{"id":"W4210687899","doi":"10.1109/cdc45484.2021.9683031","title":"PA-FaSTrack: Planner-Aware Real-Time Guaranteed Safe Planning","year":2021,"lang":"en","type":"article","venue":"2021 60th IEEE Conference on Decision and Control (CDC)","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Mitacs","keywords":"Planner; Trajectory; Computer science; Motion planning; Sequence (biology); Tracking (education); Tree (set theory); Path (computing); State space; Mathematical optimization; Robot; Real-time computing; Artificial intelligence; Mathematics","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":[0.0006418462,0.0004862591,0.0007728906,0.000232788,0.0003528155,0.0007696641,0.001005594,0.0002661357,0.0003032459],"category_scores_gemma":[0.0003062104,0.0004387535,0.0001559237,0.00047616,0.00009777045,0.0003888148,0.0001970371,0.0005043608,0.0007081068],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005691463,"about_ca_system_score_gemma":0.0004729001,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002760523,"about_ca_topic_score_gemma":0.000004095687,"domain_scores_codex":[0.9960959,0.0002937573,0.0006942318,0.001274798,0.0008908446,0.0007504502],"domain_scores_gemma":[0.9966549,0.001085932,0.0002572261,0.001170083,0.0004146617,0.0004172009],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007987786,0.0006541174,0.002660952,0.0000566817,0.0003471439,0.009149619,0.002195786,0.01240252,0.03521145,0.01733389,0.02244914,0.8967399],"study_design_scores_gemma":[0.005839679,0.0005597522,0.01263693,0.001085325,0.00005511269,0.0004240476,0.000352832,0.9691675,0.00139637,0.003699246,0.003715532,0.001067719],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04028703,0.0004840501,0.939383,0.002459925,0.001886305,0.0004205599,0.0001055774,0.0003238085,0.01464975],"genre_scores_gemma":[0.9551929,0.0002179927,0.03896728,0.001974463,0.000310047,0.00004005173,0.00003392219,0.00004525735,0.003218058],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9567649,"threshold_uncertainty_score":0.9998064,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02704713068040716,"score_gpt":0.2779675423181514,"score_spread":0.2509204116377442,"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."}}