{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005008726,0.0002762841,0.0002703435,0.0003659737,0.000885925,0.0001011432,0.00107692,0.00006382622,0.0003022483],"category_scores_gemma":[0.000006574969,0.0002941028,0.0001918168,0.0007060309,0.00007207501,0.0003655448,0.00001569879,0.0006638354,0.0001195305],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002848909,"about_ca_system_score_gemma":0.0001611505,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001620906,"about_ca_topic_score_gemma":0.000001756183,"domain_scores_codex":[0.9974211,0.0002813273,0.0004139704,0.000620649,0.0007245615,0.0005384175],"domain_scores_gemma":[0.9987345,0.0002424704,0.0001008301,0.0006911112,0.00006245772,0.0001686822],"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":[0.00002065267,0.0002895626,0.0000252787,0.00003238746,0.00005781727,0.00005544307,0.00118915,0.9834792,0.0005324357,0.00009000851,0.001176241,0.0130518],"study_design_scores_gemma":[0.0002143713,0.0002813949,0.00002875618,0.0001138724,0.00003645122,0.0001791376,0.0004647919,0.9744045,0.02065701,0.0001992247,0.003000077,0.0004204728],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02324913,0.0005253789,0.9737567,0.0003699483,0.001089346,0.0002849143,0.00005322502,0.0004348239,0.0002365329],"genre_scores_gemma":[0.9348466,0.0001729048,0.06103161,0.001598492,0.0001087589,0.0001876388,0.000007885222,0.00006560933,0.001980515],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9127251,"threshold_uncertainty_score":0.9999511,"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."}}