{"id":"W2901959574","doi":"10.1049/iet-its.2018.5387","title":"Mixed local motion planning and tracking control framework for autonomous vehicles based on model predictive control","year":2018,"lang":"en","type":"article","venue":"IET Intelligent Transport Systems","topic":"Vehicle Dynamics and Control Systems","field":"Engineering","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"CarSim; Motion planning; Kinematics; Model predictive control; Acceleration; Control theory (sociology); Tracking (education); Motion control; Computer science; Trajectory; MATLAB; Vehicle dynamics; Motion (physics); Engineering; Control engineering; Simulation; Control (management); Artificial intelligence; Automotive engineering; Robot","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":[0.0005532019,0.000400245,0.000641969,0.0001560618,0.0001799285,0.00008590064,0.0001871318,0.0003572686,0.000003911393],"category_scores_gemma":[0.00001666273,0.000389334,0.0001861372,0.0001032523,0.0001034504,0.000123897,0.000002444984,0.0002830182,0.000007947325],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001769979,"about_ca_system_score_gemma":0.00003692816,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003768521,"about_ca_topic_score_gemma":0.00001968826,"domain_scores_codex":[0.9978911,0.00005403759,0.0007473318,0.0004572332,0.0003146486,0.0005356874],"domain_scores_gemma":[0.9989038,0.0002957656,0.0001223554,0.0003049089,0.0001781568,0.0001950247],"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":[0.0004407547,0.00005128189,0.00256039,0.0002764669,0.0002045553,0.00000824756,0.0006114031,0.9876944,0.0004957687,0.004234762,0.00002786646,0.003394086],"study_design_scores_gemma":[0.00172058,0.0004254239,0.001046219,0.0006199108,0.0001346039,0.000006504667,0.0003765585,0.9943631,0.000352624,0.0003225079,0.0002552449,0.0003767293],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0550549,0.0005752617,0.9402928,0.00004419567,0.001247079,0.001630189,0.0004488146,0.0004505873,0.0002562291],"genre_scores_gemma":[0.9983668,0.000006376028,0.0003969199,0.00009307179,0.0005126699,0.0004495769,0.00004516881,0.0001071315,0.00002225457],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9433119,"threshold_uncertainty_score":0.9998559,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01922817758579747,"score_gpt":0.2280101268565726,"score_spread":0.2087819492707751,"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."}}