{"id":"W4401218773","doi":"10.1080/00423114.2024.2373140","title":"Gain-scheduled model predictive controller for vehicle-following trajectory generation for autonomous vehicles","year":2024,"lang":"en","type":"article","venue":"Vehicle System Dynamics","topic":"Vehicle Dynamics and Control Systems","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Headway; Controller (irrigation); Model predictive control; Acceleration; Range (aeronautics); Engineering; Trajectory; Brake; Vehicle dynamics; Control theory (sociology); Simulation; Automotive engineering; Computer science; Control engineering; Control (management); Artificial intelligence","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008861454,0.0005410982,0.0007916592,0.0002701416,0.0003129276,0.0003980261,0.0003281691,0.000422612,0.000001203863],"category_scores_gemma":[0.0000316171,0.0005520686,0.0007009415,0.0002650064,0.00003478125,0.0003742606,0.00003010841,0.0002709418,0.00001674169],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001371238,"about_ca_system_score_gemma":0.0001612403,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000266834,"about_ca_topic_score_gemma":0.0001112359,"domain_scores_codex":[0.9971026,0.00006938026,0.0009382468,0.0007306139,0.0003498755,0.0008092757],"domain_scores_gemma":[0.9987945,0.0003098558,0.00009860331,0.0004057818,0.0001859693,0.0002052723],"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":[0.0000992502,0.00003374776,0.00005556488,0.001433303,0.0007740704,0.00001022529,0.0004955026,0.9053988,0.0574221,0.02548983,0.0004296003,0.008357951],"study_design_scores_gemma":[0.002441065,0.0001597071,0.00002926322,0.0002894016,0.0002764737,0.00001029136,0.000419983,0.9950145,0.000269655,0.0002527656,0.0002457871,0.0005911423],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3021387,0.001788935,0.6879386,0.00009103488,0.002166093,0.002667907,0.0008832754,0.001749052,0.0005763905],"genre_scores_gemma":[0.9937541,0.00001433798,0.001757021,0.00003747546,0.0009588074,0.002327195,0.0002626508,0.0002961854,0.0005922194],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6916154,"threshold_uncertainty_score":0.9996931,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01099223956424431,"score_gpt":0.2150973288892608,"score_spread":0.2041050893250165,"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."}}