{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002586406,0.0004755195,0.0004896725,0.0004269742,0.000578715,0.0005884935,0.001104894,0.0008420576,0.002348527],"category_scores_gemma":[0.0005851859,0.0001791906,0.0003154083,0.0002424556,0.0002912967,0.0002915371,0.0003975328,0.0005717169,0.0006667551],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003191978,"about_ca_system_score_gemma":0.0004924956,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008775014,"about_ca_topic_score_gemma":0.006661348,"domain_scores_codex":[0.9996998,0.00002705466,0.00002183408,0.00007527426,0.0001360568,0.00003998106],"domain_scores_gemma":[0.9997074,0.00005370915,0.00003555704,0.00001792862,0.0001654797,0.00001984525],"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":[0.001413082,0.0004575217,0.002441767,0.0007724903,0.0001950572,0.001257079,0.0004778198,0.4376091,0.1200984,0.00691709,0.008834582,0.419526],"study_design_scores_gemma":[0.0001616707,0.0005489102,0.001959082,0.000036212,0.00006487554,0.0003294448,0.00003466899,0.9788418,0.01039578,0.0007541872,0.006824006,0.00004935208],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09246738,0.001349421,0.8761765,0.0003201724,0.0004349755,0.0003616664,0.0001656229,0.004259275,0.02446491],"genre_scores_gemma":[0.9615996,0.0002219758,0.03108856,0.0001454926,0.00007586418,0.0001833377,0.0001031941,0.00001917141,0.006562859],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008775014,"threshold_uncertainty_score":0.01744789,"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."}}