{"id":"W2008433546","doi":"10.5539/mer.v3n1p143","title":"Fuzzy-Based Adaptive Cruise Controller with Collision Avoidance and Warning System","year":2013,"lang":"en","type":"article","venue":"Mechanical Engineering Research","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Cruise control; Collision avoidance system; Collision avoidance; Collision; Crash; Computer science; Robustness (evolution); Lane departure warning system; Controller (irrigation); Automotive engineering; Fuzzy logic; Warning system; Control theory (sociology); Simulation; Real-time computing; Engineering; Control (management); Computer security; Artificial intelligence; Telecommunications","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000708592,0.0002013995,0.0003006416,0.00022139,0.0001558368,0.00005088493,0.0002167644,0.0002445724,0.00001462978],"category_scores_gemma":[0.0001063911,0.0001687049,0.00003280219,0.0003945155,0.00007194345,0.000122341,0.00007400463,0.0009760249,0.0001038031],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001965884,"about_ca_system_score_gemma":0.00002873092,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002240352,"about_ca_topic_score_gemma":0.000002918239,"domain_scores_codex":[0.998466,0.00005756213,0.0002199631,0.0003012449,0.0003426341,0.0006125522],"domain_scores_gemma":[0.9990579,0.0003412297,0.00001661702,0.0002566949,0.000145066,0.0001824228],"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":[0.0001996624,0.00006996863,0.0001329307,0.0007349493,0.0002023094,0.0001226901,0.0001688781,0.7848746,0.102087,0.09516823,0.0004161463,0.01582262],"study_design_scores_gemma":[0.000871025,0.0002220258,0.0007046297,0.0002212812,0.000006728608,0.00001383776,0.0001057496,0.9874285,0.009756133,0.0001634633,0.0002890094,0.0002176549],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7384161,0.001007062,0.25523,0.0002931043,0.0001288038,0.001286524,0.000007897701,0.002469867,0.001160611],"genre_scores_gemma":[0.9940566,0.00002054183,0.005455511,0.000008098968,0.0000401387,0.0003065102,0.000001567394,0.00005933164,0.00005168893],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2556405,"threshold_uncertainty_score":0.6879585,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01424474499339194,"score_gpt":0.2210459892904093,"score_spread":0.2068012442970174,"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."}}