{"id":"W4400027651","doi":"10.1080/15472450.2024.2370010","title":"A reinforcement learning based autonomous vehicle control in diverse daytime and weather scenarios","year":2024,"lang":"en","type":"article","venue":"Journal of Intelligent Transportation Systems","topic":"Traffic control and management","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"","keywords":"Daytime; Reinforcement learning; Computer science; Reinforcement; Control (management); Aeronautics; Engineering; Simulation; Artificial intelligence; Environmental science; Atmospheric sciences; Structural engineering; Geology","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":[0.0003618402,0.0004211521,0.0003954423,0.0001573501,0.0002402458,0.0003957749,0.0007298882,0.0005635335,0.001059591],"category_scores_gemma":[0.0007755484,0.0002114474,0.0002803144,0.0001124169,0.0004469655,0.0004175452,0.000615755,0.0006929068,0.0001321866],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004988848,"about_ca_system_score_gemma":0.0008022223,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01141513,"about_ca_topic_score_gemma":0.007236226,"domain_scores_codex":[0.9998612,0.00002670605,0.000005249905,0.00004802494,0.00002229702,0.00003646552],"domain_scores_gemma":[0.9997553,0.00009657587,0.00003823715,0.00001416673,0.00006560337,0.00003008665],"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":[0.00004332771,0.00003351038,0.0007840669,0.0000169098,0.000009821546,0.00004451139,0.00002421358,0.9873798,0.001397315,0.001171129,0.0002756272,0.008819642],"study_design_scores_gemma":[0.000004046376,0.00002084103,0.00008682951,0.000001083257,0.000001932652,0.000003250338,0.000002469392,0.9994113,0.0001141499,0.0002750498,0.00007754667,0.000001370839],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2239026,0.0003317745,0.765895,0.0006079219,0.0001119255,0.00008098022,0.0001386679,0.0005133892,0.008417801],"genre_scores_gemma":[0.9916145,0.00003953596,0.006783608,0.00004057489,0.00000736833,0.00003245837,0.00003448789,0.000005920755,0.001441676],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01141513,"threshold_uncertainty_score":0.02269739,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008562479796449203,"score_gpt":0.204747670604887,"score_spread":0.1961851908084378,"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."}}