{"id":"W4410282424","doi":"10.1007/s11760-025-04210-8","title":"Real-time traffic signal adjustment using YOLOv8 for improved integration of emergency vehicles in smart traffic systems","year":2025,"lang":"en","type":"article","venue":"Signal Image and Video Processing","topic":"IoT and GPS-based Vehicle Safety Systems","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Emergency vehicle; Computer science; Real-time computing; Traffic signal; SIGNAL (programming language)","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005264625,0.0002584949,0.0004326058,0.0002889724,0.000113161,0.00006894134,0.0001080556,0.0001448402,0.000009959364],"category_scores_gemma":[0.00001751683,0.0002487707,0.00009193389,0.0004033072,0.00004176219,0.0003941717,0.00001612388,0.0001459724,0.000001207125],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001103388,"about_ca_system_score_gemma":0.0001048726,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009478353,"about_ca_topic_score_gemma":0.00003024145,"domain_scores_codex":[0.9984351,0.0000644686,0.0007487095,0.0002903591,0.000113788,0.0003475106],"domain_scores_gemma":[0.9994646,0.0000837697,0.0001290244,0.0001096796,0.0001555611,0.00005732189],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001078515,0.00005199478,0.00003793206,0.002014066,0.00003911208,0.000001710721,0.0006853449,0.0365298,0.8291436,0.00001043465,0.0001531751,0.1312249],"study_design_scores_gemma":[0.0007943747,0.00008788317,0.0002007441,0.001237768,0.00007116734,0.00000304235,0.0006248264,0.9661183,0.03052949,0.00003152877,0.00005835217,0.0002424865],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9394364,0.00548651,0.05331806,0.00003755273,0.0003851487,0.000903264,0.00002668827,0.0001766187,0.0002297959],"genre_scores_gemma":[0.998129,0.000122084,0.00133833,0.000006917744,0.0001465466,0.00006820621,0.00002733375,0.00003811004,0.0001234554],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9295886,"threshold_uncertainty_score":0.9999965,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01443001879649056,"score_gpt":0.2636362487546836,"score_spread":0.249206229958193,"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."}}