.9758661,0.0001661195,0.00009126709,0.00002454236,0.00004606728,0.0006340728,0.002292474],"genre_scores_gemma":[0.9040077,0.0001980418,0.09028492,0.0001833859,0.00004356957,0.00006356097,0.0001238435,0.0000373049,0.005057714],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009014289,"threshold_uncertainty_score":0.01792359,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01323526936434742,"score_gpt":0.2204389158134715,"score_spread":0.207203646449124,"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."}}