{"id":"W4408384608","doi":"10.1049/itr2.70013","title":"SECRM‐2D: RL‐Based Efficient and Comfortable Route‐Following Autonomous Driving With Analytic Safety Guarantees","year":2025,"lang":"en","type":"article","venue":"IET Intelligent Transport Systems","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Vehicle safety; Automotive engineering; Embedded system; Real-time computing; Transport engineering; Engineering","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":[0.0005803602,0.0005262867,0.0004142123,0.0002303799,0.0002064809,0.0004469501,0.001033854,0.0005774469,0.0019117],"category_scores_gemma":[0.001280307,0.0002149125,0.0002935595,0.00007190585,0.0004371114,0.0003195282,0.0009882589,0.0007418689,0.0004044346],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003293903,"about_ca_system_score_gemma":0.0006679393,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003641531,"about_ca_topic_score_gemma":0.002864619,"domain_scores_codex":[0.9997297,0.00006389635,0.00001567102,0.0000576913,0.00008933631,0.0000437403],"domain_scores_gemma":[0.9991968,0.0002567027,0.0001007362,0.0001313219,0.0002311601,0.00008313942],"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":[0.0001391117,0.00009614765,0.001074748,0.00004040502,0.00001964584,0.00005430565,0.0000354955,0.9608423,0.008357309,0.001130753,0.000800738,0.02740899],"study_design_scores_gemma":[0.000009580975,0.00006953241,0.00009563572,0.000001475495,0.000001352829,0.000005517103,0.000002788454,0.998575,0.0008934608,0.0001393097,0.0002039017,0.000002411246],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.29963,0.0002721933,0.6839812,0.0003136296,0.0001252646,0.000142071,0.0001535843,0.004771366,0.01061059],"genre_scores_gemma":[0.969162,0.00002331852,0.02892083,0.0000600308,0.00001006894,0.0000597767,0.0001082697,0.00003733103,0.001618374],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003641531,"threshold_uncertainty_score":0.007240713,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005062171888390562,"score_gpt":0.199327879071374,"score_spread":0.1942657071829835,"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."}}