{"id":"W4389302505","doi":"10.1109/cog57401.2023.10333163","title":"Efficient Ground Vehicle Path Following in Game AI","year":2023,"lang":"en","type":"article","venue":"","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Robustness (evolution); Computer science; Path (computing); Unmanned ground vehicle; Focus (optics); Mathematical optimization; Benchmark (surveying); Quadratic equation; Curvature; Motion planning; Simulation; Robot; Algorithm; Artificial intelligence; Mathematics; Computer network","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":[],"consensus_categories":[],"category_scores_codex":[0.0001628258,0.0000871615,0.0001155349,0.0001358402,0.00002953664,0.000008731651,0.0001073355,0.0001133582,0.00003826039],"category_scores_gemma":[0.000009896463,0.00008659482,0.00004798318,0.0004780467,0.00001635949,0.00003032115,0.00004064824,0.0001992179,0.0005908138],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006158112,"about_ca_system_score_gemma":0.000007688815,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002073077,"about_ca_topic_score_gemma":0.00002270814,"domain_scores_codex":[0.9993612,0.000007752484,0.0001463438,0.0001259119,0.00007007577,0.0002886875],"domain_scores_gemma":[0.9997702,0.00003190448,0.000005583347,0.000161112,0.000003701434,0.00002748941],"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":[0.000009220446,0.00006958603,0.03750126,0.00005296996,0.00006856127,0.0004302138,0.00127224,0.9056926,0.01251158,0.01589153,0.001226217,0.02527399],"study_design_scores_gemma":[0.0003356929,0.00001237219,0.08230117,0.00001328623,0.000003273632,0.00000180795,0.0001571936,0.9140891,0.001147696,0.0007828387,0.001003684,0.0001518984],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9906849,0.00006373123,0.001689502,0.0002467551,0.0001989361,0.00007973241,9.363355e-7,0.002216839,0.004818695],"genre_scores_gemma":[0.9995124,0.000007616516,0.00006781443,0.00007514932,0.00001271785,0.00001467501,0.00000264778,0.00001940891,0.0002875266],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04479991,"threshold_uncertainty_score":0.7593913,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006815858273737283,"score_gpt":0.2148709598207476,"score_spread":0.2080551015470103,"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."}}