{"id":"W3092477836","doi":"10.1155/2020/8892693","title":"A Discretionary Lane-Changing Decision-Making Mechanism Incorporating Drivers’ Heterogeneity: A Signalling Game-Based Approach","year":2020,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Traffic control and management","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Higher Education Discipline Innovation Project; Fundamental Research Funds for the Central Universities; Chang'an University","keywords":"Computer science; Bayesian probability; Robustness (evolution); Reciprocal; Stochastic game; Context (archaeology); Sensitivity (control systems); Signalling; Endogeneity; Bayesian game; Simulation; Mathematical optimization; Game theory; Sequential game; Machine learning; Mathematics; Engineering; Artificial intelligence; Mathematical economics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002739407,0.001196757,0.001694393,0.001155549,0.000656052,0.002534985,0.003639811,0.002541658,0.005671895],"category_scores_gemma":[0.005951543,0.0007085091,0.001648431,0.001034042,0.001843763,0.003204329,0.001818443,0.002644438,0.0005766708],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00205408,"about_ca_system_score_gemma":0.001704672,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007463377,"about_ca_topic_score_gemma":0.004810266,"domain_scores_codex":[0.9978096,0.0008862509,0.00008659698,0.0005173846,0.0002492501,0.0004510406],"domain_scores_gemma":[0.9966844,0.00183935,0.0006368178,0.0002101955,0.0003187094,0.0003105398],"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":[0.0001107646,0.00008425026,0.00203496,0.00007066783,0.00008094206,0.0002347362,0.0001739466,0.9007534,0.001462228,0.08737103,0.0005862869,0.0070369],"study_design_scores_gemma":[0.00001741924,0.000052275,0.00037616,0.000009097063,0.00002870576,0.00004674037,0.00004124895,0.9732915,0.0001443882,0.0255001,0.0004664084,0.00002595348],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1660891,0.0003802881,0.8189629,0.001009273,0.0001183373,0.0002086474,0.000471132,0.0002255392,0.01253472],"genre_scores_gemma":[0.9733337,0.0002142736,0.02048477,0.0001016249,0.00003301538,0.0001253041,0.0001190792,0.0000229817,0.005565178],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007463377,"threshold_uncertainty_score":0.01897436,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009262906789195434,"score_gpt":0.2118541593889854,"score_spread":0.20259125259979,"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."}}