{"id":"W4384787430","doi":"10.1109/tiv.2023.3296435","title":"Adaptive Pure Pursuit: A Real-Time Path Planner Using Tracking Controllers to Plan Safe and Kinematically Feasible Paths","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Vehicles","topic":"Robotic Path Planning Algorithms","field":"Computer Science","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Natural Science Foundation of Hunan Province; National Natural Science Foundation of China","keywords":"Planner; Path (computing); Tracking (education); Plan (archaeology); Computer science; Motion planning; Control theory (sociology); Artificial intelligence; Computer vision; Real-time computing; Mathematical optimization; Mathematics; Robot; Control (management); Geography; Psychology","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":[0.0005718278,0.0003483278,0.0004386097,0.0005342335,0.0004046093,0.0003064307,0.0006196903,0.0001549832,0.00003031641],"category_scores_gemma":[0.00002905217,0.0003254333,0.0001316156,0.0008853463,0.00007737792,0.0003370719,0.00001461627,0.0003345211,0.0003967166],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000125861,"about_ca_system_score_gemma":0.00009951663,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008335304,"about_ca_topic_score_gemma":0.000008500901,"domain_scores_codex":[0.9973948,0.000162166,0.0004954648,0.0007012726,0.0006119522,0.0006343671],"domain_scores_gemma":[0.9983139,0.0006050813,0.0001097776,0.0004980433,0.0001172506,0.0003559393],"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":[0.0002942944,0.000284836,0.00004966094,0.00006224803,0.0002489118,0.0003750587,0.00645527,0.8361222,0.02662705,0.0007548444,0.0007529395,0.1279727],"study_design_scores_gemma":[0.0005059955,0.0005070486,0.000528064,0.0003841454,0.00005235466,0.00008144943,0.0005190821,0.9846349,0.01162373,0.0006345079,0.00005715725,0.0004715316],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06122505,0.00002195805,0.9360395,0.0007055206,0.0005148536,0.0005974346,0.00008430177,0.0005605718,0.0002507713],"genre_scores_gemma":[0.870855,0.00008120408,0.1278774,0.0003360858,0.00009386463,0.00008995295,0.000004552444,0.0000608371,0.0006010893],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.80963,"threshold_uncertainty_score":0.9999198,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05724224967894712,"score_gpt":0.2861207910976258,"score_spread":0.2288785414186787,"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."}}