{"id":"W4205699800","doi":"10.1109/tiv.2022.3141881","title":"Tunable Trajectory Planner Using G<sup>3</sup> Curves","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Vehicles","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Discretization; Trajectory; Motion planning; Path (computing); Jerk; Curvature; Mathematical optimization; Computer science; Mathematics; Control theory (sociology); Set (abstract data type); Path length; Mathematical analysis; Robot; Geometry; Acceleration; Physics; Artificial intelligence; Classical mechanics; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000393443,0.0007652161,0.0005207321,0.000461137,0.0004694256,0.0006664456,0.0008738205,0.0009772927,0.003091254],"category_scores_gemma":[0.001073304,0.000358959,0.000588041,0.0005703432,0.0007640283,0.0006520141,0.001081489,0.0008033748,0.0005140425],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001116323,"about_ca_system_score_gemma":0.001076598,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008789795,"about_ca_topic_score_gemma":0.007487069,"domain_scores_codex":[0.9998233,0.00003991323,0.000007525144,0.00004172695,0.00005817019,0.00002942063],"domain_scores_gemma":[0.9997236,0.0001399646,0.00003616151,0.00003201414,0.00004171292,0.00002658545],"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":[0.00004639117,0.00001255663,0.0002384739,0.00002995353,0.000007779866,0.00006741576,0.00006379346,0.9631517,0.002127247,0.01032276,0.0008504769,0.02308151],"study_design_scores_gemma":[0.000009214158,0.0000195526,0.00005074708,0.000006236472,0.000002740493,0.00001947693,0.00001727791,0.9914955,0.0008836039,0.005827411,0.001662063,0.000006311027],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02644723,0.000170293,0.9644991,0.0001281893,0.00002422405,0.00007889581,0.0001226474,0.001108286,0.00742121],"genre_scores_gemma":[0.6215294,0.0001744932,0.37337,0.00005983279,0.000009986999,0.0001711372,0.000325283,0.0003266035,0.00403332],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008789795,"threshold_uncertainty_score":0.01747727,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.045028324561116,"score_gpt":0.2671886598073157,"score_spread":0.2221603352461997,"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."}}