{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002572165,0.0006181629,0.0005047866,0.0002675578,0.000382429,0.0005019711,0.001008975,0.0006309589,0.002238906],"category_scores_gemma":[0.0007925006,0.0002668417,0.0003090986,0.0002075235,0.0003805353,0.0006159786,0.0009401785,0.0007442839,0.0004151994],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004132809,"about_ca_system_score_gemma":0.0005428629,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004566298,"about_ca_topic_score_gemma":0.003950018,"domain_scores_codex":[0.9997239,0.00004583192,0.0000116126,0.00006307193,0.0001049229,0.00005081116],"domain_scores_gemma":[0.9997699,0.00009298682,0.00002807995,0.00002826754,0.00005148141,0.00002931118],"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":[0.0001692277,0.00009474714,0.0007772166,0.00009597743,0.0000389683,0.0001834743,0.0002146057,0.8148662,0.02431968,0.01571889,0.001772112,0.1417489],"study_design_scores_gemma":[0.000009143851,0.00005884224,0.0001132465,0.000003314846,0.000003380081,0.00003060128,0.00002289431,0.9937365,0.001857364,0.003117826,0.001042336,0.000004555281],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02634725,0.00005019476,0.9704478,0.00004792761,0.00001749714,0.00004337317,0.00002169367,0.0006319712,0.002392217],"genre_scores_gemma":[0.6765822,0.00008733523,0.3178295,0.00005806727,0.00001684146,0.00009269749,0.00009222318,0.0001316492,0.005109462],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004566298,"threshold_uncertainty_score":0.009079456,"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."}}