{"id":"W2551332360","doi":"10.1109/fuzz-ieee.2016.7737729","title":"Game theoretic Fuzzy Multi-Entity Bayesian Networks for collision avoidance in VANETs","year":2016,"lang":"en","type":"article","venue":"","topic":"Vehicular Ad Hoc Networks (VANETs)","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Collision avoidance; Computer science; Fuzzy logic; Bayesian game; Bayesian network; Game theory; Component (thermodynamics); Collision; Advanced driver assistance systems; Threat assessment; Bayesian probability; Active safety; Computer security; Artificial intelligence; Engineering; Sequential game; Automotive engineering; Mathematics","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":[0.002153618,0.00111741,0.001366386,0.001137576,0.0008000146,0.002001744,0.001950933,0.001633929,0.002448604],"category_scores_gemma":[0.006052092,0.0006707081,0.001000974,0.001468516,0.00132067,0.00272862,0.001480782,0.001766234,0.0003302012],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003270313,"about_ca_system_score_gemma":0.0017626,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0246533,"about_ca_topic_score_gemma":0.02102499,"domain_scores_codex":[0.9987311,0.0006620049,0.00004738421,0.0001980926,0.0002298925,0.000131541],"domain_scores_gemma":[0.9978865,0.001493613,0.0002026872,0.00005861819,0.0002457903,0.0001128144],"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":[0.00003908165,0.00002468693,0.0003997498,0.00003927987,0.00003377084,0.00008991501,0.00006654939,0.90857,0.0002261968,0.08300013,0.0006017827,0.006908925],"study_design_scores_gemma":[0.000005612665,0.0000106896,0.00007469171,0.000005458133,0.000008136263,0.0000128757,0.00001573759,0.9757826,0.00003075549,0.0236534,0.0003926568,0.000007287168],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02389078,0.0009807639,0.9674842,0.0007139037,0.0000799749,0.0001020369,0.0002575405,0.00009683119,0.006393883],"genre_scores_gemma":[0.8871163,0.001737575,0.1018771,0.0001840379,0.0001523911,0.0003597171,0.0003090267,0.00003509815,0.00822881],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0246533,"threshold_uncertainty_score":0.04901963,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007723329661434031,"score_gpt":0.216117657285377,"score_spread":0.208394327623943,"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."}}