{"id":"W4414538258","doi":"10.1109/tits.2025.3612984","title":"Multi-Objective Agent-Based Model Predictive Controller for Plug-and-Play Vehicle Control","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Transportation Systems","topic":"Advanced Control Systems Optimization","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Model predictive control; Flexibility (engineering); Decoupling (probability); Vehicle dynamics; Control theory (sociology); Convergence (economics); Controller (irrigation); Control (management)","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":[0.0002029746,0.0003724465,0.0005357283,0.0003836591,0.0002191931,0.00006093844,0.0001220002,0.000229997,0.000004792904],"category_scores_gemma":[0.000007351971,0.0003898719,0.000217016,0.0002809114,0.00005406742,0.0002386773,1.118078e-7,0.0002234812,0.00000859297],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002875603,"about_ca_system_score_gemma":0.0000729044,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000483107,"about_ca_topic_score_gemma":0.0001368374,"domain_scores_codex":[0.9980987,0.00006398746,0.0008235234,0.0004437527,0.0002305365,0.0003395294],"domain_scores_gemma":[0.9988706,0.000358315,0.0001136023,0.0002210778,0.0003203344,0.0001160315],"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":[0.0005391977,0.00009910481,0.00003644967,0.0002275621,0.000415939,8.797167e-7,0.0005209701,0.9926893,0.003872076,0.0002937665,0.00004036195,0.001264359],"study_design_scores_gemma":[0.005153046,0.0001105769,0.0001074835,0.0002121961,0.0002759982,4.848666e-7,0.0004449588,0.9848267,0.008347268,0.00003662919,0.0001895223,0.0002951039],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001904444,0.0003854567,0.9913374,0.00003865551,0.00132354,0.003349546,0.0009971667,0.0005779328,0.00008588269],"genre_scores_gemma":[0.9940842,0.00005063206,0.002033436,0.00008922863,0.00003337149,0.003041209,0.0000407297,0.00007286846,0.0005543376],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9921798,"threshold_uncertainty_score":0.9998553,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01359584014872537,"score_gpt":0.2453228007668294,"score_spread":0.231726960618104,"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."}}