{"id":"W2889470209","doi":"10.1016/j.ymssp.2018.08.028","title":"Model predictive path following control for autonomous cars considering a measurable disturbance: Implementation, testing, and verification","year":2018,"lang":"en","type":"article","venue":"Mechanical Systems and Signal Processing","topic":"Advanced Control Systems Optimization","field":"Engineering","cited_by":209,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"National Natural Science Foundation of China","keywords":"Model predictive control; Controller (irrigation); Scheme (mathematics); Path (computing); Control theory (sociology); Computer science; Vehicle dynamics; Control engineering; Differential (mechanical device); Control (management); Engineering; Automotive engineering; Artificial intelligence; Mathematics","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":[0.0008028873,0.0005276016,0.0005848227,0.0002297738,0.0004547652,0.0006959941,0.0007584532,0.0006549166,0.00116772],"category_scores_gemma":[0.00175182,0.0002844624,0.000293576,0.0002331999,0.0006564364,0.0005199579,0.0005122142,0.0007690801,0.0001431191],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004903259,"about_ca_system_score_gemma":0.00136486,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01436215,"about_ca_topic_score_gemma":0.007890062,"domain_scores_codex":[0.9996403,0.00006895113,0.0000125304,0.00005900611,0.0001648559,0.00005432866],"domain_scores_gemma":[0.9988573,0.0006321031,0.0001375986,0.0001021326,0.000239968,0.00003090687],"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":[0.0003177696,0.0001568772,0.0007621649,0.0001122441,0.0000299945,0.00007134296,0.0001436812,0.9478579,0.01042598,0.003401006,0.0003910185,0.03633],"study_design_scores_gemma":[0.00002061501,0.0001343032,0.0002000011,0.000001930345,0.000005424107,0.000005691548,0.000007407754,0.9976162,0.00161681,0.0002954652,0.00009314228,0.000003079882],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4373685,0.0002578422,0.5544725,0.0002844496,0.0000957057,0.000130388,0.00006013623,0.001368013,0.005962448],"genre_scores_gemma":[0.9947885,0.00002215485,0.004621682,0.000008041176,0.000003128123,0.00002277744,0.00001327385,0.000007639255,0.0005127167],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01436215,"threshold_uncertainty_score":0.02855712,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02147818205786141,"score_gpt":0.2484112907734069,"score_spread":0.2269331087155455,"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."}}