{"id":"W2994880973","doi":"10.1109/iecon.2019.8926856","title":"Optimization-based Path Planning for an Autonomous Vehicle in a Racing Track","year":2019,"lang":"en","type":"article","venue":"","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Motion planning; Track (disk drive); Path (computing); Computer science; Point (geometry); Mathematical optimization; Trajectory; Time horizon; Nonlinear system; Optimization problem; Control theory (sociology); Optimal control; Control (management); Mathematics; Algorithm; Artificial intelligence; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002820612,0.0006332129,0.0006264562,0.0003596612,0.0005485111,0.0005142027,0.0005492606,0.000634744,0.002483384],"category_scores_gemma":[0.0006328766,0.0003821862,0.000422466,0.0004656592,0.0004474592,0.0005411124,0.0004640354,0.0007468897,0.0002356646],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006621457,"about_ca_system_score_gemma":0.001489259,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01899867,"about_ca_topic_score_gemma":0.01387813,"domain_scores_codex":[0.9998609,0.00003786566,0.000004970224,0.00004042134,0.000035298,0.00002050778],"domain_scores_gemma":[0.9998143,0.00009253668,0.00003260348,0.00001256027,0.00003787909,0.00001003832],"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":[0.00001641101,0.000008977727,0.0001227957,0.00002105413,0.000005822307,0.00002365354,0.00002043055,0.9829579,0.0008017871,0.002946278,0.0002303541,0.01284464],"study_design_scores_gemma":[0.000001807317,0.00001154879,0.00004715716,0.000001591399,0.00000204659,0.000005489968,0.000005405612,0.9983896,0.0002385984,0.0009055971,0.0003889525,0.000002202564],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01164764,0.000100296,0.9851739,0.00007449897,0.00001641161,0.00003682361,0.00003887513,0.000179943,0.002731639],"genre_scores_gemma":[0.6702663,0.0003782018,0.3206601,0.00005362216,0.00003803875,0.0001899016,0.0002178179,0.00009787777,0.008098061],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01899867,"threshold_uncertainty_score":0.03777611,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02599891958919706,"score_gpt":0.2772808388389634,"score_spread":0.2512819192497663,"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."}}