{"id":"W4411516718","doi":"10.1155/atr/1188373","title":"Vehicle Collision Warning Based on Combination of the YOLO Algorithm and the Kalman Filter in the Driving Assistance System","year":2025,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Collision; Warning system; Computer science; Process (computing); Kalman filter; Set (abstract data type); Filter (signal processing); Lane departure warning system; Monocular vision; Interval (graph theory); Focus (optics); Algorithm; Simulation; Real-time computing; Artificial intelligence; Computer vision; Computer security; 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":[0.0005287061,0.00006800752,0.0001437912,0.00007760765,0.00009154349,0.000006808819,0.0001457021,0.00005658737,5.440615e-7],"category_scores_gemma":[0.00002609957,0.00003697071,0.0000525385,0.0002564598,0.00006103269,0.0001079694,0.000001521312,0.0002948732,8.149592e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005614733,"about_ca_system_score_gemma":0.00001673654,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001991206,"about_ca_topic_score_gemma":0.00002517936,"domain_scores_codex":[0.9993047,0.00007180992,0.0003542096,0.00005482004,0.0001436646,0.00007086043],"domain_scores_gemma":[0.9993649,0.0002877114,0.0001763593,0.0001114671,0.00005290135,0.000006659501],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0001652677,0.00004124074,0.009491262,0.0001151307,0.00002731466,0.00000635559,0.001979812,0.9526978,0.001498218,0.00773941,0.00001302466,0.02622517],"study_design_scores_gemma":[0.00241975,0.0000551406,0.7861643,0.0006061569,0.00004238685,0.000002346255,0.001513349,0.2042731,0.003991395,0.0007353032,0.0001429037,0.00005388814],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9558517,0.0002174115,0.04247375,0.0009022792,0.000198378,0.0001890748,0.000002641946,0.00002012652,0.0001446705],"genre_scores_gemma":[0.9989602,0.00003772449,0.0009300336,0.00004573679,0.000007193549,0.00000669664,0.000001421261,0.000005083487,0.000005893819],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.776673,"threshold_uncertainty_score":0.1507622,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.002691690850678071,"score_gpt":0.1971770746764916,"score_spread":0.1944853838258135,"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."}}