{"id":"W2747722670","doi":"10.1109/tits.2017.2735380","title":"A Distributed Reference Governor Approach to Ecological Cooperative Adaptive Cruise Control","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Transportation Systems","topic":"Traffic control and management","field":"Engineering","cited_by":75,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Ontario Centres of Excellence","keywords":"Platoon; Cooperative Adaptive Cruise Control; Control theory (sociology); String (physics); Controller (irrigation); Control engineering; Constraint (computer-aided design); Cruise control; Engineering; Model predictive control; Stability (learning theory); Computer science; Control (management); Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001481558,0.000301774,0.0003895397,0.0001014784,0.0003636765,0.0001715743,0.000331757,0.0001345176,0.00004086706],"category_scores_gemma":[0.000004589022,0.0002722942,0.0001326106,0.0001104951,0.00005335327,0.0001602907,3.399825e-7,0.0002616179,0.0001594541],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001834709,"about_ca_system_score_gemma":0.00002120049,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001961916,"about_ca_topic_score_gemma":0.0005827912,"domain_scores_codex":[0.9984274,0.00004632454,0.000507126,0.000385966,0.0003106387,0.0003225366],"domain_scores_gemma":[0.9990483,0.00007129565,0.00008648114,0.0004412843,0.0001453026,0.0002073361],"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":[0.0001967266,0.0002514978,0.00001295356,0.00005160672,0.0002446701,0.000009060982,0.0004600015,0.9916909,0.000237556,0.002080548,0.0003086395,0.004455837],"study_design_scores_gemma":[0.007053343,0.001755971,0.04429941,0.0005021584,0.0009163685,0.00001302492,0.005419254,0.862153,0.004908386,0.0000424908,0.07031839,0.002618219],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01231849,0.0000428477,0.9800087,0.00007806802,0.001137866,0.001469675,0.001353319,0.0004048016,0.003186257],"genre_scores_gemma":[0.9980384,0.00004943807,0.0001784476,0.00004586031,0.00004897236,0.0010213,0.00004733148,0.00003126738,0.0005390085],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9857199,"threshold_uncertainty_score":0.9999729,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03547517089211464,"score_gpt":0.2411622473417268,"score_spread":0.2056870764496122,"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."}}