{"id":"W4390738661","doi":"10.1109/tetci.2023.3349183","title":"Robust Learning-Based Gain-Scheduled Path Following Controller Design for Autonomous Ground Vehicles","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Emerging Topics in Computational Intelligence","topic":"Vehicle Dynamics and Control Systems","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Control theory (sociology); Controller (irrigation); CarSim; Path (computing); Engineering; Support vector machine; Control engineering; Computer science; Artificial intelligence; Control (management)","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":[],"consensus_categories":[],"category_scores_codex":[0.0004031144,0.0002119422,0.0002374618,0.0002642472,0.0001557697,0.0001439763,0.0001538053,0.0001039313,0.00001575944],"category_scores_gemma":[0.00001054527,0.0002323657,0.0002101993,0.0002923589,0.00002462036,0.0001174261,8.429956e-7,0.0003724938,0.00001654076],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002300224,"about_ca_system_score_gemma":0.00007468445,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002771092,"about_ca_topic_score_gemma":0.00001902169,"domain_scores_codex":[0.9986946,0.00006323426,0.0004380231,0.000305526,0.0002157793,0.0002828333],"domain_scores_gemma":[0.9989456,0.0008138092,0.00002736298,0.00009604097,0.00006109141,0.00005607692],"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":[0.00002351057,0.00003788345,0.000007372277,0.00007913612,0.0000956761,0.00001220834,0.0002640886,0.9654682,0.0001045334,0.001644166,0.00001015898,0.03225306],"study_design_scores_gemma":[0.0002988777,0.00008185607,0.00002823983,0.0001776721,0.0000283297,0.000002491819,0.00007576109,0.9954644,0.0003639787,0.00296324,0.0002777173,0.0002374184],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01099499,0.0003654599,0.9859794,0.0002634388,0.001507277,0.0004399272,0.00001313713,0.0003826903,0.00005372813],"genre_scores_gemma":[0.9846691,0.00001557603,0.01472921,0.00005361086,0.00009800163,0.0002251376,0.00000782329,0.00005000907,0.000151452],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9736742,"threshold_uncertainty_score":0.9475597,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03297313238036767,"score_gpt":0.2642233404439404,"score_spread":0.2312502080635727,"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."}}