{"id":"W4381730776","doi":"10.3390/s23104659","title":"Multi-Lane Differential Variable Speed Limit Control via Deep Neural Networks Optimized by an Adaptive Evolutionary Strategy","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Traffic control and management","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"National Key Research and Development Program of China","keywords":"CMA-ES; Reinforcement learning; Computer science; Evolution strategy; Speed limit; Artificial neural network; Differential evolution; Artificial intelligence; Limit (mathematics); Controller (irrigation); Mathematical optimization; Deep learning; Evolutionary algorithm; Mathematics; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00007885326,0.0002561271,0.0003036463,0.00008972195,0.00008844688,0.00004605719,0.0001553024,0.000115482,0.0001587507],"category_scores_gemma":[0.000007544982,0.0002534374,0.00008229363,0.0002167362,0.00003205077,0.0001047106,0.00003076477,0.0002183879,0.00006172754],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005403136,"about_ca_system_score_gemma":0.000004877666,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008593471,"about_ca_topic_score_gemma":0.00003600352,"domain_scores_codex":[0.9986818,0.00007402546,0.0002504691,0.000287538,0.000168837,0.0005372633],"domain_scores_gemma":[0.999468,0.00007815652,0.00003399414,0.0002297946,0.00003393462,0.0001561519],"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":[0.0001823927,0.00005634047,0.000009413716,0.000009670186,0.0001841389,0.00003177167,0.00004169949,0.9900112,0.001574792,0.00006454762,0.001636499,0.006197562],"study_design_scores_gemma":[0.003396568,0.0001118403,0.003225856,0.000004855341,0.00008326018,0.000002770004,0.0001161762,0.9924874,0.000006073521,0.00001639866,0.0002720978,0.0002767451],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3501322,0.0006608349,0.6398573,0.0001619598,0.002367066,0.001372178,0.0001518032,0.004033536,0.001263144],"genre_scores_gemma":[0.997567,0.00003663623,0.001197315,0.00003477576,0.0002457678,0.00002269248,0.0001962676,0.00005467075,0.000644905],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6474348,"threshold_uncertainty_score":0.9999918,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01127671746788732,"score_gpt":0.1991492124512754,"score_spread":0.1878724949833881,"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."}}