{"id":"W6979356377","doi":"","title":"SECRM-2D: RL-Based Efficient and Comfortable Route-Following Autonomous Driving with Analytic Safety Guarantees","year":2024,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hudbay Minerals (Canada)","funders":"","keywords":"Controller (irrigation); Crash; Headway; Reinforcement learning; Training (meteorology); Control (management); Software deployment; Active safety; Vehicle safety; Vehicle dynamics","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":[0.0001739173,0.0002452594,0.0002707641,0.0002620826,0.000213153,0.00005016335,0.0002068987,0.0001585667,0.00004491443],"category_scores_gemma":[0.000005820805,0.00024649,0.0001029123,0.0006700362,0.0001233286,0.0001793034,0.00005812362,0.0003390705,0.00003019773],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002515225,"about_ca_system_score_gemma":0.00007617218,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003749277,"about_ca_topic_score_gemma":0.0001129803,"domain_scores_codex":[0.9989218,0.00002171695,0.0001620042,0.0004499799,0.00005428608,0.0003901751],"domain_scores_gemma":[0.9994446,0.00009911325,0.00002430797,0.0003233644,0.00001542175,0.00009316348],"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":[0.00002711291,0.00002331696,0.01470125,0.00008602867,0.0002193752,0.0007329884,0.0001326738,0.9669603,0.0002036087,0.01605166,0.0000287835,0.0008329581],"study_design_scores_gemma":[0.0005139573,0.0000551623,0.004074237,0.0001420901,0.0001687801,0.00001469518,0.0001777584,0.9926158,0.000450885,0.0002491085,0.001207176,0.0003304107],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8619248,0.0003174824,0.133054,0.00004099451,0.0001562286,0.0001442822,0.000008906005,0.00140837,0.002944902],"genre_scores_gemma":[0.9991237,0.00003379205,0.000224946,0.00002104739,0.00001543482,7.803965e-7,0.000008466403,0.00003965865,0.0005321637],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1371989,"threshold_uncertainty_score":0.9999987,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01181615104047887,"score_gpt":0.1478137211795839,"score_spread":0.135997570139105,"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."}}